system

Abstract

Provide a system. A user authentication means; Market research data collection means; market research data filtering means; an analytical means using a generative artificial intelligence model; a means of synthesizing the results of the analysis; a means for generating a marketing portfolio; a means for displaying the generated marketing portfolio; A means for proposing engagement measures based on user profiles; A system including:

Description

[Technical Field]

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

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

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

[0004] A problem with traditional marketing portfolio work is that it takes a lot of time and effort, from market research, PEST analysis, and 3C analysis to creating a Blueprint. Specifically, it takes about 50 hours per report, which places a heavy burden on marketing portfolio staff. There is a need for a way to improve this situation and streamline work. [Means for solving the problem]

[0005] The present invention provides a system including a user authentication means, a market research data collection means, a market research data filtering means, an analysis means using a generative AI model, a means for integrating analysis results, a means for generating a marketing portfolio, a means for displaying the generated marketing portfolio, and a means for proposing engagement measures based on a user profile. This system automates each phase of market research and analysis, significantly reducing the time required to create a marketing portfolio. Specifically, the market research data filtering means extracts reliable data based on specific criteria, and the analysis means using a generative AI model analyzes political, economic, social, technological, company, competitor, and customer factors, enabling the creation of a marketing portfolio more quickly and efficiently than ever before.

[0006] The "user authentication means" is a means for verifying the login information entered by the user and confirming the legitimacy of the user.

[0007] "Market research data collection means" refers to means of obtaining market information from external data sources.

[0008] A "market research data filtering means" is a means for filtering collected market data based on specific criteria and extracting highly relevant information.

[0009] A "generative AI model" is a model for performing data analysis using artificial intelligence technology.

[0010] An "analytical tool" is a tool that uses a generative AI model to analyze market data and derive results from a specific perspective.

[0011] "Means for integrating analysis results" refers to a means for compiling multiple analysis results into a single report.

[0012] The "means for generating a marketing portfolio" is a means for creating a report including a marketing strategy and an action plan based on the integrated analysis results.

[0013] The "means for displaying the generated marketing portfolio" is a means for presenting the generated marketing portfolio to the user.

[0014] "Means for proposing engagement measures based on user profiles" refers to means for proposing appropriate marketing measures based on user attribute information and past data. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] The present invention is a system for streamlining marketing operations, and includes a user authentication means, a market research data collection means, a market research data filtering means, an analysis means using a generative AI model, a means for integrating analysis results, a means for generating a marketing portfolio, a means for displaying the generated marketing portfolio, and a means for proposing engagement measures based on a user profile.

[0037] When a user enters their login information, the server checks the information against a database to verify the user's authenticity. Specifically, the server queries the database for the username and password, and if successful, loads the user profile.

[0038] Next, the user enters keywords related to market research, and the device collects market data from external data sources (such as APIs) based on the keywords. The server retrieves the collected data and filters it based on specific criteria to extract the most relevant information.

[0039] The server then uses the generative AI model to perform PEST and 3C analyses on the filtered market data. PEST analysis analyzes political, economic, social, and technological factors, while 3C analysis analyzes company, competitor, and customer factors. This provides a deep understanding of a wide range of factors.

[0040] The server integrates the analysis results and automatically creates a marketing portfolio, which details marketing strategies and action plans. The server then displays the created marketing portfolio on the user's dashboard, where the user can view it.

[0041] Additionally, the server will suggest engagement strategies based on the user profile, including targeting and messaging strategies using user demographics and historical data, resulting in optimal marketing strategies for the user.

[0042] For example, when a user enters keywords such as "AI" and "marketing," data on the AI ​​market and marketing trends is collected from external market data sources. The server filters this data and performs PEST and 3C analyses to obtain insights into the current and forecast state of the AI ​​market, competitor trends, and target customer demand. Finally, a marketing portfolio is automatically generated based on this information, and the user is presented with the next step in marketing strategies.

[0043] This invention significantly reduces the work that previously took about 50 hours per report, thereby reducing the burden on marketing portfolio staff.

[0044] The processing flow will be explained below.

[0045] Step 1:

[0046] The user enters login information. The user enters their username and password on the system login screen.

[0047] Step 2:

[0048] The server authenticates the login information. It checks the entered username and password against a database to verify the user's identity. If the match is successful, the user profile is loaded.

[0049] Step 3:

[0050] The user inputs market research keywords. The user inputs search keywords for creating a marketing portfolio into the terminal.

[0051] Step 4:

[0052] The terminal collects market research data. The terminal sends keywords to an external data source (e.g., API) to obtain relevant market data.

[0053] Step 5:

[0054] The server filters the collected market data. The server filters the acquired market data based on specific criteria (e.g., the latest, most reliable information) to extract the most relevant information.

[0055] Step 6:

[0056] The server performs the PEST analysis, using a generative AI model to analyze political, economic, social, and technological factors against the filtered market data.

[0057] Step 7:

[0058] The server then performs the 3C analysis, also using a generative AI model to analyze the filtered market data for the company, competitors, and customers.

[0059] Step 8:

[0060] The server integrates the results of the analysis. The server integrates the results of the PEST analysis and the 3C analysis and automatically generates a marketing portfolio that includes strategic items and action plans.

[0061] Step 9:

[0062] The server displays the marketing portfolio. The server displays the generated marketing portfolio on the user's dashboard, where the user can view it.

[0063] Step 10:

[0064] The server proposes engagement strategies: The server proposes appropriate engagement strategies (targeting, messaging, etc.) based on the user profile.

[0065] By going through each step in this way, the user can efficiently create a marketing portfolio.

[0066] Example 1

[0067] 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."

[0068] Traditional marketing operations require a significant amount of time and effort to conduct market research, filter and analyze the data, and then integrate the results to create a marketing strategy. Furthermore, because much of this work is done manually, there is a high risk of human error and efficiency is low. Another issue is that it is difficult to appropriately propose engagement measures based on user profiles.

[0069] 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.

[0070] In this invention, the server includes a user authentication means, a market research data acquisition means, a market research data selection means, an analysis means using a generative artificial intelligence model, a means for integrating the analysis results, a means for generating a marketing strategy document, a means for displaying the generated marketing strategy document, and a means for proposing an engagement strategy based on a user profile. This makes marketing operations more efficient, significantly reduces time and labor, reduces human error, and enables the proposal of appropriate engagement measures.

[0071] "User authentication means" is a means for verifying the validity of a user by querying a database using authentication information (user name, password, etc.) entered by the user.

[0072] The "market research data acquisition means" is a means for collecting market research data from an external data source (such as an API) based on keywords specified by the user.

[0073] The "market research data selection means" is a means for filtering collected market research data based on specific criteria and extracting only highly relevant information.

[0074] An "analysis means using a generative artificial intelligence model" is a means for analyzing market research data that has been filtered using generative artificial intelligence (e.g., GPT-3 (registered trademark)).

[0075] The "means for integrating the analysis results" refers to a means for compiling the analysis results obtained by the generative artificial intelligence model into a single integrated data set.

[0076] The "means for generating a marketing strategy document" is a means for automatically creating a document detailing a marketing strategy and an action plan based on the integrated analysis results.

[0077] The "means for displaying the generated marketing strategy document" refers to a means for displaying the generated marketing strategy document on the dashboard of the user's operating terminal.

[0078] "Means for proposing engagement strategies based on user profiles" refers to means for proposing optimal targeting and messaging strategies based on user attribute information and past data.

[0079] The present invention is a system for improving the efficiency of marketing operations, and includes a user authentication means, a market research data acquisition means, a market research data selection means, an analysis means using a generative artificial intelligence model, a means for integrating the analysis results, a means for generating a marketing strategy document, a means for displaying the generated marketing strategy document, and a means for proposing an engagement strategy based on a user profile.

[0080] First, the user enters login information (username and password) using a terminal. The terminal sends this information to the server, which checks the MySQL database to verify the user's authenticity. If the verification is successful, the server loads the user profile.

[0081] Next, the user enters keywords related to market research (e.g., "AI" or "marketing"), and the device sends the keywords to the server, which then uses Python's requests library to retrieve market research data from external data sources (e.g., the Google® Trends API).

[0082] The acquired data is processed by the server using the Pandas library and filtered based on specific criteria (e.g., time period or relevance).The server then inputs the filtered data into a generative artificial intelligence model (e.g., GPT-3) to perform PEST (political, economic, social, technological) and 3C (company, competitor, customer) analysis.

[0083] Example prompt sentence:

[0084] "Conduct a PEST analysis of the AI ​​and Marketing industry based on the following data: [filtered data]"

[0085] The analysis results obtained from the generative AI model are integrated by the server to automatically create a marketing strategy document, which is generated using the Jinja2 template engine and displayed on the dashboard of the user's device.

[0086] Additionally, the server suggests optimal engagement strategies (e.g., targeting and messaging strategies) based on the user profile, using machine learning models such as Scikit-learn to make predictions based on past user activity and demographic information.

[0087] As a specific example, if a user enters the keywords "AI" and "marketing," the server uses the Google Trends API to collect data on the AI ​​market and marketing trends. The collected data is filtered, and PEST and 3C analyses are performed using GPT-3. The results reveal the current state and forecast of the AI ​​market, the trends of competitors, and the needs of target customers. Based on this information, a marketing strategy document is generated and presented to the user. Optimal engagement measures are also proposed based on the user profile.

[0088] This streamlines marketing operations, significantly reducing the time and errors involved compared to traditional manual analysis.

[0089] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0090] Step 1:

[0091] The user enters a username and password on the login page. The device sends the information to the server, which then checks the received login information against a MySQL database to verify the user's authenticity. If the verification is successful, the server loads the user profile information and starts the session.

[0092] Input: Username, Password

[0093] Output: Authentication successful (session started) or authentication failed (error message)

[0094] Step 2:

[0095] The user enters keywords related to market research (e.g., "AI" or "marketing"). The device sends the keywords to the server. The server receives the keywords and proceeds to the next processing step.

[0096] Input: keyword

[0097] Output: The keyword is sent to the server

[0098] Step 3:

[0099] The server uses the provided keywords to collect market research data from external data sources (e.g., Google Trends API), using the Python requests library.

[0100] Input: keyword

[0101] Output: Market research data (obtained from external data sources)

[0102] Specific operation: The server sends a request to the Google Trends API for the keywords "AI" and "marketing" to obtain related market data.

[0103] Step 4:

[0104] The server processes the acquired market data using the Pandas library and filters it based on specified criteria (e.g., time range or correlation), eliminating unnecessary data and extracting only the most relevant information.

[0105] Input: Market research data

[0106] Output: Filtered market research data

[0107] Specific operation: The server keeps AI marketing-related data from the past year and removes old or irrelevant data from before that.

[0108] Step 5:

[0109] The server inputs the filtered data into a generative artificial intelligence model (e.g., GPT-3) to perform PEST analysis (political, economic, social, technological) and 3C analysis (company, competitors, customers). The server generates prompt statements, passes them to the AI ​​model, and receives responses from the AI.

[0110] Input: Filtered market research data

[0111] Output: PEST and 3C analysis results

[0112] Specific behavior: The server generates the following prompt:

[0113] "Conduct a PEST analysis of the AI ​​and Marketing industry based on the following data: [filtered data]"

[0114] Receive responses from the generative AI model and obtain analytical results.

[0115] Step 6:

[0116] The server uses Pandas to integrate the results of the PEST and 3C analyses obtained from the generative artificial intelligence model and compile them into a single dataset.

[0117] Input: PEST and 3C analysis results

[0118] Output: Marketing insights to be integrated

[0119] Specific operation: The server compiles data on "Politics," "Economy," "Society," "Technology," as well as "Company," "Competitor," and "Customer" into a single data frame.

[0120] Step 7:

[0121] The server uses the Jinja2 template engine to generate a marketing strategy document from the consolidated data, which details the marketing strategy and action plan.

[0122] Input: Integrated data

[0123] Output: Marketing strategy document

[0124] What it does: The server uses Jinja2 to automatically generate a detailed portfolio of "Current Status and Strategies of the AI ​​and Marketing Industry."

[0125] Step 8:

[0126] The server displays the generated marketing strategy document on the user's dashboard, and the terminal updates the dashboard to display the new document.

[0127] Input: Marketing Strategy Document

[0128] Output: Document displayed on the user's dashboard

[0129] What it does: The user's dashboard displays the "latest marketing portfolio on AI and marketing."

[0130] Step 9:

[0131] The server analyzes the data to suggest engagement strategies based on the user profile, using Scikit-learn to make predictions based on past user activity and attribute information.

[0132] Input: User profile information, history data

[0133] Output: The optimal targeting and messaging strategy suggested to the user.

[0134] Specific operation: The server will suggest new related strategies to users who have frequently viewed "AI marketing" related initiatives in the past.

[0135] (Application example 1)

[0136] 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."

[0137] Traditional marketing operations have the problem of requiring a great deal of time and effort to collect and analyze market research data and then propose advertising strategies based on that data. Furthermore, the lack of real-time capabilities makes it difficult to implement prompt and appropriate marketing measures. Another problem is the difficulty of providing optimal engagement measures based on user profiles.

[0138] 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.

[0139] In this invention, the server includes a user authentication means, a market research data collection means, a market research data filtering means, an analysis means using a generative AI model, a means for integrating analysis results, a means for generating a marketing portfolio, a means for displaying the generated marketing portfolio, a means for proposing engagement measures based on a user profile, and a means for collecting real-time market research data and proposing advertising strategies using that information. This enables significant efficiency improvements in marketing operations, utilization of real-time market research data, and the rapid provision of optimal engagement measures based on user profiles.

[0140] The "user authentication means" is a means for verifying the validity of a user based on the user's login information.

[0141] "Market research data collection means" refers to means for collecting data necessary for market research from external data sources.

[0142] A "market research data filtering means" is a means for extracting reliable data from collected data based on specific criteria.

[0143] An "analytical means using a generative artificial intelligence model" is a means for analyzing collected and filtered market data using a generative AI model.

[0144] "Means for integrating analytical results" refers to means for integrating analytical results obtained by generative AI models to generate a single comprehensive analytical result.

[0145] The "means for generating a marketing portfolio" is a means for creating a portfolio including a marketing strategy and an action plan based on the integrated analysis results.

[0146] The "means for displaying the generated marketing portfolio" is a means for displaying the generated marketing portfolio in a format that can be viewed by a user.

[0147] "Means for proposing engagement measures based on user profiles" refers to means for proposing targeting and messaging strategies based on user attribute information and past data.

[0148] "Means for collecting real-time market research data and proposing advertising strategies using that information" refers to means for collecting market research data in real time from external data sources and using that data to propose appropriate advertising strategies.

[0149] The present invention is a system for improving the efficiency of marketing operations, and includes a user authentication means, a market research data collection means, a market research data filtering means, an analysis means using a generative artificial intelligence model, a means for integrating analysis results, a means for generating a marketing portfolio, a means for displaying the generated marketing portfolio, a means for proposing engagement measures based on a user profile, and a means for collecting real-time market research data and proposing advertising strategies using that information.

[0150] This system performs the following processes:

[0151] First, a user logs into the smartphone application. The login process includes verifying the validity of the username and password using Firebase Authentication. If authentication is successful, the user's profile is loaded.

[0152] Next, the user enters keywords related to a specific market or product. Based on the keywords entered, the device collects market research data in real time from the Google Trends API and other similar market data sources. This market research data collection method gathers a wide range of data related to the keywords entered by the user.

[0153] The collected market data is filtered by the terminal using Python. The market research data filtering means extracts reliable information from the collected data based on specific criteria. The filtered data is refined to include only highly accurate information.

[0154] The server then analyzes the filtered data using generative AI models, such as Hugging Face's Transformer model, to derive deep insights from the data based on political, economic, social, and technological factors, as well as company, competitor, and customer factors.

[0155] The obtained analysis results are integrated by the server. A marketing portfolio is generated from the integrated analysis results. A marketing portfolio is a document that details a strategy and action plan.

[0156] The generated marketing portfolio is automatically displayed on the user's dashboard from the server, providing detailed information on the current and forecast market situation, competitor trends, and target customer demand.

[0157] Additionally, the server proposes engagement strategies based on the user profile. The engagement strategy proposal means uses the user's demographic information and historical data to generate individually customized targeting and messaging strategies.

[0158] As a specific example, when a user enters the keyword "AI marketing," the following steps are executed. First, the user is authenticated using Firebase Authentication. Next, trend data related to "AI marketing" is collected from the Google Trends API. Next, the data is filtered using Python to extract the most relevant data. After that, PEST and 3C analyses are performed using Hugging Face's Transformer model, and a marketing portfolio is generated from the integrated analysis results. Finally, the app suggests optimal advertising measures to the user based on the generated portfolio.

[0159] An example of a prompt to be input into a generative AI model is, "Collect and analyze market data related to AI marketing and propose insights based on the following indicators: political influence, economic influence, social influence, technological influence, company situation, competitive trends, and customer demand."

[0160] In this way, the system of the present invention significantly improves the efficiency of marketing operations, enables the utilization of market research data in real time, and can quickly provide optimal engagement measures based on user profiles.

[0161] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0162] Step 1:

[0163] A user launches a smartphone application and enters their login information (username and password). The entered login information is verified on the server side using Firebase Authentication. The server then queries the database and loads the correct user profile. This authenticates the user and allows them to access their individual data.

[0164] Input: Username, Password

[0165] Output: User profile loaded

[0166] Step 2:

[0167] The user enters keywords related to a specific market or product into the application's input fields. The keywords are sent to the server, which collects real-time market data from Google Trends API and other market data sources. The terminal sends an API request to collect the data and receives the results.

[0168] Input:keyword

[0169] Output: Market data collection

[0170] Step 3:

[0171] The server receives the collected market data and filters it using a Python script. The market research data filtering method extracts reliable information based on specific criteria. The filtered data contains only highly accurate information and then moves on to the next step.

[0172] Input: Collected market data

[0173] Output: Filtered market data

[0174] Step 4:

[0175] The server analyzes the filtered data using generative AI models (e.g., the Hugging Face Transformer model), and performs PEST and 3C analyses using prompt statements to gain insights into various factors (political, economic, social, technological, company, competitors, and customers).

[0176] Input: Filtered market data

[0177] Output: Analysis results (PEST analysis and 3C analysis insights)

[0178] Step 5:

[0179] The server then integrates the resulting analysis results and automatically generates a marketing portfolio, which details a marketing strategy and action plan based on a wide range of factors.

[0180] Input: Analysis results

[0181] Output: Marketing portfolio

[0182] Step 6:

[0183] The generated marketing portfolio is displayed on the user's dashboard by the server, allowing the user to view the portfolio within the application and check the current state and forecast of the specific market.

[0184] Enter: Marketing Portfolio

[0185] Output: Marketing portfolio displayed in a dashboard

[0186] Step 7:

[0187] The server then proposes engagement strategies based on the user profile. Taking into account the user's demographic information and past data, a personalized targeting and messaging strategy is automatically generated and presented to the user.

[0188] Input: User profile, historical data

[0189] Output: Proposal of engagement measures

[0190] As a concrete example, when a user enters "AI marketing" as a keyword, the following process is executed: After user authentication using Firebase Authentication, trend data related to "AI marketing" is collected from the Google Trends API. The data is filtered using Python, and PEST and 3C analysis is performed using the Hugging Face Transformer model. The obtained insights are integrated to generate a marketing portfolio and display it on a dashboard. Finally, optimal engagement measures are proposed based on the user profile.

[0191] Example prompt: "Collect and analyze market data on AI marketing and propose insights based on the following metrics: political influence, economic influence, social influence, technological influence, company situation, competitive trends, and customer demand."

[0192] 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.

[0193] The present invention is a system for improving the efficiency of marketing operations, and includes a user authentication means, a market research data collection means, a market research data filtering means, an analysis means using a generative AI model, a means for integrating analysis results, a means for generating a marketing portfolio, a means for displaying the generated marketing portfolio, a means for proposing engagement measures based on a user profile, and an emotion engine for recognizing user emotions.

[0194] The user enters login information. The user enters their username and password on the system's login screen. The server checks the information against a database to verify the user's authenticity. If the match is successful, the user profile is loaded.

[0195] Next, the user inputs keywords related to market research. The user then inputs search keywords for creating a marketing portfolio into the device. The device collects market data from external data sources (such as APIs) based on the keywords. The server retrieves the collected data and filters it based on specific criteria to extract relevant information.

[0196] The server then uses the generative AI model to perform PEST and 3C analyses on the filtered market data. PEST analysis analyzes political, economic, social, and technological factors, while 3C analysis analyzes company, competitor, and customer factors. This provides a deep understanding of a wide range of factors.

[0197] The server integrates the analysis results and automatically creates a marketing portfolio, which details marketing strategies and action plans. The server then displays the created marketing portfolio on the user's dashboard, where the user can view it.

[0198] Additionally, the server will suggest engagement strategies based on the user profile, including targeting and messaging strategies using user demographics and historical data, resulting in optimal marketing strategies for the user.

[0199] The present invention introduces an emotion engine that analyzes a user's emotional state to further enhance marketing initiatives. Specifically, by analyzing a user's inputs and operations and identifying their emotional state, more personalized recommendations can be made. The emotion engine analyzes the user's text inputs, click patterns, mouse movements, etc. to determine whether the user is excited, confused, or calm. Based on this, engagement initiatives can be appropriately adjusted. For example, if it is determined that the user is dissatisfied, special offers or support information can be presented to alleviate the dissatisfaction.

[0200] For example, if a user types in the keywords "AI" and "marketing," the sentiment engine analyzes the user's reaction to the search results. If the user responds positively to the search results, the server can further increase the user's engagement by suggesting new marketing campaigns or powerful strategies against competitors.

[0201] This invention significantly reduces the work required to create one report, which previously took approximately 50 hours, thereby reducing the burden on marketing portfolio managers. Furthermore, by incorporating an emotion engine, it becomes possible to respond flexibly to user emotions, resulting in higher engagement and more effective marketing strategies.

[0202] The processing flow will be explained below.

[0203] Step 1:

[0204] The user enters login information. The user enters their username and password on the system login screen.

[0205] Step 2:

[0206] The server authenticates the login information. It checks the entered username and password against a database to verify the user's identity. If the match is successful, it loads the user profile.

[0207] Step 3:

[0208] The user inputs market research keywords. The user inputs search keywords for creating a marketing portfolio into the terminal. For example, let's use keywords such as "AI" and "marketing."

[0209] Step 4:

[0210] The device collects market research data. It uses input keywords to retrieve market data from external APIs, which then return relevant articles, reports, and statistics.

[0211] Step 5:

[0212] The server filters the collected market data. The server sorts the acquired market data based on criteria and extracts reliable and up-to-date information.

[0213] Step 6:

[0214] The server performs a PEST analysis, analyzing political, economic, social, and technological factors against market data filtered using a generative AI model. For example, it evaluates government AI policies, economic impact, social acceptance, and technological advances.

[0215] Step 7:

[0216] The server performs the 3C analysis. It also uses a generative AI model to analyze the company, competitors, and customers. For example, it evaluates the company's strengths and weaknesses, competitor trends, customer needs, and market trends.

[0217] Step 8:

[0218] The server integrates the results of the analysis. The server integrates the results of the PEST analysis and the 3C analysis to obtain comprehensive insights. Marketing strategies and action plans are determined based on these insights.

[0219] Step 9:

[0220] The server automatically generates a marketing portfolio based on the integrated analysis results. The portfolio includes specific strategic items, market forecasts, and measures.

[0221] Step 10:

[0222] The server displays the marketing portfolio. The server displays the generated marketing portfolio on the user's dashboard. The user views it and checks the required information.

[0223] Step 11:

[0224] The server starts an emotion engine to analyze the user's reaction. The emotion engine analyzes the user's input, operation, mouse movement, etc. to identify the user's emotional state.

[0225] Step 12:

[0226] The server proposes appropriate engagement strategies based on the user's emotional state and user profile, as recognized by the emotion engine. For example, if the user is dissatisfied, the server may present special offers or support information.

[0227] By going through each step in this way, users can not only create a marketing portfolio efficiently, but also respond flexibly according to their emotional state.

[0228] Example 2

[0229] 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."

[0230] Traditional marketing operations require a great deal of time and effort, and there is a need for greater efficiency, particularly in market research and data analysis. Another issue is the difficulty of proposing personalized engagement measures that take user emotions into account. There is a need for a system that can solve these problems, improve operational efficiency, and realize effective marketing measures.

[0231] 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.

[0232] In this invention, the server includes a user authentication means, a market research data collection means, a market research data filtering means, an analysis means using a generative AI model, a means for integrating analysis results, a means for generating a marketing portfolio, a means for displaying the generated marketing portfolio, a means for proposing engagement measures based on a user profile, a means for analyzing user emotions, and a means for adjusting the engagement measures based on the analyzed emotions. This makes it possible to improve the efficiency of marketing operations and realize flexible marketing measures that correspond to user emotions.

[0233] "User authentication means" is a means used by a user to log in to a system, and is a function that authenticates the user's legitimacy by inputting a user name and password.

[0234] The "market research data collection means" is a function that allows the terminal or server to collect market data from external data sources based on keywords entered by the user.

[0235] The "market research data filtering means" is a function that filters collected market data based on specific criteria and extracts only highly relevant data.

[0236] An "analysis means using a generative artificial intelligence model" is a means for analyzing market data using a generative AI model (e.g., a generative language model) and performing a specific analysis.

[0237] The "means for integrating analysis results" is a function for integrating the analysis results obtained individually and generating a comprehensive report.

[0238] The "means for generating a marketing portfolio" is a function for automatically generating a portfolio including a marketing strategy and an action plan based on the integrated analysis results.

[0239] The "means for displaying the generated marketing portfolio" is a function that displays the generated marketing portfolio on the user's dashboard and allows the user to view or download it.

[0240] "Means to propose engagement measures based on user profiles" is a function that proposes targeting and messaging strategies based on user attribute information and past data.

[0241] The "means for analyzing user emotions" is a function that analyzes the user's inputs and operations and identifies the user's emotional state.

[0242] The "means for adjusting engagement measures based on analyzed emotions" is a function that appropriately adjusts engagement measures and provides appropriate offers and support information based on the analyzed emotional state of the user.

[0243] The present invention provides a system for improving the efficiency of marketing operations and providing flexible engagement measures that correspond to user emotions. Specific embodiments will be described below.

[0244] User Authentication

[0245] The user enters a username and password on the system's login screen. The terminal sends this information to the server, which checks the database to verify the user's authenticity. If authentication is successful, the server loads the user profile and returns a login success message to the user.

[0246] Market research data collection

[0247] The user inputs keywords for market research. The device sends these keywords to the server. The server accesses external data sources (e.g., APIs, databases) and collects market data based on the specified keywords. This data is temporarily stored on the server.

[0248] Filtering Market Research Data

[0249] The server filters the collected data according to certain criteria, which involves extracting reliable data and filtering out less relevant data.

[0250] Data analysis

[0251] The server performs PEST analysis (political, economic, social, technological) and 3C analysis (company, competitors, customers) on the collected and filtered market data using a generative artificial intelligence model (e.g., GPT-4®). The prompt text is as follows:

[0252] Conduct a PEST analysis and 3C analysis based on the following data: Data: {Collected market data}

[0253] Integration of analysis results

[0254] The server consolidates the results of each analysis and generates a comprehensive report containing insights and conclusions that form the basis of marketing strategies.

[0255] Marketing portfolio generation

[0256] The server automatically generates a marketing portfolio based on the integrated analysis results, which describes a detailed marketing strategy and action plan.

[0257] View the generated marketing portfolio

[0258] The server displays the generated marketing portfolio on the user's dashboard, where the user can view it and download or print it as needed.

[0259] Proposing engagement measures

[0260] The server then proposes engagement strategies based on the user profile, including targeting and messaging strategies, leveraging the user's historical data and demographic information to provide personalized recommendations.

[0261] Emotion analysis

[0262] The server runs an emotion engine based on the user's input and operations to analyze the user's emotional state. For example, it analyzes the user's click patterns and text input to identify the user's emotional state (e.g., excited, frustrated, calm).

[0263] Tailoring engagement strategies based on emotions

[0264] The server can then adjust engagement strategies appropriately based on the analyzed emotional state: for example, if a user expresses dissatisfaction, it can increase engagement by providing special discount offers or additional support information.

[0265] As described above, the present invention makes it possible to improve the efficiency of marketing operations and provide flexible and effective marketing measures that respond to the emotions of users.

[0266] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0267] Step 1:

[0268] User Authentication

[0269] Input: The user enters their username and password on the login screen.

[0270] Specific operation: The terminal sends the entered username and password to the server.

[0271] Data processing: The server checks the received username and password against a database to verify the user's authenticity.

[0272] Output: If authentication is successful, the server loads the user profile and sends a login successful message to the user.

[0273] Step 2:

[0274] Market research data collection

[0275] Input: The user inputs keywords into the terminal to create a marketing portfolio.

[0276] Specific operation: The terminal sends the input keyword to the server.

[0277] Data Processing: The server accesses external data sources (APIs, databases) and collects market data based on the specified keywords.

[0278] Output: The collected market data is temporarily stored on the server.

[0279] Step 3:

[0280] Filtering Market Research Data

[0281] Input: Market data temporarily stored on the server.

[0282] Specific Actions: The server filters the data based on certain criteria (e.g., data reliability, date, relevance).

[0283] Data processing: Filtered market data removes irrelevant data and extracts only the necessary data.

[0284] Output: The filtered market data is passed on to the next analytical step.

[0285] Step 4:

[0286] Data analysis (PEST analysis and 3C analysis)

[0287] Input: Filtered market data.

[0288] Specific operation: The server generates prompt sentences using a generative artificial intelligence model (e.g., GPT-4) and performs analysis.

[0289] example:

[0290] Conduct a PEST and 3C analysis based on the following data: Data: {Filtered Market Data}

[0291] Data calculation: The generative AI model performs PEST and 3C analysis on the input market data and analyzes each element.

[0292] Output: The analysis results are saved on the server.

[0293] Step 5:

[0294] Integration of analysis results

[0295] Input: Results of PEST analysis and 3C analysis.

[0296] Specific operation: The server integrates the results of each analysis.

[0297] Data Processing: Comprehensive reports are generated from the integrated analysis results.

[0298] Output: A comprehensive report is generated and passed to the next step, Marketing Portfolio Generation.

[0299] Step 6:

[0300] Marketing portfolio generation

[0301] Input: Consolidated analysis results.

[0302] Specific operation: The server automatically generates a marketing portfolio based on the comprehensive report.

[0303] Data Processing: A detailed portfolio is generated, including marketing strategies and action plans.

[0304] Output: The generated marketing portfolio is saved on the server.

[0305] Step 7:

[0306] View the generated marketing portfolio

[0307] Input: The generated marketing portfolio.

[0308] Specific operation: The server displays the generated portfolio on the user's dashboard.

[0309] Output: Users can view the portfolio and download or print it as needed.

[0310] Step 8:

[0311] Proposing engagement measures

[0312] Input: User profile and historical data.

[0313] Specific operation: The server proposes optimal engagement measures based on the user profile.

[0314] Data processing: Analyze historical data and attribute information to develop targeting and messaging strategies.

[0315] Output: The user is notified of the suggested engagement measures.

[0316] Step 9:

[0317] Emotion analysis

[0318] Input: User input and operation data (click patterns, text input, etc.).

[0319] Specific operation: The server uses the emotion engine to analyze the user's emotional state in real time.

[0320] Data calculation: Analyzes user operation data and identifies emotional states such as excitement, frustration, and calmness.

[0321] Output: The analyzed emotional state is used to adjust the next policy.

[0322] Step 10:

[0323] Tailoring engagement strategies based on emotions

[0324] Input: Parsed emotional state.

[0325] Specific behavior: The server adjusts engagement strategies based on emotional state.

[0326] Data processing: Special offers and support information are generated according to the user's emotional state.

[0327] Output: The tailored engagement measures are provided to the user.

[0328] (Application example 2)

[0329] 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."

[0330] Conventional marketing systems mainly respond only to users' online actions and inputs, and lack mechanisms for utilizing customer behavior data in physical stores. This makes it difficult to immediately provide appropriate marketing measures based on customers' emotions and behavior in physical stores. Furthermore, the lack of real-time personalized information notifications leads to a decline in customer engagement. The present invention aims to solve these issues by analyzing customer behavior data and emotional states in physical stores in real time and providing personalized measures based on the results.

[0331] 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.

[0332] In this invention, the server includes means for analyzing the emotional state of a user, means for adjusting marketing measures based on the analyzed emotional state, means for collecting customer behavior data in a physical store and analyzing the emotional state in real time, and means for notifying personalized information in real time according to the customer's movements and behavior in the physical store. This makes it possible to instantly provide appropriate marketing measures based on the customer's behavior and emotions in the physical store, and is expected to improve customer engagement.

[0333] "User authentication" refers to the method or process by which a user verifies their identity and gains access to a system.

[0334] "Market research data collection tools" are methods and techniques for collecting information about market trends and consumer preferences.

[0335] "Market research data filtering means" refers to methods or techniques for selecting reliable information from collected market data based on specific criteria.

[0336] "Analytical methods using generative artificial intelligence models" are methods or techniques that use generative AI models to analyze data and derive insights or conclusions.

[0337] "Means for synthesizing analytical results" are methods or techniques for bringing together the results of separate data analyses and producing a single, unified conclusion or report.

[0338] "Means for generating a marketing portfolio" refers to methods and techniques for creating specific marketing strategies and action plans based on the results of analysis.

[0339] The "means for displaying the generated marketing portfolio" refers to a method or technology that allows a user to view the generated marketing portfolio.

[0340] "Means for proposing engagement measures based on user profiles" refers to methods and technologies for proposing appropriate marketing measures and messages based on user attribute information and past behavioral data.

[0341] A "means for analyzing a user's emotional state" is a method or technique for monitoring a user's actions or inputs and identifying the user's emotional state therefrom.

[0342] "Means for adjusting marketing strategies based on the analyzed emotional state" refers to methods and technologies for changing and adapting marketing strategies and messages according to the emotional state of the user.

[0343] "Means for collecting customer behavioral data in a physical store and analyzing their emotional state in real time" refers to methods and technologies for instantly obtaining customer behavioral data in a physical store and analyzing the emotional state of customers based on that data.

[0344] "Means of notifying customers of personalized information in real time according to their movements and actions within a physical store" refers to methods and technologies that instantly notify customers of individually optimized information and offers when they move to a specific area or take a specific action within a physical store.

[0345] The present invention is a system for improving the efficiency of marketing operations, and includes the following means.

[0346] User authentication method

[0347] A user launches an application and hits a login screen, where they enter their username and password, and the server validates the user by checking that information against a database, and then loads the user profile accordingly.

[0348] Market research data collection methods

[0349] The user inputs search keywords for creating a marketing portfolio into the device. The device collects market data from external data sources (such as APIs) based on the keywords. The server retrieves and stores the collected data.

[0350] Market research data filtering tools

[0351] The server filters the collected market data based on specific criteria to extract reliable information, thereby retaining the most relevant data.

[0352] Analytical tools using generative artificial intelligence models

[0353] The server performs PEST analysis (analysis of political, economic, social, and technological factors) and 3C analysis (analysis of company, competitor, and customer factors) on the market data filtered using the generative AI model.

[0354] A means of synthesizing analytical results

[0355] The analysis results are integrated by the server, and a marketing portfolio is automatically created. The integrated data details marketing strategies and action plans.

[0356] A means to create and display marketing portfolios

[0357] The created marketing portfolio is displayed on the user's dashboard and can be viewed by the user.

[0358] A means of proposing engagement measures based on user profiles

[0359] The server recommends engagement strategies based on the user profile, including targeting and messaging strategies using user demographics and historical data.

[0360] A means of analyzing the user's emotional state

[0361] The server uses an emotion engine that analyzes user input and actions to identify their emotional state. This is achieved by analyzing the user's text input, click patterns, mouse movements, etc.

[0362] A means of adjusting marketing efforts based on analyzed emotional states

[0363] After the emotion engine identifies the user's emotional state, the server can use this information to adjust marketing efforts appropriately. For example, if it determines that the user is excited, it will display information about new campaigns. However, if it determines that the user is confused, it will prioritize displaying help and support information.

[0364] In-store operation

[0365] Working in a physical store, the application collects real-time customer behavior data (e.g., product scanning, feedback input, in-app chat), which is used by an emotion engine to analyze the customer's emotional state.

[0366] Real-time notifications of new information

[0367] When a customer moves to a specific area or takes a specific action in a physical store, the system will send them personalized information in real time. For example, when a customer scans a specific product, they will instantly receive push notifications about related campaigns and special offers.

[0368] Hardware and software used

[0369] The hardware used to implement this invention includes a smartphone (compatible with iOS / ANDROID (registered trademark)), a server (cloud server), and a database (MySQL, MongoDB). The software includes a smartphone app development framework (React Native), a generative AI model, and a sentiment analysis engine.

[0370] Examples of prompt sentences

[0371] The following prompts are used:

[0372] "We will explain how to analyze customer behavior data to make personalized product suggestions to increase customer engagement in physical stores. When a customer scans a product, the data is sent to a server in real time and analyzed by a sentiment analysis engine. If the customer responds positively, a generative AI model is used to generate targeted product and campaign information, which is then displayed on the app dashboard."

[0373] As described above, the present invention aims to improve customer engagement by analyzing customer behavioral data and emotional states in physical stores in real time and providing personalized measures based on that data.

[0374] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0375] Step 1:

[0376] User Authentication

[0377] A user launches an application and enters their username and password at the login screen. The server receives this input and checks it against the authentication information in its database.

[0378] Input: Username, Password

[0379] Processing: Database Matching

[0380] Output: Authentication result, user profile

[0381] Step 2:

[0382] Market research data collection

[0383] The user inputs search keywords for creating a marketing portfolio into the device, which then collects market data via external data sources (such as APIs) based on the keywords and sends it to the server.

[0384] Input: Search keyword

[0385] Processing: Data collection from external data sources

[0386] Output: Market data

[0387] Step 3:

[0388] Market Research Data Filtering

[0389] The server filters the collected market data based on specific criteria to extract reliable information.

[0390] Input: Market Data

[0391] Processing: Data Filtering

[0392] Output: Filtered market data

[0393] Step 4:

[0394] Data analysis using generative artificial intelligence models

[0395] The server uses generative AI models to perform PEST and 3C analysis on the filtered market data.

[0396] Input: Filtered market data

[0397] Processing: PEST analysis, 3C analysis

[0398] Output: Analysis results

[0399] Step 5:

[0400] Integration of analysis results

[0401] The server integrates the generated analysis results and automatically generates a marketing portfolio.

[0402] Input: Analysis results

[0403] Processing: Data Integration

[0404] Output: Marketing portfolio

[0405] Step 6:

[0406] View Marketing Portfolio

[0407] The generated marketing portfolio is displayed on the user's dashboard, where the user can view it.

[0408] Enter: Marketing Portfolio

[0409] Processing: Data display

[0410] Output: Marketing portfolio on a dashboard

[0411] Step 7:

[0412] Proposing engagement measures

[0413] The server proposes engagement measures based on the user profile.

[0414] Input: User profile, marketing portfolio

[0415] Processing: Targeting, Messaging

[0416] Output: Proposed engagement measures

[0417] Step 8:

[0418] Analyzing the user's emotional state

[0419] The server analyzes emotions from the user's inputs and actions (e.g., text input, click patterns, mouse movements) to identify their emotional state.

[0420] Input: User input and operation data

[0421] Processing: Sentiment Analysis

[0422] Output: User's emotional state

[0423] Step 9:

[0424] Adjusting policies based on analyzed emotional states

[0425] Based on the analysis results of the emotion engine, the server dynamically adjusts marketing measures.

[0426] Input: User's emotional state

[0427] Action: Adjustment of measures

[0428] Output: Coordinated marketing efforts

[0429] Step 10:

[0430] Collecting customer behavior data in physical stores and analyzing it in real time

[0431] Customer behavioral data (e.g., product scanning, feedback input) is collected in real time within physical stores and their emotional state is analyzed.

[0432] Input: Customer behavior data

[0433] Processing: Real-time data collection, sentiment analysis

[0434] Output: Customer emotional state and behavior data

[0435] Step 11:

[0436] Real-time personalized notifications

[0437] The server then sends personalized information in real time based on customer behavior, such as pushing relevant information when scanning a specific product or entering a specific area.

[0438] Input: Customer behavior data, emotional state

[0439] Processing: Information generation, notification

[0440] Output: Personalized push notification

[0441] Through these steps, the system is able to instantly provide appropriate marketing measures based on customer behavior and emotions in physical stores.

[0442] 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.

[0443] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0444] 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.

[0445] [Second embodiment]

[0446] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0447] 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.

[0448] 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).

[0449] 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.

[0450] 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.

[0451] 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).

[0452] 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.

[0453] 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.

[0454] 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.

[0455] 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.

[0456] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0457] 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."

[0458] The present invention is a system for streamlining marketing operations, and includes a user authentication means, a market research data collection means, a market research data filtering means, an analysis means using a generative AI model, a means for integrating analysis results, a means for generating a marketing portfolio, a means for displaying the generated marketing portfolio, and a means for proposing engagement measures based on a user profile.

[0459] When a user enters their login information, the server checks the information against a database to verify the user's authenticity. Specifically, the server queries the database for the username and password, and if successful, loads the user profile.

[0460] Next, the user enters keywords related to market research, and the device collects market data from external data sources (such as APIs) based on the keywords. The server retrieves the collected data and filters it based on specific criteria to extract the most relevant information.

[0461] The server then uses the generative AI model to perform PEST and 3C analyses on the filtered market data. PEST analysis analyzes political, economic, social, and technological factors, while 3C analysis analyzes company, competitor, and customer factors. This provides a deep understanding of a wide range of factors.

[0462] The server integrates the analysis results and automatically creates a marketing portfolio, which details marketing strategies and action plans. The server then displays the created marketing portfolio on the user's dashboard, where the user can view it.

[0463] Additionally, the server will suggest engagement strategies based on the user profile, including targeting and messaging strategies using user demographics and historical data, resulting in optimal marketing strategies for the user.

[0464] For example, when a user enters keywords such as "AI" and "marketing," data on the AI ​​market and marketing trends is collected from external market data sources. The server filters this data and performs PEST and 3C analyses to obtain insights into the current and forecast state of the AI ​​market, competitor trends, and target customer demand. Finally, a marketing portfolio is automatically generated based on this information, and the user is presented with the next step in marketing strategies.

[0465] This invention significantly reduces the work that previously took about 50 hours per report, thereby reducing the burden on marketing portfolio staff.

[0466] The processing flow will be explained below.

[0467] Step 1:

[0468] The user enters login information. The user enters their username and password on the system login screen.

[0469] Step 2:

[0470] The server authenticates the login information. It checks the entered username and password against a database to verify the user's identity. If the match is successful, the user profile is loaded.

[0471] Step 3:

[0472] The user inputs market research keywords. The user inputs search keywords for creating a marketing portfolio into the terminal.

[0473] Step 4:

[0474] The terminal collects market research data. The terminal sends keywords to an external data source (e.g., API) to obtain relevant market data.

[0475] Step 5:

[0476] The server filters the collected market data. The server filters the acquired market data based on specific criteria (e.g., the latest, most reliable information) to extract the most relevant information.

[0477] Step 6:

[0478] The server performs the PEST analysis, using a generative AI model to analyze political, economic, social, and technological factors against the filtered market data.

[0479] Step 7:

[0480] The server then performs the 3C analysis, also using a generative AI model to analyze the filtered market data for the company, competitors, and customers.

[0481] Step 8:

[0482] The server integrates the results of the analysis. The server integrates the results of the PEST analysis and the 3C analysis and automatically generates a marketing portfolio that includes strategic items and action plans.

[0483] Step 9:

[0484] The server displays the marketing portfolio. The server displays the generated marketing portfolio on the user's dashboard, where the user can view it.

[0485] Step 10:

[0486] The server proposes engagement strategies: The server proposes appropriate engagement strategies (targeting, messaging, etc.) based on the user profile.

[0487] By going through each step in this way, the user can efficiently create a marketing portfolio.

[0488] Example 1

[0489] 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."

[0490] Traditional marketing operations require a significant amount of time and effort to conduct market research, filter and analyze the data, and then integrate the results to create a marketing strategy. Furthermore, because much of this work is done manually, there is a high risk of human error and efficiency is low. Another issue is that it is difficult to appropriately propose engagement measures based on user profiles.

[0491] 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.

[0492] In this invention, the server includes a user authentication means, a market research data acquisition means, a market research data selection means, an analysis means using a generative artificial intelligence model, a means for integrating the analysis results, a means for generating a marketing strategy document, a means for displaying the generated marketing strategy document, and a means for proposing an engagement strategy based on a user profile. This makes marketing operations more efficient, significantly reduces time and labor, reduces human error, and enables the proposal of appropriate engagement measures.

[0493] "User authentication means" is a means for verifying the validity of a user by querying a database using authentication information (user name, password, etc.) entered by the user.

[0494] The "market research data acquisition means" is a means for collecting market research data from an external data source (such as an API) based on keywords specified by the user.

[0495] The "market research data selection means" is a means for filtering collected market research data based on specific criteria and extracting only highly relevant information.

[0496] An "analysis means using a generative artificial intelligence model" is a means for analyzing market research data that has been filtered using generative artificial intelligence (e.g., GPT-3).

[0497] The "means for integrating the analysis results" refers to a means for compiling the analysis results obtained by the generative artificial intelligence model into a single integrated data set.

[0498] The "means for generating a marketing strategy document" is a means for automatically creating a document detailing a marketing strategy and an action plan based on the integrated analysis results.

[0499] The "means for displaying the generated marketing strategy document" refers to a means for displaying the generated marketing strategy document on the dashboard of the user's operating terminal.

[0500] "Means for proposing engagement strategies based on user profiles" refers to means for proposing optimal targeting and messaging strategies based on user attribute information and past data.

[0501] The present invention is a system for improving the efficiency of marketing operations, and includes a user authentication means, a market research data acquisition means, a market research data selection means, an analysis means using a generative artificial intelligence model, a means for integrating the analysis results, a means for generating a marketing strategy document, a means for displaying the generated marketing strategy document, and a means for proposing an engagement strategy based on a user profile.

[0502] First, the user enters their login information (username and password) at the terminal, which then sends this information to the server, which checks the MySQL database to verify the user's authenticity. If the match is successful, the server loads the user profile.

[0503] Next, the user enters keywords related to market research (e.g., "AI" or "marketing"), and the device sends the keywords to the server, which then uses Python's requests library to retrieve market research data from external data sources (e.g., the Google Trends API).

[0504] The acquired data is processed by the server using the Pandas library and filtered based on specific criteria (e.g., time period or relevance).The server then inputs the filtered data into a generative artificial intelligence model (e.g., GPT-3) to perform PEST (political, economic, social, technological) and 3C (company, competitor, customer) analysis.

[0505] Example prompt sentence:

[0506] "Conduct a PEST analysis of the AI ​​and Marketing industry based on the following data: [filtered data]"

[0507] The analysis results obtained from the generative AI model are integrated by the server to automatically create a marketing strategy document, which is generated using the Jinja2 template engine and displayed on the dashboard of the user's device.

[0508] Additionally, the server suggests optimal engagement strategies (e.g., targeting and messaging strategies) based on the user profile, using machine learning models such as Scikit-learn to make predictions based on past user activity and demographic information.

[0509] As a specific example, if a user enters the keywords "AI" and "marketing," the server uses the Google Trends API to collect data on the AI ​​market and marketing trends. The collected data is filtered, and PEST and 3C analyses are performed using GPT-3. The results reveal the current state and forecast of the AI ​​market, the trends of competitors, and the needs of target customers. Based on this information, a marketing strategy document is generated and presented to the user. Optimal engagement measures are also proposed based on the user profile.

[0510] This streamlines marketing operations, significantly reducing the time and errors involved compared to traditional manual analysis.

[0511] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0512] Step 1:

[0513] The user enters a username and password on the login page. The device sends the information to the server, which then checks the received login information against a MySQL database to verify the user's authenticity. If the verification is successful, the server loads the user profile information and starts the session.

[0514] Input: Username, Password

[0515] Output: Authentication successful (session started) or authentication failed (error message)

[0516] Step 2:

[0517] The user enters keywords related to market research (e.g., "AI" or "marketing"). The device sends the keywords to the server. The server receives the keywords and proceeds to the next processing step.

[0518] Input: keyword

[0519] Output: The keyword is sent to the server

[0520] Step 3:

[0521] The server uses the provided keywords to collect market research data from external data sources (e.g., Google Trends API), using the Python requests library.

[0522] Input: keyword

[0523] Output: Market research data (obtained from external data sources)

[0524] Specific operation: The server sends a request to the Google Trends API for the keywords "AI" and "marketing" to obtain related market data.

[0525] Step 4:

[0526] The server processes the acquired market data using the Pandas library and filters it based on specified criteria (e.g., time range or correlation), eliminating unnecessary data and extracting only the most relevant information.

[0527] Input: Market research data

[0528] Output: Filtered market research data

[0529] Specific operation: The server keeps AI marketing-related data from the past year and removes old or irrelevant data from before that.

[0530] Step 5:

[0531] The server inputs the filtered data into a generative artificial intelligence model (e.g., GPT-3) to perform PEST analysis (political, economic, social, technological) and 3C analysis (company, competitors, customers). The server generates prompt statements, passes them to the AI ​​model, and receives responses from the AI.

[0532] Input: Filtered market research data

[0533] Output: PEST and 3C analysis results

[0534] Specific behavior: The server generates the following prompt:

[0535] "Conduct a PEST analysis of the AI ​​and Marketing industry based on the following data: [filtered data]"

[0536] Receive responses from the generative AI model and obtain analytical results.

[0537] Step 6:

[0538] The server uses Pandas to integrate the results of the PEST and 3C analyses obtained from the generative artificial intelligence model and compile them into a single dataset.

[0539] Input: PEST and 3C analysis results

[0540] Output: Marketing insights to be integrated

[0541] Specific operation: The server compiles data on "Politics," "Economy," "Society," "Technology," as well as "Company," "Competitor," and "Customer" into a single data frame.

[0542] Step 7:

[0543] The server uses the Jinja2 template engine to generate a marketing strategy document from the consolidated data, which details the marketing strategy and action plan.

[0544] Input: Integrated data

[0545] Output: Marketing strategy document

[0546] What it does: The server uses Jinja2 to automatically generate a detailed portfolio of "Current Status and Strategies of the AI ​​and Marketing Industry."

[0547] Step 8:

[0548] The server displays the generated marketing strategy document on the user's dashboard, and the terminal updates the dashboard to display the new document.

[0549] Input: Marketing Strategy Document

[0550] Output: Document displayed on the user's dashboard

[0551] What it does: The user's dashboard displays the "latest marketing portfolio on AI and marketing."

[0552] Step 9:

[0553] The server analyzes the data to suggest engagement strategies based on the user profile, using Scikit-learn to make predictions based on past user activity and attribute information.

[0554] Input: User profile information, history data

[0555] Output: The optimal targeting and messaging strategy suggested to the user.

[0556] Specific operation: The server will suggest new related strategies to users who have frequently viewed "AI marketing" related initiatives in the past.

[0557] (Application example 1)

[0558] 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."

[0559] Traditional marketing operations have the problem of requiring a great deal of time and effort to collect and analyze market research data and then propose advertising strategies based on that data. Furthermore, the lack of real-time capabilities makes it difficult to implement prompt and appropriate marketing measures. Another problem is the difficulty of providing optimal engagement measures based on user profiles.

[0560] 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.

[0561] In this invention, the server includes a user authentication means, a market research data collection means, a market research data filtering means, an analysis means using a generative AI model, a means for integrating analysis results, a means for generating a marketing portfolio, a means for displaying the generated marketing portfolio, a means for proposing engagement measures based on a user profile, and a means for collecting real-time market research data and proposing advertising strategies using that information. This enables significant efficiency improvements in marketing operations, utilization of real-time market research data, and the rapid provision of optimal engagement measures based on user profiles.

[0562] The "user authentication means" is a means for verifying the validity of a user based on the user's login information.

[0563] "Market research data collection means" refers to means for collecting data necessary for market research from external data sources.

[0564] A "market research data filtering means" is a means for extracting reliable data from collected data based on specific criteria.

[0565] An "analytical means using a generative artificial intelligence model" is a means for analyzing collected and filtered market data using a generative AI model.

[0566] "Means for integrating analytical results" refers to means for integrating analytical results obtained by generative AI models to generate a single comprehensive analytical result.

[0567] The "means for generating a marketing portfolio" is a means for creating a portfolio including a marketing strategy and an action plan based on the integrated analysis results.

[0568] The "means for displaying the generated marketing portfolio" is a means for displaying the generated marketing portfolio in a format that can be viewed by a user.

[0569] "Means for proposing engagement measures based on user profiles" refers to means for proposing targeting and messaging strategies based on user attribute information and past data.

[0570] "Means for collecting real-time market research data and proposing advertising strategies using that information" refers to means for collecting market research data in real time from external data sources and using that data to propose appropriate advertising strategies.

[0571] The present invention is a system for improving the efficiency of marketing operations, and includes a user authentication means, a market research data collection means, a market research data filtering means, an analysis means using a generative artificial intelligence model, a means for integrating analysis results, a means for generating a marketing portfolio, a means for displaying the generated marketing portfolio, a means for proposing engagement measures based on a user profile, and a means for collecting real-time market research data and proposing advertising strategies using that information.

[0572] This system performs the following processes:

[0573] First, a user logs into the smartphone application. The login process includes verifying the validity of the username and password using Firebase Authentication. If authentication is successful, the user's profile is loaded.

[0574] Next, the user enters keywords related to a specific market or product. Based on the keywords entered, the device collects market research data in real time from the Google Trends API and other similar market data sources. This market research data collection method gathers a wide range of data related to the keywords entered by the user.

[0575] The collected market data is filtered by the terminal using Python. The market research data filtering means extracts reliable information from the collected data based on specific criteria. The filtered data is refined to include only highly accurate information.

[0576] The server then analyzes the filtered data using generative AI models, such as Hugging Face's Transformer model, to derive deep insights from the data based on political, economic, social, and technological factors, as well as company, competitor, and customer factors.

[0577] The obtained analysis results are integrated by the server. A marketing portfolio is generated from the integrated analysis results. A marketing portfolio is a document that details a strategy and action plan.

[0578] The generated marketing portfolio is automatically displayed on the user's dashboard from the server, providing detailed information on the current and forecast market situation, competitor trends, and target customer demand.

[0579] Additionally, the server proposes engagement strategies based on the user profile. The engagement strategy proposal means uses the user's demographic information and historical data to generate individually customized targeting and messaging strategies.

[0580] As a specific example, when a user enters the keyword "AI marketing," the following steps are executed. First, the user is authenticated using Firebase Authentication. Next, trend data related to "AI marketing" is collected from the Google Trends API. Next, the data is filtered using Python to extract the most relevant data. After that, PEST and 3C analyses are performed using Hugging Face's Transformer model, and a marketing portfolio is generated from the integrated analysis results. Finally, the app suggests optimal advertising measures to the user based on the generated portfolio.

[0581] An example of a prompt to be input into a generative AI model is, "Collect and analyze market data related to AI marketing and propose insights based on the following indicators: political influence, economic influence, social influence, technological influence, company situation, competitive trends, and customer demand."

[0582] In this way, the system of the present invention significantly improves the efficiency of marketing operations, enables the utilization of market research data in real time, and can quickly provide optimal engagement measures based on user profiles.

[0583] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0584] Step 1:

[0585] A user launches a smartphone application and enters their login information (username and password). The entered login information is verified on the server side using Firebase Authentication. The server then queries the database and loads the correct user profile. This authenticates the user and allows them to access their individual data.

[0586] Input: Username, Password

[0587] Output: User profile loaded

[0588] Step 2:

[0589] The user enters keywords related to a specific market or product into the application's input fields. The keywords are sent to the server, which collects real-time market data from Google Trends API and other market data sources. The terminal sends an API request to collect the data and receives the results.

[0590] Input:keyword

[0591] Output: Market data collection

[0592] Step 3:

[0593] The server receives the collected market data and filters it using a Python script. The market research data filtering method extracts reliable information based on specific criteria. The filtered data contains only highly accurate information and then moves on to the next step.

[0594] Input: Collected market data

[0595] Output: Filtered market data

[0596] Step 4:

[0597] The server analyzes the filtered data using generative AI models (e.g., the Hugging Face Transformer model), and performs PEST and 3C analyses using prompt statements to gain insights into various factors (political, economic, social, technological, company, competitors, and customers).

[0598] Input: Filtered market data

[0599] Output: Analysis results (PEST analysis and 3C analysis insights)

[0600] Step 5:

[0601] The server then integrates the resulting analysis results and automatically generates a marketing portfolio, which details a marketing strategy and action plan based on a wide range of factors.

[0602] Input: Analysis results

[0603] Output: Marketing portfolio

[0604] Step 6:

[0605] The generated marketing portfolio is displayed on the user's dashboard by the server, allowing the user to view the portfolio within the application and check the current state and forecast of the specific market.

[0606] Enter: Marketing Portfolio

[0607] Output: Marketing portfolio displayed in a dashboard

[0608] Step 7:

[0609] The server then proposes engagement strategies based on the user profile. Taking into account the user's demographic information and past data, a personalized targeting and messaging strategy is automatically generated and presented to the user.

[0610] Input: User profile, historical data

[0611] Output: Proposal of engagement measures

[0612] As a concrete example, when a user enters "AI marketing" as a keyword, the following process is executed: After user authentication using Firebase Authentication, trend data related to "AI marketing" is collected from the Google Trends API. The data is filtered using Python, and PEST and 3C analysis is performed using the Hugging Face Transformer model. The obtained insights are integrated to generate a marketing portfolio and display it on a dashboard. Finally, optimal engagement measures are proposed based on the user profile.

[0613] Example prompt: "Collect and analyze market data on AI marketing and propose insights based on the following metrics: political influence, economic influence, social influence, technological influence, company situation, competitive trends, and customer demand."

[0614] 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.

[0615] The present invention is a system for improving the efficiency of marketing operations, and includes a user authentication means, a market research data collection means, a market research data filtering means, an analysis means using a generative AI model, a means for integrating analysis results, a means for generating a marketing portfolio, a means for displaying the generated marketing portfolio, a means for proposing engagement measures based on a user profile, and an emotion engine for recognizing user emotions.

[0616] The user enters login information. The user enters their username and password on the system's login screen. The server checks the information against a database to verify the user's authenticity. If the match is successful, the user profile is loaded.

[0617] Next, the user inputs keywords related to market research. The user then inputs search keywords for creating a marketing portfolio into the device. The device collects market data from external data sources (such as APIs) based on the keywords. The server retrieves the collected data and filters it based on specific criteria to extract relevant information.

[0618] The server then uses the generative AI model to perform PEST and 3C analyses on the filtered market data. PEST analysis analyzes political, economic, social, and technological factors, while 3C analysis analyzes company, competitor, and customer factors. This provides a deep understanding of a wide range of factors.

[0619] The server integrates the analysis results and automatically creates a marketing portfolio, which details marketing strategies and action plans. The server then displays the created marketing portfolio on the user's dashboard, where the user can view it.

[0620] Additionally, the server will suggest engagement strategies based on the user profile, including targeting and messaging strategies using user demographics and historical data, resulting in optimal marketing strategies for the user.

[0621] The present invention introduces an emotion engine that analyzes a user's emotional state to further enhance marketing initiatives. Specifically, by analyzing a user's inputs and operations and identifying their emotional state, more personalized recommendations can be made. The emotion engine analyzes the user's text inputs, click patterns, mouse movements, etc. to determine whether the user is excited, confused, or calm. Based on this, engagement initiatives can be appropriately adjusted. For example, if it is determined that the user is dissatisfied, special offers or support information can be presented to alleviate the dissatisfaction.

[0622] For example, if a user types in the keywords "AI" and "marketing," the sentiment engine analyzes the user's reaction to the search results. If the user responds positively to the search results, the server can further increase the user's engagement by suggesting new marketing campaigns or powerful strategies against competitors.

[0623] This invention significantly reduces the work required to create one report, which previously took approximately 50 hours, thereby reducing the burden on marketing portfolio managers. Furthermore, by incorporating an emotion engine, it becomes possible to respond flexibly to user emotions, resulting in higher engagement and more effective marketing strategies.

[0624] The processing flow will be explained below.

[0625] Step 1:

[0626] The user enters login information. The user enters their username and password on the system login screen.

[0627] Step 2:

[0628] The server authenticates the login information. It checks the entered username and password against a database to verify the user's identity. If the match is successful, it loads the user profile.

[0629] Step 3:

[0630] The user inputs market research keywords. The user inputs search keywords for creating a marketing portfolio into the terminal. For example, let's use keywords such as "AI" and "marketing."

[0631] Step 4:

[0632] The device collects market research data. It uses input keywords to retrieve market data from external APIs, which then return relevant articles, reports, and statistics.

[0633] Step 5:

[0634] The server filters the collected market data. The server sorts the acquired market data based on criteria and extracts reliable and up-to-date information.

[0635] Step 6:

[0636] The server performs a PEST analysis, analyzing political, economic, social, and technological factors against market data filtered using a generative AI model. For example, it evaluates government AI policies, economic impact, social acceptance, and technological advances.

[0637] Step 7:

[0638] The server performs the 3C analysis. It also uses a generative AI model to analyze the company, competitors, and customers. For example, it evaluates the company's strengths and weaknesses, competitor trends, customer needs, and market trends.

[0639] Step 8:

[0640] The server integrates the results of the analysis. The server integrates the results of the PEST analysis and the 3C analysis to obtain comprehensive insights. Marketing strategies and action plans are determined based on these insights.

[0641] Step 9:

[0642] The server automatically generates a marketing portfolio based on the integrated analysis results. The portfolio includes specific strategic items, market forecasts, and measures.

[0643] Step 10:

[0644] The server displays the marketing portfolio. The server displays the generated marketing portfolio on the user's dashboard. The user views it and checks the required information.

[0645] Step 11:

[0646] The server starts an emotion engine to analyze the user's reaction. The emotion engine analyzes the user's input, operation, mouse movement, etc. to identify the user's emotional state.

[0647] Step 12:

[0648] The server proposes appropriate engagement strategies based on the user's emotional state and user profile, as recognized by the emotion engine. For example, if the user is dissatisfied, the server may present special offers or support information.

[0649] By going through each step in this way, users can not only create a marketing portfolio efficiently, but also respond flexibly according to their emotional state.

[0650] Example 2

[0651] 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."

[0652] Traditional marketing operations require a great deal of time and effort, and there is a need for greater efficiency, particularly in market research and data analysis. Another issue is the difficulty of proposing personalized engagement measures that take user emotions into account. There is a need for a system that can solve these problems, improve operational efficiency, and realize effective marketing measures.

[0653] 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.

[0654] In this invention, the server includes a user authentication means, a market research data collection means, a market research data filtering means, an analysis means using a generative AI model, a means for integrating analysis results, a means for generating a marketing portfolio, a means for displaying the generated marketing portfolio, a means for proposing engagement measures based on a user profile, a means for analyzing user emotions, and a means for adjusting the engagement measures based on the analyzed emotions. This makes it possible to improve the efficiency of marketing operations and realize flexible marketing measures that correspond to user emotions.

[0655] "User authentication means" is a means used by a user to log in to a system, and is a function that authenticates the user's legitimacy by inputting a user name and password.

[0656] The "market research data collection means" is a function that allows the terminal or server to collect market data from external data sources based on keywords entered by the user.

[0657] The "market research data filtering means" is a function that filters collected market data based on specific criteria and extracts only highly relevant data.

[0658] An "analysis means using a generative artificial intelligence model" is a means for analyzing market data using a generative AI model (e.g., a generative language model) and performing a specific analysis.

[0659] The "means for integrating analysis results" is a function for integrating the analysis results obtained individually and generating a comprehensive report.

[0660] The "means for generating a marketing portfolio" is a function for automatically generating a portfolio including a marketing strategy and an action plan based on the integrated analysis results.

[0661] The "means for displaying the generated marketing portfolio" is a function that displays the generated marketing portfolio on the user's dashboard and allows the user to view or download it.

[0662] "Means to propose engagement measures based on user profiles" is a function that proposes targeting and messaging strategies based on user attribute information and past data.

[0663] The "means for analyzing user emotions" is a function that analyzes the user's inputs and operations and identifies the user's emotional state.

[0664] The "means for adjusting engagement measures based on analyzed emotions" is a function that appropriately adjusts engagement measures and provides appropriate offers and support information based on the analyzed emotional state of the user.

[0665] The present invention provides a system for improving the efficiency of marketing operations and providing flexible engagement measures that correspond to user emotions. Specific embodiments will be described below.

[0666] User Authentication

[0667] The user enters a username and password on the system's login screen. The terminal sends this information to the server, which checks the database to verify the user's authenticity. If authentication is successful, the server loads the user profile and returns a login success message to the user.

[0668] Market research data collection

[0669] The user inputs keywords for market research. The device sends these keywords to the server. The server accesses external data sources (e.g., APIs, databases) and collects market data based on the specified keywords. This data is temporarily stored on the server.

[0670] Filtering Market Research Data

[0671] The server filters the collected data according to certain criteria, which involves extracting reliable data and filtering out less relevant data.

[0672] Data analysis

[0673] The server performs PEST analysis (political, economic, social, technological) and 3C analysis (company, competitors, customers) on the market data collected and filtered using a generative artificial intelligence model (e.g., GPT-4). The prompt is as follows:

[0674] Conduct a PEST analysis and 3C analysis based on the following data: Data: {Collected market data}

[0675] Integration of analysis results

[0676] The server consolidates the results of each analysis and generates a comprehensive report containing insights and conclusions that form the basis of marketing strategies.

[0677] Marketing portfolio generation

[0678] The server automatically generates a marketing portfolio based on the integrated analysis results, which describes a detailed marketing strategy and action plan.

[0679] View the generated marketing portfolio

[0680] The server displays the generated marketing portfolio on the user's dashboard, where the user can view it and download or print it as needed.

[0681] Proposing engagement measures

[0682] The server then proposes engagement strategies based on the user profile, including targeting and messaging strategies, leveraging the user's historical data and demographic information to provide personalized recommendations.

[0683] Emotion analysis

[0684] The server runs an emotion engine based on the user's input and operations to analyze the user's emotional state. For example, it analyzes the user's click patterns and text input to identify the user's emotional state (e.g., excited, frustrated, calm).

[0685] Tailoring engagement strategies based on emotions

[0686] The server can then adjust engagement strategies appropriately based on the analyzed emotional state: for example, if a user expresses dissatisfaction, it can increase engagement by providing special discount offers or additional support information.

[0687] As described above, the present invention makes it possible to improve the efficiency of marketing operations and provide flexible and effective marketing measures that respond to the emotions of users.

[0688] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0689] Step 1:

[0690] User Authentication

[0691] Input: The user enters their username and password on the login screen.

[0692] Specific operation: The terminal sends the entered username and password to the server.

[0693] Data processing: The server checks the received username and password against a database to verify the user's authenticity.

[0694] Output: If authentication is successful, the server loads the user profile and sends a login successful message to the user.

[0695] Step 2:

[0696] Market research data collection

[0697] Input: The user inputs keywords into the terminal to create a marketing portfolio.

[0698] Specific operation: The terminal sends the input keyword to the server.

[0699] Data Processing: The server accesses external data sources (APIs, databases) and collects market data based on the specified keywords.

[0700] Output: The collected market data is temporarily stored on the server.

[0701] Step 3:

[0702] Filtering Market Research Data

[0703] Input: Market data temporarily stored on the server.

[0704] Specific Actions: The server filters the data based on certain criteria (e.g., data reliability, date, relevance).

[0705] Data processing: Filtered market data removes irrelevant data and extracts only the necessary data.

[0706] Output: The filtered market data is passed on to the next analytical step.

[0707] Step 4:

[0708] Data analysis (PEST analysis and 3C analysis)

[0709] Input: Filtered market data.

[0710] Specific operation: The server generates prompt sentences using a generative artificial intelligence model (e.g., GPT-4) and performs analysis.

[0711] example:

[0712] Conduct a PEST and 3C analysis based on the following data: Data: {Filtered Market Data}

[0713] Data calculation: The generative AI model performs PEST and 3C analysis on the input market data and analyzes each element.

[0714] Output: The analysis results are saved on the server.

[0715] Step 5:

[0716] Integration of analysis results

[0717] Input: Results of PEST analysis and 3C analysis.

[0718] Specific operation: The server integrates the results of each analysis.

[0719] Data Processing: Comprehensive reports are generated from the integrated analysis results.

[0720] Output: A comprehensive report is generated and passed to the next step, Marketing Portfolio Generation.

[0721] Step 6:

[0722] Marketing portfolio generation

[0723] Input: Consolidated analysis results.

[0724] Specific operation: The server automatically generates a marketing portfolio based on the comprehensive report.

[0725] Data Processing: A detailed portfolio is generated, including marketing strategies and action plans.

[0726] Output: The generated marketing portfolio is saved on the server.

[0727] Step 7:

[0728] View the generated marketing portfolio

[0729] Input: The generated marketing portfolio.

[0730] Specific operation: The server displays the generated portfolio on the user's dashboard.

[0731] Output: Users can view the portfolio and download or print it as needed.

[0732] Step 8:

[0733] Proposing engagement measures

[0734] Input: User profile and historical data.

[0735] Specific operation: The server proposes optimal engagement measures based on the user profile.

[0736] Data processing: Analyze historical data and attribute information to develop targeting and messaging strategies.

[0737] Output: The user is notified of the suggested engagement measures.

[0738] Step 9:

[0739] Emotion analysis

[0740] Input: User input and operation data (click patterns, text input, etc.).

[0741] Specific operation: The server uses the emotion engine to analyze the user's emotional state in real time.

[0742] Data calculation: Analyzes user operation data and identifies emotional states such as excitement, frustration, and calmness.

[0743] Output: The analyzed emotional state is used to adjust the next policy.

[0744] Step 10:

[0745] Tailoring engagement strategies based on emotions

[0746] Input: Parsed emotional state.

[0747] Specific behavior: The server adjusts engagement strategies based on emotional state.

[0748] Data processing: Special offers and support information are generated according to the user's emotional state.

[0749] Output: The tailored engagement measures are provided to the user.

[0750] (Application example 2)

[0751] 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."

[0752] Conventional marketing systems mainly respond only to users' online actions and inputs, and lack mechanisms for utilizing customer behavior data in physical stores. This makes it difficult to immediately provide appropriate marketing measures based on customers' emotions and behavior in physical stores. Furthermore, the lack of real-time personalized information notifications leads to a decline in customer engagement. The present invention aims to solve these issues by analyzing customer behavior data and emotional states in physical stores in real time and providing personalized measures based on the results.

[0753] 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.

[0754] In this invention, the server includes means for analyzing the emotional state of a user, means for adjusting marketing measures based on the analyzed emotional state, means for collecting customer behavior data in a physical store and analyzing the emotional state in real time, and means for notifying personalized information in real time according to the customer's movements and behavior in the physical store. This makes it possible to instantly provide appropriate marketing measures based on the customer's behavior and emotions in the physical store, and is expected to improve customer engagement.

[0755] "User authentication" refers to the method or process by which a user verifies their identity and gains access to a system.

[0756] "Market research data collection tools" are methods and techniques for collecting information about market trends and consumer preferences.

[0757] "Market research data filtering means" refers to methods or techniques for selecting reliable information from collected market data based on specific criteria.

[0758] "Analytical methods using generative artificial intelligence models" are methods or techniques that use generative AI models to analyze data and derive insights or conclusions.

[0759] "Means for synthesizing analytical results" are methods or techniques for bringing together the results of separate data analyses and producing a single, unified conclusion or report.

[0760] "Means for generating a marketing portfolio" refers to methods and techniques for creating specific marketing strategies and action plans based on the results of analysis.

[0761] The "means for displaying the generated marketing portfolio" refers to a method or technology that allows a user to view the generated marketing portfolio.

[0762] "Means for proposing engagement measures based on user profiles" refers to methods and technologies for proposing appropriate marketing measures and messages based on user attribute information and past behavioral data.

[0763] A "means for analyzing a user's emotional state" is a method or technique for monitoring a user's actions or inputs and identifying the user's emotional state therefrom.

[0764] "Means for adjusting marketing strategies based on the analyzed emotional state" refers to methods and technologies for changing and adapting marketing strategies and messages according to the emotional state of the user.

[0765] "Means for collecting customer behavioral data in a physical store and analyzing their emotional state in real time" refers to methods and technologies for instantly obtaining customer behavioral data in a physical store and analyzing the emotional state of customers based on that data.

[0766] "Means of notifying customers of personalized information in real time according to their movements and actions within a physical store" refers to methods and technologies that instantly notify customers of individually optimized information and offers when they move to a specific area or take a specific action within a physical store.

[0767] The present invention is a system for improving the efficiency of marketing operations, and includes the following means.

[0768] User authentication method

[0769] A user launches an application and hits a login screen, where they enter their username and password, and the server validates the user by checking that information against a database, and then loads the user profile accordingly.

[0770] Market research data collection methods

[0771] The user inputs search keywords for creating a marketing portfolio into the device. The device collects market data from external data sources (such as APIs) based on the keywords. The server retrieves and stores the collected data.

[0772] Market research data filtering tools

[0773] The server filters the collected market data based on specific criteria to extract reliable information, thereby retaining the most relevant data.

[0774] Analytical tools using generative artificial intelligence models

[0775] The server performs PEST analysis (analysis of political, economic, social, and technological factors) and 3C analysis (analysis of company, competitor, and customer factors) on the market data filtered using the generative AI model.

[0776] A means of synthesizing analytical results

[0777] The analysis results are integrated by the server, and a marketing portfolio is automatically created. The integrated data details marketing strategies and action plans.

[0778] A means to create and display marketing portfolios

[0779] The created marketing portfolio is displayed on the user's dashboard and can be viewed by the user.

[0780] A means of proposing engagement measures based on user profiles

[0781] The server recommends engagement strategies based on the user profile, including targeting and messaging strategies using user demographics and historical data.

[0782] A means of analyzing the user's emotional state

[0783] The server uses an emotion engine that analyzes user input and actions to identify their emotional state. This is achieved by analyzing the user's text input, click patterns, mouse movements, etc.

[0784] A means of adjusting marketing efforts based on analyzed emotional states

[0785] After the emotion engine identifies the user's emotional state, the server can use this information to adjust marketing efforts appropriately. For example, if it determines that the user is excited, it will display information about new campaigns. However, if it determines that the user is confused, it will prioritize displaying help and support information.

[0786] In-store operation

[0787] Working in a physical store, the application collects real-time customer behavior data (e.g., product scanning, feedback input, in-app chat), which is used by an emotion engine to analyze the customer's emotional state.

[0788] Real-time notifications of new information

[0789] When a customer moves to a specific area or takes a specific action in a physical store, the system will send them personalized information in real time. For example, when a customer scans a specific product, they will instantly receive push notifications about related campaigns and special offers.

[0790] Hardware and software used

[0791] The hardware used to implement this invention includes a smartphone (compatible with iOS / Android), a server (cloud server), and a database (MySQL, MongoDB). The software used includes a smartphone app development framework (React Native), a generative AI model, and a sentiment analysis engine.

[0792] Examples of prompt sentences

[0793] The following prompts are used:

[0794] "We will explain how to analyze customer behavior data to make personalized product suggestions to increase customer engagement in physical stores. When a customer scans a product, the data is sent to a server in real time and analyzed by a sentiment analysis engine. If the customer responds positively, a generative AI model is used to generate targeted product and campaign information, which is then displayed on the app dashboard."

[0795] As described above, the present invention aims to improve customer engagement by analyzing customer behavioral data and emotional states in physical stores in real time and providing personalized measures based on that data.

[0796] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0797] Step 1:

[0798] User Authentication

[0799] A user launches an application and enters their username and password at the login screen. The server receives this input and checks it against the authentication information in its database.

[0800] Input: Username, Password

[0801] Processing: Database Matching

[0802] Output: Authentication result, user profile

[0803] Step 2:

[0804] Market research data collection

[0805] The user inputs search keywords for creating a marketing portfolio into the device, which then collects market data via external data sources (such as APIs) based on the keywords and sends it to the server.

[0806] Input: Search keyword

[0807] Processing: Data collection from external data sources

[0808] Output: Market data

[0809] Step 3:

[0810] Market Research Data Filtering

[0811] The server filters the collected market data based on specific criteria to extract reliable information.

[0812] Input: Market Data

[0813] Processing: Data Filtering

[0814] Output: Filtered market data

[0815] Step 4:

[0816] Data analysis using generative artificial intelligence models

[0817] The server uses generative AI models to perform PEST and 3C analysis on the filtered market data.

[0818] Input: Filtered market data

[0819] Processing: PEST analysis, 3C analysis

[0820] Output: Analysis results

[0821] Step 5:

[0822] Integration of analysis results

[0823] The server integrates the generated analysis results and automatically generates a marketing portfolio.

[0824] Input: Analysis results

[0825] Processing: Data Integration

[0826] Output: Marketing portfolio

[0827] Step 6:

[0828] View Marketing Portfolio

[0829] The generated marketing portfolio is displayed on the user's dashboard, where the user can view it.

[0830] Enter: Marketing Portfolio

[0831] Processing: Data display

[0832] Output: Marketing portfolio on a dashboard

[0833] Step 7:

[0834] Proposing engagement measures

[0835] The server proposes engagement measures based on the user profile.

[0836] Input: User profile, marketing portfolio

[0837] Processing: Targeting, Messaging

[0838] Output: Proposed engagement measures

[0839] Step 8:

[0840] Analyzing the user's emotional state

[0841] The server analyzes emotions from the user's inputs and actions (e.g., text input, click patterns, mouse movements) to identify their emotional state.

[0842] Input: User input and operation data

[0843] Processing: Sentiment Analysis

[0844] Output: User's emotional state

[0845] Step 9:

[0846] Adjusting policies based on analyzed emotional states

[0847] Based on the analysis results of the emotion engine, the server dynamically adjusts marketing measures.

[0848] Input: User's emotional state

[0849] Action: Adjustment of measures

[0850] Output: Coordinated marketing efforts

[0851] Step 10:

[0852] Collecting customer behavior data in physical stores and analyzing it in real time

[0853] Customer behavioral data (e.g., product scanning, feedback input) is collected in real time within physical stores and their emotional state is analyzed.

[0854] Input: Customer behavior data

[0855] Processing: Real-time data collection, sentiment analysis

[0856] Output: Customer emotional state and behavior data

[0857] Step 11:

[0858] Real-time personalized notifications

[0859] The server then sends personalized information in real time based on customer behavior, such as pushing relevant information when scanning a specific product or entering a specific area.

[0860] Input: Customer behavior data, emotional state

[0861] Processing: Information generation, notification

[0862] Output: Personalized push notification

[0863] Through these steps, the system is able to instantly provide appropriate marketing measures based on customer behavior and emotions in physical stores.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] [Third embodiment]

[0868] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0869] 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.

[0870] 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).

[0871] 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.

[0872] 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.

[0873] 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).

[0874] 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.

[0875] 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.

[0876] 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.

[0877] 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.

[0878] 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.

[0879] 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."

[0880] The present invention is a system for streamlining marketing operations, and includes a user authentication means, a market research data collection means, a market research data filtering means, an analysis means using a generative AI model, a means for integrating analysis results, a means for generating a marketing portfolio, a means for displaying the generated marketing portfolio, and a means for proposing engagement measures based on a user profile.

[0881] When a user enters their login information, the server checks the information against a database to verify the user's authenticity. Specifically, the server queries the database for the username and password, and if successful, loads the user profile.

[0882] Next, the user enters keywords related to market research, and the device collects market data from external data sources (such as APIs) based on the keywords. The server retrieves the collected data and filters it based on specific criteria to extract the most relevant information.

[0883] The server then uses the generative AI model to perform PEST and 3C analyses on the filtered market data. PEST analysis analyzes political, economic, social, and technological factors, while 3C analysis analyzes company, competitor, and customer factors. This provides a deep understanding of a wide range of factors.

[0884] The server integrates the analysis results and automatically creates a marketing portfolio, which details marketing strategies and action plans. The server then displays the created marketing portfolio on the user's dashboard, where the user can view it.

[0885] Additionally, the server will suggest engagement strategies based on the user profile, including targeting and messaging strategies using user demographics and historical data, resulting in optimal marketing strategies for the user.

[0886] For example, when a user enters keywords such as "AI" and "marketing," data on the AI ​​market and marketing trends is collected from external market data sources. The server filters this data and performs PEST and 3C analyses to obtain insights into the current and forecast state of the AI ​​market, competitor trends, and target customer demand. Finally, a marketing portfolio is automatically generated based on this information, and the user is presented with the next step in marketing strategies.

[0887] This invention significantly reduces the work that previously took about 50 hours per report, thereby reducing the burden on marketing portfolio staff.

[0888] The processing flow will be explained below.

[0889] Step 1:

[0890] The user enters login information. The user enters their username and password on the system login screen.

[0891] Step 2:

[0892] The server authenticates the login information. It checks the entered username and password against a database to verify the user's identity. If the match is successful, the user profile is loaded.

[0893] Step 3:

[0894] The user inputs market research keywords. The user inputs search keywords for creating a marketing portfolio into the terminal.

[0895] Step 4:

[0896] The terminal collects market research data. The terminal sends keywords to an external data source (e.g., API) to obtain relevant market data.

[0897] Step 5:

[0898] The server filters the collected market data. The server filters the acquired market data based on specific criteria (e.g., the latest, most reliable information) to extract the most relevant information.

[0899] Step 6:

[0900] The server performs the PEST analysis, using a generative AI model to analyze political, economic, social, and technological factors against the filtered market data.

[0901] Step 7:

[0902] The server then performs the 3C analysis, also using a generative AI model to analyze the filtered market data for the company, competitors, and customers.

[0903] Step 8:

[0904] The server integrates the results of the analysis. The server integrates the results of the PEST analysis and the 3C analysis and automatically generates a marketing portfolio that includes strategic items and action plans.

[0905] Step 9:

[0906] The server displays the marketing portfolio. The server displays the generated marketing portfolio on the user's dashboard, where the user can view it.

[0907] Step 10:

[0908] The server proposes engagement strategies: The server proposes appropriate engagement strategies (targeting, messaging, etc.) based on the user profile.

[0909] By going through each step in this way, the user can efficiently create a marketing portfolio.

[0910] Example 1

[0911] 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."

[0912] Traditional marketing operations require a significant amount of time and effort to conduct market research, filter and analyze the data, and then integrate the results to create a marketing strategy. Furthermore, because much of this work is done manually, there is a high risk of human error and efficiency is low. Another issue is that it is difficult to appropriately propose engagement measures based on user profiles.

[0913] 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.

[0914] In this invention, the server includes a user authentication means, a market research data acquisition means, a market research data selection means, an analysis means using a generative artificial intelligence model, a means for integrating the analysis results, a means for generating a marketing strategy document, a means for displaying the generated marketing strategy document, and a means for proposing an engagement strategy based on a user profile. This makes marketing operations more efficient, significantly reduces time and labor, reduces human error, and enables the proposal of appropriate engagement measures.

[0915] "User authentication means" is a means for verifying the validity of a user by querying a database using authentication information (user name, password, etc.) entered by the user.

[0916] The "market research data acquisition means" is a means for collecting market research data from an external data source (such as an API) based on keywords specified by the user.

[0917] The "market research data selection means" is a means for filtering collected market research data based on specific criteria and extracting only highly relevant information.

[0918] An "analysis means using a generative artificial intelligence model" is a means for analyzing market research data that has been filtered using generative artificial intelligence (e.g., GPT-3).

[0919] The "means for integrating the analysis results" refers to a means for compiling the analysis results obtained by the generative artificial intelligence model into a single integrated data set.

[0920] The "means for generating a marketing strategy document" is a means for automatically creating a document detailing a marketing strategy and an action plan based on the integrated analysis results.

[0921] The "means for displaying the generated marketing strategy document" refers to a means for displaying the generated marketing strategy document on the dashboard of the user's operating terminal.

[0922] "Means for proposing engagement strategies based on user profiles" refers to means for proposing optimal targeting and messaging strategies based on user attribute information and past data.

[0923] The present invention is a system for improving the efficiency of marketing operations, and includes a user authentication means, a market research data acquisition means, a market research data selection means, an analysis means using a generative artificial intelligence model, a means for integrating the analysis results, a means for generating a marketing strategy document, a means for displaying the generated marketing strategy document, and a means for proposing an engagement strategy based on a user profile.

[0924] First, the user enters their login information (username and password) at the terminal, which then sends this information to the server, which checks the MySQL database to verify the user's authenticity. If the match is successful, the server loads the user profile.

[0925] Next, the user enters keywords related to market research (e.g., "AI" or "marketing"), and the device sends the keywords to the server, which then uses Python's requests library to retrieve market research data from external data sources (e.g., the Google Trends API).

[0926] The acquired data is processed by the server using the Pandas library and filtered based on specific criteria (e.g., time period or relevance).The server then inputs the filtered data into a generative artificial intelligence model (e.g., GPT-3) to perform PEST (political, economic, social, technological) and 3C (company, competitor, customer) analysis.

[0927] Example prompt sentence:

[0928] "Conduct a PEST analysis of the AI ​​and Marketing industry based on the following data: [filtered data]"

[0929] The analysis results obtained from the generative AI model are integrated by the server to automatically create a marketing strategy document, which is generated using the Jinja2 template engine and displayed on the dashboard of the user's device.

[0930] Additionally, the server suggests optimal engagement strategies (e.g., targeting and messaging strategies) based on the user profile, using machine learning models such as Scikit-learn to make predictions based on past user activity and demographic information.

[0931] As a specific example, if a user enters the keywords "AI" and "marketing," the server uses the Google Trends API to collect data on the AI ​​market and marketing trends. The collected data is filtered, and PEST and 3C analyses are performed using GPT-3. The results reveal the current state and forecast of the AI ​​market, the trends of competitors, and the needs of target customers. Based on this information, a marketing strategy document is generated and presented to the user. Optimal engagement measures are also proposed based on the user profile.

[0932] This streamlines marketing operations, significantly reducing the time and errors involved compared to traditional manual analysis.

[0933] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0934] Step 1:

[0935] The user enters a username and password on the login page. The device sends the information to the server, which then checks the received login information against a MySQL database to verify the user's authenticity. If the verification is successful, the server loads the user profile information and starts the session.

[0936] Input: Username, Password

[0937] Output: Authentication successful (session started) or authentication failed (error message)

[0938] Step 2:

[0939] The user enters keywords related to market research (e.g., "AI" or "marketing"). The device sends the keywords to the server. The server receives the keywords and proceeds to the next processing step.

[0940] Input: keyword

[0941] Output: The keyword is sent to the server

[0942] Step 3:

[0943] The server uses the provided keywords to collect market research data from external data sources (e.g., Google Trends API), using the Python requests library.

[0944] Input: keyword

[0945] Output: Market research data (obtained from external data sources)

[0946] Specific operation: The server sends a request to the Google Trends API for the keywords "AI" and "marketing" to obtain related market data.

[0947] Step 4:

[0948] The server processes the acquired market data using the Pandas library and filters it based on specified criteria (e.g., time range or correlation), eliminating unnecessary data and extracting only the most relevant information.

[0949] Input: Market research data

[0950] Output: Filtered market research data

[0951] Specific operation: The server keeps AI marketing-related data from the past year and removes old or irrelevant data from before that.

[0952] Step 5:

[0953] The server inputs the filtered data into a generative artificial intelligence model (e.g., GPT-3) to perform PEST analysis (political, economic, social, technological) and 3C analysis (company, competitors, customers). The server generates prompt statements, passes them to the AI ​​model, and receives responses from the AI.

[0954] Input: Filtered market research data

[0955] Output: PEST and 3C analysis results

[0956] Specific behavior: The server generates the following prompt:

[0957] "Conduct a PEST analysis of the AI ​​and Marketing industry based on the following data: [filtered data]"

[0958] Receive responses from the generative AI model and obtain analytical results.

[0959] Step 6:

[0960] The server uses Pandas to integrate the results of the PEST and 3C analyses obtained from the generative artificial intelligence model and compile them into a single dataset.

[0961] Input: PEST and 3C analysis results

[0962] Output: Marketing insights to be integrated

[0963] Specific operation: The server compiles data on "Politics," "Economy," "Society," "Technology," as well as "Company," "Competitor," and "Customer" into a single data frame.

[0964] Step 7:

[0965] The server uses the Jinja2 template engine to generate a marketing strategy document from the consolidated data, which details the marketing strategy and action plan.

[0966] Input: Integrated data

[0967] Output: Marketing strategy document

[0968] What it does: The server uses Jinja2 to automatically generate a detailed portfolio of "Current Status and Strategies of the AI ​​and Marketing Industry."

[0969] Step 8:

[0970] The server displays the generated marketing strategy document on the user's dashboard, and the terminal updates the dashboard to display the new document.

[0971] Input: Marketing Strategy Document

[0972] Output: Document displayed on the user's dashboard

[0973] What it does: The user's dashboard displays the "latest marketing portfolio on AI and marketing."

[0974] Step 9:

[0975] The server analyzes the data to suggest engagement strategies based on the user profile, using Scikit-learn to make predictions based on past user activity and attribute information.

[0976] Input: User profile information, history data

[0977] Output: The optimal targeting and messaging strategy suggested to the user.

[0978] Specific operation: The server will suggest new related strategies to users who have frequently viewed "AI marketing" related initiatives in the past.

[0979] (Application example 1)

[0980] 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."

[0981] Traditional marketing operations have the problem of requiring a great deal of time and effort to collect and analyze market research data and then propose advertising strategies based on that data. Furthermore, the lack of real-time capabilities makes it difficult to implement prompt and appropriate marketing measures. Another problem is the difficulty of providing optimal engagement measures based on user profiles.

[0982] 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.

[0983] In this invention, the server includes a user authentication means, a market research data collection means, a market research data filtering means, an analysis means using a generative AI model, a means for integrating analysis results, a means for generating a marketing portfolio, a means for displaying the generated marketing portfolio, a means for proposing engagement measures based on a user profile, and a means for collecting real-time market research data and proposing advertising strategies using that information. This enables significant efficiency improvements in marketing operations, utilization of real-time market research data, and the rapid provision of optimal engagement measures based on user profiles.

[0984] The "user authentication means" is a means for verifying the validity of a user based on the user's login information.

[0985] "Market research data collection means" refers to means for collecting data necessary for market research from external data sources.

[0986] A "market research data filtering means" is a means for extracting reliable data from collected data based on specific criteria.

[0987] An "analytical means using a generative artificial intelligence model" is a means for analyzing collected and filtered market data using a generative AI model.

[0988] "Means for integrating analytical results" refers to means for integrating analytical results obtained by generative AI models to generate a single comprehensive analytical result.

[0989] The "means for generating a marketing portfolio" is a means for creating a portfolio including a marketing strategy and an action plan based on the integrated analysis results.

[0990] The "means for displaying the generated marketing portfolio" is a means for displaying the generated marketing portfolio in a format that can be viewed by a user.

[0991] "Means for proposing engagement measures based on user profiles" refers to means for proposing targeting and messaging strategies based on user attribute information and past data.

[0992] "Means for collecting real-time market research data and proposing advertising strategies using that information" refers to means for collecting market research data in real time from external data sources and using that data to propose appropriate advertising strategies.

[0993] The present invention is a system for improving the efficiency of marketing operations, and includes a user authentication means, a market research data collection means, a market research data filtering means, an analysis means using a generative artificial intelligence model, a means for integrating analysis results, a means for generating a marketing portfolio, a means for displaying the generated marketing portfolio, a means for proposing engagement measures based on a user profile, and a means for collecting real-time market research data and proposing advertising strategies using that information.

[0994] This system performs the following processes:

[0995] First, a user logs into the smartphone application. The login process includes verifying the validity of the username and password using Firebase Authentication. If authentication is successful, the user's profile is loaded.

[0996] Next, the user enters keywords related to a specific market or product. Based on the keywords entered, the device collects market research data in real time from the Google Trends API and other similar market data sources. This market research data collection method gathers a wide range of data related to the keywords entered by the user.

[0997] The collected market data is filtered by the terminal using Python. The market research data filtering means extracts reliable information from the collected data based on specific criteria. The filtered data is refined to include only highly accurate information.

[0998] The server then analyzes the filtered data using generative AI models, such as Hugging Face's Transformer model, to derive deep insights from the data based on political, economic, social, and technological factors, as well as company, competitor, and customer factors.

[0999] The obtained analysis results are integrated by the server. A marketing portfolio is generated from the integrated analysis results. A marketing portfolio is a document that details a strategy and action plan.

[1000] The generated marketing portfolio is automatically displayed on the user's dashboard from the server, providing detailed information on the current and forecast market situation, competitor trends, and target customer demand.

[1001] Additionally, the server proposes engagement strategies based on the user profile. The engagement strategy proposal means uses the user's demographic information and historical data to generate individually customized targeting and messaging strategies.

[1002] As a specific example, when a user enters the keyword "AI marketing," the following steps are executed. First, the user is authenticated using Firebase Authentication. Next, trend data related to "AI marketing" is collected from the Google Trends API. Next, the data is filtered using Python to extract the most relevant data. After that, PEST and 3C analyses are performed using Hugging Face's Transformer model, and a marketing portfolio is generated from the integrated analysis results. Finally, the app suggests optimal advertising measures to the user based on the generated portfolio.

[1003] An example of a prompt to be input into a generative AI model is, "Collect and analyze market data related to AI marketing and propose insights based on the following indicators: political influence, economic influence, social influence, technological influence, company situation, competitive trends, and customer demand."

[1004] In this way, the system of the present invention significantly improves the efficiency of marketing operations, enables the utilization of market research data in real time, and can quickly provide optimal engagement measures based on user profiles.

[1005] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1006] Step 1:

[1007] A user launches a smartphone application and enters their login information (username and password). The entered login information is verified on the server side using Firebase Authentication. The server then queries the database and loads the correct user profile. This authenticates the user and allows them to access their individual data.

[1008] Input: Username, Password

[1009] Output: User profile loaded

[1010] Step 2:

[1011] The user enters keywords related to a specific market or product into the application's input fields. The keywords are sent to the server, which collects real-time market data from Google Trends API and other market data sources. The terminal sends an API request to collect the data and receives the results.

[1012] Input:keyword

[1013] Output: Market data collection

[1014] Step 3:

[1015] The server receives the collected market data and filters it using a Python script. The market research data filtering method extracts reliable information based on specific criteria. The filtered data contains only highly accurate information and then moves on to the next step.

[1016] Input: Collected market data

[1017] Output: Filtered market data

[1018] Step 4:

[1019] The server analyzes the filtered data using generative AI models (e.g., the Hugging Face Transformer model), and performs PEST and 3C analyses using prompt statements to gain insights into various factors (political, economic, social, technological, company, competitors, and customers).

[1020] Input: Filtered market data

[1021] Output: Analysis results (PEST analysis and 3C analysis insights)

[1022] Step 5:

[1023] The server then integrates the resulting analysis results and automatically generates a marketing portfolio, which details a marketing strategy and action plan based on a wide range of factors.

[1024] Input: Analysis results

[1025] Output: Marketing portfolio

[1026] Step 6:

[1027] The generated marketing portfolio is displayed on the user's dashboard by the server, allowing the user to view the portfolio within the application and check the current state and forecast of the specific market.

[1028] Enter: Marketing Portfolio

[1029] Output: Marketing portfolio displayed in a dashboard

[1030] Step 7:

[1031] The server then proposes engagement strategies based on the user profile. Taking into account the user's demographic information and past data, a personalized targeting and messaging strategy is automatically generated and presented to the user.

[1032] Input: User profile, historical data

[1033] Output: Proposal of engagement measures

[1034] As a concrete example, when a user enters "AI marketing" as a keyword, the following process is executed: After user authentication using Firebase Authentication, trend data related to "AI marketing" is collected from the Google Trends API. The data is filtered using Python, and PEST and 3C analysis is performed using the Hugging Face Transformer model. The obtained insights are integrated to generate a marketing portfolio and display it on a dashboard. Finally, optimal engagement measures are proposed based on the user profile.

[1035] Example prompt: "Collect and analyze market data on AI marketing and propose insights based on the following metrics: political influence, economic influence, social influence, technological influence, company situation, competitive trends, and customer demand."

[1036] 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.

[1037] The present invention is a system for improving the efficiency of marketing operations, and includes a user authentication means, a market research data collection means, a market research data filtering means, an analysis means using a generative AI model, a means for integrating analysis results, a means for generating a marketing portfolio, a means for displaying the generated marketing portfolio, a means for proposing engagement measures based on a user profile, and an emotion engine for recognizing user emotions.

[1038] The user enters login information. The user enters their username and password on the system's login screen. The server checks the information against a database to verify the user's authenticity. If the match is successful, the user profile is loaded.

[1039] Next, the user inputs keywords related to market research. The user then inputs search keywords for creating a marketing portfolio into the device. The device collects market data from external data sources (such as APIs) based on the keywords. The server retrieves the collected data and filters it based on specific criteria to extract relevant information.

[1040] The server then uses the generative AI model to perform PEST and 3C analyses on the filtered market data. PEST analysis analyzes political, economic, social, and technological factors, while 3C analysis analyzes company, competitor, and customer factors. This provides a deep understanding of a wide range of factors.

[1041] The server integrates the analysis results and automatically creates a marketing portfolio, which details marketing strategies and action plans. The server then displays the created marketing portfolio on the user's dashboard, where the user can view it.

[1042] Additionally, the server will suggest engagement strategies based on the user profile, including targeting and messaging strategies using user demographics and historical data, resulting in optimal marketing strategies for the user.

[1043] The present invention introduces an emotion engine that analyzes a user's emotional state to further enhance marketing initiatives. Specifically, by analyzing a user's inputs and operations and identifying their emotional state, more personalized recommendations can be made. The emotion engine analyzes the user's text inputs, click patterns, mouse movements, etc. to determine whether the user is excited, confused, or calm. Based on this, engagement initiatives can be appropriately adjusted. For example, if it is determined that the user is dissatisfied, special offers or support information can be presented to alleviate the dissatisfaction.

[1044] For example, if a user types in the keywords "AI" and "marketing," the sentiment engine analyzes the user's reaction to the search results. If the user responds positively to the search results, the server can further increase the user's engagement by suggesting new marketing campaigns or powerful strategies against competitors.

[1045] This invention significantly reduces the work required to create one report, which previously took approximately 50 hours, thereby reducing the burden on marketing portfolio managers. Furthermore, by incorporating an emotion engine, it becomes possible to respond flexibly to user emotions, resulting in higher engagement and more effective marketing strategies.

[1046] The processing flow will be explained below.

[1047] Step 1:

[1048] The user enters login information. The user enters their username and password on the system login screen.

[1049] Step 2:

[1050] The server authenticates the login information. It checks the entered username and password against a database to verify the user's identity. If the match is successful, it loads the user profile.

[1051] Step 3:

[1052] The user inputs market research keywords. The user inputs search keywords for creating a marketing portfolio into the terminal. For example, let's use keywords such as "AI" and "marketing."

[1053] Step 4:

[1054] The device collects market research data. It uses input keywords to retrieve market data from external APIs, which then return relevant articles, reports, and statistics.

[1055] Step 5:

[1056] The server filters the collected market data. The server sorts the acquired market data based on criteria and extracts reliable and up-to-date information.

[1057] Step 6:

[1058] The server performs a PEST analysis, analyzing political, economic, social, and technological factors against market data filtered using a generative AI model. For example, it evaluates government AI policies, economic impact, social acceptance, and technological advances.

[1059] Step 7:

[1060] The server performs the 3C analysis. It also uses a generative AI model to analyze the company, competitors, and customers. For example, it evaluates the company's strengths and weaknesses, competitor trends, customer needs, and market trends.

[1061] Step 8:

[1062] The server integrates the results of the analysis. The server integrates the results of the PEST analysis and the 3C analysis to obtain comprehensive insights. Marketing strategies and action plans are determined based on these insights.

[1063] Step 9:

[1064] The server automatically generates a marketing portfolio based on the integrated analysis results. The portfolio includes specific strategic items, market forecasts, and measures.

[1065] Step 10:

[1066] The server displays the marketing portfolio. The server displays the generated marketing portfolio on the user's dashboard. The user views it and checks the required information.

[1067] Step 11:

[1068] The server starts an emotion engine to analyze the user's reaction. The emotion engine analyzes the user's input, operation, mouse movement, etc. to identify the user's emotional state.

[1069] Step 12:

[1070] The server proposes appropriate engagement strategies based on the user's emotional state and user profile, as recognized by the emotion engine. For example, if the user is dissatisfied, the server may present special offers or support information.

[1071] By going through each step in this way, users can not only create a marketing portfolio efficiently, but also respond flexibly according to their emotional state.

[1072] Example 2

[1073] 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."

[1074] Traditional marketing operations require a great deal of time and effort, and there is a need for greater efficiency, particularly in market research and data analysis. Another issue is the difficulty of proposing personalized engagement measures that take user emotions into account. There is a need for a system that can solve these problems, improve operational efficiency, and realize effective marketing measures.

[1075] 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.

[1076] In this invention, the server includes a user authentication means, a market research data collection means, a market research data filtering means, an analysis means using a generative AI model, a means for integrating analysis results, a means for generating a marketing portfolio, a means for displaying the generated marketing portfolio, a means for proposing engagement measures based on a user profile, a means for analyzing user emotions, and a means for adjusting the engagement measures based on the analyzed emotions. This makes it possible to improve the efficiency of marketing operations and realize flexible marketing measures that correspond to user emotions.

[1077] "User authentication means" is a means used by a user to log in to a system, and is a function that authenticates the user's legitimacy by inputting a user name and password.

[1078] The "market research data collection means" is a function that allows the terminal or server to collect market data from external data sources based on keywords entered by the user.

[1079] The "market research data filtering means" is a function that filters collected market data based on specific criteria and extracts only highly relevant data.

[1080] An "analysis means using a generative artificial intelligence model" is a means for analyzing market data using a generative AI model (e.g., a generative language model) and performing a specific analysis.

[1081] The "means for integrating analysis results" is a function for integrating the analysis results obtained individually and generating a comprehensive report.

[1082] The "means for generating a marketing portfolio" is a function for automatically generating a portfolio including a marketing strategy and an action plan based on the integrated analysis results.

[1083] The "means for displaying the generated marketing portfolio" is a function that displays the generated marketing portfolio on the user's dashboard and allows the user to view or download it.

[1084] "Means to propose engagement measures based on user profiles" is a function that proposes targeting and messaging strategies based on user attribute information and past data.

[1085] The "means for analyzing user emotions" is a function that analyzes the user's inputs and operations and identifies the user's emotional state.

[1086] The "means for adjusting engagement measures based on analyzed emotions" is a function that appropriately adjusts engagement measures and provides appropriate offers and support information based on the analyzed emotional state of the user.

[1087] The present invention provides a system for improving the efficiency of marketing operations and providing flexible engagement measures that correspond to user emotions. Specific embodiments will be described below.

[1088] User Authentication

[1089] The user enters a username and password on the system's login screen. The terminal sends this information to the server, which checks the database to verify the user's authenticity. If authentication is successful, the server loads the user profile and returns a login success message to the user.

[1090] Market research data collection

[1091] The user inputs keywords for market research. The device sends these keywords to the server. The server accesses external data sources (e.g., APIs, databases) and collects market data based on the specified keywords. This data is temporarily stored on the server.

[1092] Filtering Market Research Data

[1093] The server filters the collected data according to certain criteria, which involves extracting reliable data and filtering out less relevant data.

[1094] Data analysis

[1095] The server performs PEST analysis (political, economic, social, technological) and 3C analysis (company, competitors, customers) on the market data collected and filtered using a generative artificial intelligence model (e.g., GPT-4). The prompt is as follows:

[1096] Conduct a PEST analysis and 3C analysis based on the following data: Data: {Collected market data}

[1097] Integration of analysis results

[1098] The server consolidates the results of each analysis and generates a comprehensive report containing insights and conclusions that form the basis of marketing strategies.

[1099] Marketing portfolio generation

[1100] The server automatically generates a marketing portfolio based on the integrated analysis results, which describes a detailed marketing strategy and action plan.

[1101] View the generated marketing portfolio

[1102] The server displays the generated marketing portfolio on the user's dashboard, where the user can view it and download or print it as needed.

[1103] Proposing engagement measures

[1104] The server then proposes engagement strategies based on the user profile, including targeting and messaging strategies, leveraging the user's historical data and demographic information to provide personalized recommendations.

[1105] Emotion analysis

[1106] The server runs an emotion engine based on the user's input and operations to analyze the user's emotional state. For example, it analyzes the user's click patterns and text input to identify the user's emotional state (e.g., excited, frustrated, calm).

[1107] Tailoring engagement strategies based on emotions

[1108] The server can then adjust engagement strategies appropriately based on the analyzed emotional state: for example, if a user expresses dissatisfaction, it can increase engagement by providing special discount offers or additional support information.

[1109] As described above, the present invention makes it possible to improve the efficiency of marketing operations and provide flexible and effective marketing measures that respond to the emotions of users.

[1110] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1111] Step 1:

[1112] User Authentication

[1113] Input: The user enters their username and password on the login screen.

[1114] Specific operation: The terminal sends the entered username and password to the server.

[1115] Data processing: The server checks the received username and password against a database to verify the user's authenticity.

[1116] Output: If authentication is successful, the server loads the user profile and sends a login successful message to the user.

[1117] Step 2:

[1118] Market research data collection

[1119] Input: The user inputs keywords into the terminal to create a marketing portfolio.

[1120] Specific operation: The terminal sends the input keyword to the server.

[1121] Data Processing: The server accesses external data sources (APIs, databases) and collects market data based on the specified keywords.

[1122] Output: The collected market data is temporarily stored on the server.

[1123] Step 3:

[1124] Filtering Market Research Data

[1125] Input: Market data temporarily stored on the server.

[1126] Specific Actions: The server filters the data based on certain criteria (e.g., data reliability, date, relevance).

[1127] Data processing: Filtered market data removes irrelevant data and extracts only the necessary data.

[1128] Output: The filtered market data is passed on to the next analytical step.

[1129] Step 4:

[1130] Data analysis (PEST analysis and 3C analysis)

[1131] Input: Filtered market data.

[1132] Specific operation: The server generates prompt sentences using a generative artificial intelligence model (e.g., GPT-4) and performs analysis.

[1133] example:

[1134] Conduct a PEST and 3C analysis based on the following data: Data: {Filtered Market Data}

[1135] Data calculation: The generative AI model performs PEST and 3C analysis on the input market data and analyzes each element.

[1136] Output: The analysis results are saved on the server.

[1137] Step 5:

[1138] Integration of analysis results

[1139] Input: Results of PEST analysis and 3C analysis.

[1140] Specific operation: The server integrates the results of each analysis.

[1141] Data Processing: Comprehensive reports are generated from the integrated analysis results.

[1142] Output: A comprehensive report is generated and passed to the next step, Marketing Portfolio Generation.

[1143] Step 6:

[1144] Marketing portfolio generation

[1145] Input: Consolidated analysis results.

[1146] Specific operation: The server automatically generates a marketing portfolio based on the comprehensive report.

[1147] Data Processing: A detailed portfolio is generated, including marketing strategies and action plans.

[1148] Output: The generated marketing portfolio is saved on the server.

[1149] Step 7:

[1150] View the generated marketing portfolio

[1151] Input: The generated marketing portfolio.

[1152] Specific operation: The server displays the generated portfolio on the user's dashboard.

[1153] Output: Users can view the portfolio and download or print it as needed.

[1154] Step 8:

[1155] Proposing engagement measures

[1156] Input: User profile and historical data.

[1157] Specific operation: The server proposes optimal engagement measures based on the user profile.

[1158] Data processing: Analyze historical data and attribute information to develop targeting and messaging strategies.

[1159] Output: The user is notified of the suggested engagement measures.

[1160] Step 9:

[1161] Emotion analysis

[1162] Input: User input and operation data (click patterns, text input, etc.).

[1163] Specific operation: The server uses the emotion engine to analyze the user's emotional state in real time.

[1164] Data calculation: Analyzes user operation data and identifies emotional states such as excitement, frustration, and calmness.

[1165] Output: The analyzed emotional state is used to adjust the next policy.

[1166] Step 10:

[1167] Tailoring engagement strategies based on emotions

[1168] Input: Parsed emotional state.

[1169] Specific behavior: The server adjusts engagement strategies based on emotional state.

[1170] Data processing: Special offers and support information are generated according to the user's emotional state.

[1171] Output: The tailored engagement measures are provided to the user.

[1172] (Application example 2)

[1173] 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."

[1174] Conventional marketing systems mainly respond only to users' online actions and inputs, and lack mechanisms for utilizing customer behavior data in physical stores. This makes it difficult to immediately provide appropriate marketing measures based on customers' emotions and behavior in physical stores. Furthermore, the lack of real-time personalized information notifications leads to a decline in customer engagement. The present invention aims to solve these issues by analyzing customer behavior data and emotional states in physical stores in real time and providing personalized measures based on the results.

[1175] 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.

[1176] In this invention, the server includes means for analyzing the emotional state of a user, means for adjusting marketing measures based on the analyzed emotional state, means for collecting customer behavior data in a physical store and analyzing the emotional state in real time, and means for notifying personalized information in real time according to the customer's movements and behavior in the physical store. This makes it possible to instantly provide appropriate marketing measures based on the customer's behavior and emotions in the physical store, and is expected to improve customer engagement.

[1177] "User authentication" refers to the method or process by which a user verifies their identity and gains access to a system.

[1178] "Market research data collection tools" are methods and techniques for collecting information about market trends and consumer preferences.

[1179] "Market research data filtering means" refers to methods or techniques for selecting reliable information from collected market data based on specific criteria.

[1180] "Analytical methods using generative artificial intelligence models" are methods or techniques that use generative AI models to analyze data and derive insights or conclusions.

[1181] "Means for synthesizing analytical results" are methods or techniques for bringing together the results of separate data analyses and producing a single, unified conclusion or report.

[1182] "Means for generating a marketing portfolio" refers to methods and techniques for creating specific marketing strategies and action plans based on the results of analysis.

[1183] The "means for displaying the generated marketing portfolio" refers to a method or technology that allows a user to view the generated marketing portfolio.

[1184] "Means for proposing engagement measures based on user profiles" refers to methods and technologies for proposing appropriate marketing measures and messages based on user attribute information and past behavioral data.

[1185] A "means for analyzing a user's emotional state" is a method or technique for monitoring a user's actions or inputs and identifying the user's emotional state therefrom.

[1186] "Means for adjusting marketing strategies based on the analyzed emotional state" refers to methods and technologies for changing and adapting marketing strategies and messages according to the emotional state of the user.

[1187] "Means for collecting customer behavioral data in a physical store and analyzing their emotional state in real time" refers to methods and technologies for instantly obtaining customer behavioral data in a physical store and analyzing the emotional state of customers based on that data.

[1188] "Means of notifying customers of personalized information in real time according to their movements and actions within a physical store" refers to methods and technologies that instantly notify customers of individually optimized information and offers when they move to a specific area or take a specific action within a physical store.

[1189] The present invention is a system for improving the efficiency of marketing operations, and includes the following means.

[1190] User authentication method

[1191] A user launches an application and hits a login screen, where they enter their username and password, and the server validates the user by checking that information against a database, and then loads the user profile accordingly.

[1192] Market research data collection methods

[1193] The user inputs search keywords for creating a marketing portfolio into the device. The device collects market data from external data sources (such as APIs) based on the keywords. The server retrieves and stores the collected data.

[1194] Market research data filtering tools

[1195] The server filters the collected market data based on specific criteria to extract reliable information, thereby retaining the most relevant data.

[1196] Analytical tools using generative artificial intelligence models

[1197] The server performs PEST analysis (analysis of political, economic, social, and technological factors) and 3C analysis (analysis of company, competitor, and customer factors) on the market data filtered using the generative AI model.

[1198] A means of synthesizing analytical results

[1199] The analysis results are integrated by the server, and a marketing portfolio is automatically created. The integrated data details marketing strategies and action plans.

[1200] A means to create and display marketing portfolios

[1201] The created marketing portfolio is displayed on the user's dashboard and can be viewed by the user.

[1202] A means of proposing engagement measures based on user profiles

[1203] The server recommends engagement strategies based on the user profile, including targeting and messaging strategies using user demographics and historical data.

[1204] A means of analyzing the user's emotional state

[1205] The server uses an emotion engine that analyzes user input and actions to identify their emotional state. This is achieved by analyzing the user's text input, click patterns, mouse movements, etc.

[1206] A means of adjusting marketing efforts based on analyzed emotional states

[1207] After the emotion engine identifies the user's emotional state, the server can use this information to adjust marketing efforts appropriately. For example, if it determines that the user is excited, it will display information about new campaigns. However, if it determines that the user is confused, it will prioritize displaying help and support information.

[1208] In-store operation

[1209] Working in a physical store, the application collects real-time customer behavior data (e.g., product scanning, feedback input, in-app chat), which is used by an emotion engine to analyze the customer's emotional state.

[1210] Real-time notifications of new information

[1211] When a customer moves to a specific area or takes a specific action in a physical store, the system will send them personalized information in real time. For example, when a customer scans a specific product, they will instantly receive push notifications about related campaigns and special offers.

[1212] Hardware and software used

[1213] The hardware used to implement this invention includes a smartphone (compatible with iOS / Android), a server (cloud server), and a database (MySQL, MongoDB). The software used includes a smartphone app development framework (React Native), a generative AI model, and a sentiment analysis engine.

[1214] Examples of prompt sentences

[1215] The following prompts are used:

[1216] "We will explain how to analyze customer behavior data to make personalized product suggestions to increase customer engagement in physical stores. When a customer scans a product, the data is sent to a server in real time and analyzed by a sentiment analysis engine. If the customer responds positively, a generative AI model is used to generate targeted product and campaign information, which is then displayed on the app dashboard."

[1217] As described above, the present invention aims to improve customer engagement by analyzing customer behavioral data and emotional states in physical stores in real time and providing personalized measures based on that data.

[1218] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1219] Step 1:

[1220] User Authentication

[1221] A user launches an application and enters their username and password at the login screen. The server receives this input and checks it against the authentication information in its database.

[1222] Input: Username, Password

[1223] Processing: Database Matching

[1224] Output: Authentication result, user profile

[1225] Step 2:

[1226] Market research data collection

[1227] The user inputs search keywords for creating a marketing portfolio into the device, which then collects market data via external data sources (such as APIs) based on the keywords and sends it to the server.

[1228] Input: Search keyword

[1229] Processing: Data collection from external data sources

[1230] Output: Market data

[1231] Step 3:

[1232] Market Research Data Filtering

[1233] The server filters the collected market data based on specific criteria to extract reliable information.

[1234] Input: Market Data

[1235] Processing: Data Filtering

[1236] Output: Filtered market data

[1237] Step 4:

[1238] Data analysis using generative artificial intelligence models

[1239] The server uses generative AI models to perform PEST and 3C analysis on the filtered market data.

[1240] Input: Filtered market data

[1241] Processing: PEST analysis, 3C analysis

[1242] Output: Analysis results

[1243] Step 5:

[1244] Integration of analysis results

[1245] The server integrates the generated analysis results and automatically generates a marketing portfolio.

[1246] Input: Analysis results

[1247] Processing: Data Integration

[1248] Output: Marketing portfolio

[1249] Step 6:

[1250] View Marketing Portfolio

[1251] The generated marketing portfolio is displayed on the user's dashboard, where the user can view it.

[1252] Enter: Marketing Portfolio

[1253] Processing: Data display

[1254] Output: Marketing portfolio on a dashboard

[1255] Step 7:

[1256] Proposing engagement measures

[1257] The server proposes engagement measures based on the user profile.

[1258] Input: User profile, marketing portfolio

[1259] Processing: Targeting, Messaging

[1260] Output: Proposed engagement measures

[1261] Step 8:

[1262] Analyzing the user's emotional state

[1263] The server analyzes emotions from the user's inputs and actions (e.g., text input, click patterns, mouse movements) to identify their emotional state.

[1264] Input: User input and operation data

[1265] Processing: Sentiment Analysis

[1266] Output: User's emotional state

[1267] Step 9:

[1268] Adjusting policies based on analyzed emotional states

[1269] Based on the analysis results of the emotion engine, the server dynamically adjusts marketing measures.

[1270] Input: User's emotional state

[1271] Action: Adjustment of measures

[1272] Output: Coordinated marketing efforts

[1273] Step 10:

[1274] Collecting customer behavior data in physical stores and analyzing it in real time

[1275] Customer behavioral data (e.g., product scanning, feedback input) is collected in real time within physical stores and their emotional state is analyzed.

[1276] Input: Customer behavior data

[1277] Processing: Real-time data collection, sentiment analysis

[1278] Output: Customer emotional state and behavior data

[1279] Step 11:

[1280] Real-time personalized notifications

[1281] The server then sends personalized information in real time based on customer behavior, such as pushing relevant information when scanning a specific product or entering a specific area.

[1282] Input: Customer behavior data, emotional state

[1283] Processing: Information generation, notification

[1284] Output: Personalized push notification

[1285] Through these steps, the system is able to instantly provide appropriate marketing measures based on customer behavior and emotions in physical stores.

[1286] 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.

[1287] 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.

[1288] 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.

[1289] [Fourth embodiment]

[1290] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1291] 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.

[1292] 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).

[1293] 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.

[1294] 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.

[1295] 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).

[1296] 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.

[1297] 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.

[1298] 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.

[1299] 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.

[1300] 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.

[1301] 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.

[1302] 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."

[1303] The present invention is a system for streamlining marketing operations, and includes a user authentication means, a market research data collection means, a market research data filtering means, an analysis means using a generative AI model, a means for integrating analysis results, a means for generating a marketing portfolio, a means for displaying the generated marketing portfolio, and a means for proposing engagement measures based on a user profile.

[1304] When a user enters their login information, the server checks the information against a database to verify the user's authenticity. Specifically, the server queries the database for the username and password, and if successful, loads the user profile.

[1305] Next, the user enters keywords related to market research, and the device collects market data from external data sources (such as APIs) based on the keywords. The server retrieves the collected data and filters it based on specific criteria to extract the most relevant information.

[1306] The server then uses the generative AI model to perform PEST and 3C analyses on the filtered market data. PEST analysis analyzes political, economic, social, and technological factors, while 3C analysis analyzes company, competitor, and customer factors. This provides a deep understanding of a wide range of factors.

[1307] The server integrates the analysis results and automatically creates a marketing portfolio, which details marketing strategies and action plans. The server then displays the created marketing portfolio on the user's dashboard, where the user can view it.

[1308] Additionally, the server will suggest engagement strategies based on the user profile, including targeting and messaging strategies using user demographics and historical data, resulting in optimal marketing strategies for the user.

[1309] For example, when a user enters keywords such as "AI" and "marketing," data on the AI ​​market and marketing trends is collected from external market data sources. The server filters this data and performs PEST and 3C analyses to obtain insights into the current and forecast state of the AI ​​market, competitor trends, and target customer demand. Finally, a marketing portfolio is automatically generated based on this information, and the user is presented with the next step in marketing strategies.

[1310] This invention significantly reduces the work that previously took about 50 hours per report, thereby reducing the burden on marketing portfolio staff.

[1311] The processing flow will be explained below.

[1312] Step 1:

[1313] The user enters login information. The user enters their username and password on the system login screen.

[1314] Step 2:

[1315] The server authenticates the login information. It checks the entered username and password against a database to verify the user's identity. If the match is successful, the user profile is loaded.

[1316] Step 3:

[1317] The user inputs market research keywords. The user inputs search keywords for creating a marketing portfolio into the terminal.

[1318] Step 4:

[1319] The terminal collects market research data. The terminal sends keywords to an external data source (e.g., API) to obtain relevant market data.

[1320] Step 5:

[1321] The server filters the collected market data. The server filters the acquired market data based on specific criteria (e.g., the latest, most reliable information) to extract the most relevant information.

[1322] Step 6:

[1323] The server performs the PEST analysis, using a generative AI model to analyze political, economic, social, and technological factors against the filtered market data.

[1324] Step 7:

[1325] The server then performs the 3C analysis, also using a generative AI model to analyze the filtered market data for the company, competitors, and customers.

[1326] Step 8:

[1327] The server integrates the results of the analysis. The server integrates the results of the PEST analysis and the 3C analysis and automatically generates a marketing portfolio that includes strategic items and action plans.

[1328] Step 9:

[1329] The server displays the marketing portfolio. The server displays the generated marketing portfolio on the user's dashboard, where the user can view it.

[1330] Step 10:

[1331] The server proposes engagement strategies: The server proposes appropriate engagement strategies (targeting, messaging, etc.) based on the user profile.

[1332] By going through each step in this way, the user can efficiently create a marketing portfolio.

[1333] Example 1

[1334] 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."

[1335] Traditional marketing operations require a significant amount of time and effort to conduct market research, filter and analyze the data, and then integrate the results to create a marketing strategy. Furthermore, because much of this work is done manually, there is a high risk of human error and efficiency is low. Another issue is that it is difficult to appropriately propose engagement measures based on user profiles.

[1336] 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.

[1337] In this invention, the server includes a user authentication means, a market research data acquisition means, a market research data selection means, an analysis means using a generative artificial intelligence model, a means for integrating the analysis results, a means for generating a marketing strategy document, a means for displaying the generated marketing strategy document, and a means for proposing an engagement strategy based on a user profile. This makes marketing operations more efficient, significantly reduces time and labor, reduces human error, and enables the proposal of appropriate engagement measures.

[1338] "User authentication means" is a means for verifying the validity of a user by querying a database using authentication information (user name, password, etc.) entered by the user.

[1339] The "market research data acquisition means" is a means for collecting market research data from an external data source (such as an API) based on keywords specified by the user.

[1340] The "market research data selection means" is a means for filtering collected market research data based on specific criteria and extracting only highly relevant information.

[1341] An "analysis means using a generative artificial intelligence model" is a means for analyzing market research data that has been filtered using generative artificial intelligence (e.g., GPT-3).

[1342] The "means for integrating the analysis results" refers to a means for compiling the analysis results obtained by the generative artificial intelligence model into a single integrated data set.

[1343] The "means for generating a marketing strategy document" is a means for automatically creating a document detailing a marketing strategy and an action plan based on the integrated analysis results.

[1344] The "means for displaying the generated marketing strategy document" refers to a means for displaying the generated marketing strategy document on the dashboard of the user's operating terminal.

[1345] "Means for proposing engagement strategies based on user profiles" refers to means for proposing optimal targeting and messaging strategies based on user attribute information and past data.

[1346] The present invention is a system for improving the efficiency of marketing operations, and includes a user authentication means, a market research data acquisition means, a market research data selection means, an analysis means using a generative artificial intelligence model, a means for integrating the analysis results, a means for generating a marketing strategy document, a means for displaying the generated marketing strategy document, and a means for proposing an engagement strategy based on a user profile.

[1347] First, the user enters their login information (username and password) at the terminal, which then sends this information to the server, which checks the MySQL database to verify the user's authenticity. If the match is successful, the server loads the user profile.

[1348] Next, the user enters keywords related to market research (e.g., "AI" or "marketing"), and the device sends the keywords to the server, which then uses Python's requests library to retrieve market research data from external data sources (e.g., the Google Trends API).

[1349] The acquired data is processed by the server using the Pandas library and filtered based on specific criteria (e.g., time period or relevance).The server then inputs the filtered data into a generative artificial intelligence model (e.g., GPT-3) to perform PEST (political, economic, social, technological) and 3C (company, competitor, customer) analysis.

[1350] Example prompt sentence:

[1351] "Conduct a PEST analysis of the AI ​​and Marketing industry based on the following data: [filtered data]"

[1352] The analysis results obtained from the generative AI model are integrated by the server to automatically create a marketing strategy document, which is generated using the Jinja2 template engine and displayed on the dashboard of the user's device.

[1353] Additionally, the server suggests optimal engagement strategies (e.g., targeting and messaging strategies) based on the user profile, using machine learning models such as Scikit-learn to make predictions based on past user activity and demographic information.

[1354] As a specific example, if a user enters the keywords "AI" and "marketing," the server uses the Google Trends API to collect data on the AI ​​market and marketing trends. The collected data is filtered, and PEST and 3C analyses are performed using GPT-3. The results reveal the current state and forecast of the AI ​​market, the trends of competitors, and the needs of target customers. Based on this information, a marketing strategy document is generated and presented to the user. Optimal engagement measures are also proposed based on the user profile.

[1355] This streamlines marketing operations, significantly reducing the time and errors involved compared to traditional manual analysis.

[1356] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1357] Step 1:

[1358] The user enters a username and password on the login page. The device sends the information to the server, which then checks the received login information against a MySQL database to verify the user's authenticity. If the verification is successful, the server loads the user profile information and starts the session.

[1359] Input: Username, Password

[1360] Output: Authentication successful (session started) or authentication failed (error message)

[1361] Step 2:

[1362] The user enters keywords related to market research (e.g., "AI" or "marketing"). The device sends the keywords to the server. The server receives the keywords and proceeds to the next processing step.

[1363] Input: keyword

[1364] Output: The keyword is sent to the server

[1365] Step 3:

[1366] The server uses the provided keywords to collect market research data from external data sources (e.g., Google Trends API), using the Python requests library.

[1367] Input: keyword

[1368] Output: Market research data (obtained from external data sources)

[1369] Specific operation: The server sends a request to the Google Trends API for the keywords "AI" and "marketing" to obtain related market data.

[1370] Step 4:

[1371] The server processes the acquired market data using the Pandas library and filters it based on specified criteria (e.g., time range or correlation), eliminating unnecessary data and extracting only the most relevant information.

[1372] Input: Market research data

[1373] Output: Filtered market research data

[1374] Specific operation: The server keeps AI marketing-related data from the past year and removes old or irrelevant data from before that.

[1375] Step 5:

[1376] The server inputs the filtered data into a generative artificial intelligence model (e.g., GPT-3) to perform PEST analysis (political, economic, social, technological) and 3C analysis (company, competitors, customers). The server generates prompt statements, passes them to the AI ​​model, and receives responses from the AI.

[1377] Input: Filtered market research data

[1378] Output: PEST and 3C analysis results

[1379] Specific behavior: The server generates the following prompt:

[1380] "Conduct a PEST analysis of the AI ​​and Marketing industry based on the following data: [filtered data]"

[1381] Receive responses from the generative AI model and obtain analytical results.

[1382] Step 6:

[1383] The server uses Pandas to integrate the results of the PEST and 3C analyses obtained from the generative artificial intelligence model and compile them into a single dataset.

[1384] Input: PEST and 3C analysis results

[1385] Output: Marketing insights to be integrated

[1386] Specific operation: The server compiles data on "Politics," "Economy," "Society," "Technology," as well as "Company," "Competitor," and "Customer" into a single data frame.

[1387] Step 7:

[1388] The server uses the Jinja2 template engine to generate a marketing strategy document from the consolidated data, which details the marketing strategy and action plan.

[1389] Input: Integrated data

[1390] Output: Marketing strategy document

[1391] What it does: The server uses Jinja2 to automatically generate a detailed portfolio of "Current Status and Strategies of the AI ​​and Marketing Industry."

[1392] Step 8:

[1393] The server displays the generated marketing strategy document on the user's dashboard, and the terminal updates the dashboard to display the new document.

[1394] Input: Marketing Strategy Document

[1395] Output: Document displayed on the user's dashboard

[1396] What it does: The user's dashboard displays the "latest marketing portfolio on AI and marketing."

[1397] Step 9:

[1398] The server analyzes the data to suggest engagement strategies based on the user profile, using Scikit-learn to make predictions based on past user activity and attribute information.

[1399] Input: User profile information, history data

[1400] Output: The optimal targeting and messaging strategy suggested to the user.

[1401] Specific operation: The server will suggest new related strategies to users who have frequently viewed "AI marketing" related initiatives in the past.

[1402] (Application example 1)

[1403] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1404] Traditional marketing operations have the problem of requiring a great deal of time and effort to collect and analyze market research data and then propose advertising strategies based on that data. Furthermore, the lack of real-time capabilities makes it difficult to implement prompt and appropriate marketing measures. Another problem is the difficulty of providing optimal engagement measures based on user profiles.

[1405] 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.

[1406] In this invention, the server includes a user authentication means, a market research data collection means, a market research data filtering means, an analysis means using a generative AI model, a means for integrating analysis results, a means for generating a marketing portfolio, a means for displaying the generated marketing portfolio, a means for proposing engagement measures based on a user profile, and a means for collecting real-time market research data and proposing advertising strategies using that information. This enables significant efficiency improvements in marketing operations, utilization of real-time market research data, and the rapid provision of optimal engagement measures based on user profiles.

[1407] The "user authentication means" is a means for verifying the validity of a user based on the user's login information.

[1408] "Market research data collection means" refers to means for collecting data necessary for market research from external data sources.

[1409] A "market research data filtering means" is a means for extracting reliable data from collected data based on specific criteria.

[1410] An "analytical means using a generative artificial intelligence model" is a means for analyzing collected and filtered market data using a generative AI model.

[1411] "Means for integrating analytical results" refers to means for integrating analytical results obtained by generative AI models to generate a single comprehensive analytical result.

[1412] The "means for generating a marketing portfolio" is a means for creating a portfolio including a marketing strategy and an action plan based on the integrated analysis results.

[1413] The "means for displaying the generated marketing portfolio" is a means for displaying the generated marketing portfolio in a format that can be viewed by a user.

[1414] "Means for proposing engagement measures based on user profiles" refers to means for proposing targeting and messaging strategies based on user attribute information and past data.

[1415] "Means for collecting real-time market research data and proposing advertising strategies using that information" refers to means for collecting market research data in real time from external data sources and using that data to propose appropriate advertising strategies.

[1416] The present invention is a system for improving the efficiency of marketing operations, and includes a user authentication means, a market research data collection means, a market research data filtering means, an analysis means using a generative artificial intelligence model, a means for integrating analysis results, a means for generating a marketing portfolio, a means for displaying the generated marketing portfolio, a means for proposing engagement measures based on a user profile, and a means for collecting real-time market research data and proposing advertising strategies using that information.

[1417] This system performs the following processes:

[1418] First, a user logs into the smartphone application. The login process includes verifying the validity of the username and password using Firebase Authentication. If authentication is successful, the user's profile is loaded.

[1419] Next, the user enters keywords related to a specific market or product. Based on the keywords entered, the device collects market research data in real time from the Google Trends API and other similar market data sources. This market research data collection method gathers a wide range of data related to the keywords entered by the user.

[1420] The collected market data is filtered by the terminal using Python. The market research data filtering means extracts reliable information from the collected data based on specific criteria. The filtered data is refined to include only highly accurate information.

[1421] The server then analyzes the filtered data using generative AI models, such as Hugging Face's Transformer model, to derive deep insights from the data based on political, economic, social, and technological factors, as well as company, competitor, and customer factors.

[1422] The obtained analysis results are integrated by the server. A marketing portfolio is generated from the integrated analysis results. A marketing portfolio is a document that details a strategy and action plan.

[1423] The generated marketing portfolio is automatically displayed on the user's dashboard from the server, providing detailed information on the current and forecast market situation, competitor trends, and target customer demand.

[1424] Additionally, the server proposes engagement strategies based on the user profile. The engagement strategy proposal means uses the user's demographic information and historical data to generate individually customized targeting and messaging strategies.

[1425] As a specific example, when a user enters the keyword "AI marketing," the following steps are executed. First, the user is authenticated using Firebase Authentication. Next, trend data related to "AI marketing" is collected from the Google Trends API. Next, the data is filtered using Python to extract the most relevant data. After that, PEST and 3C analyses are performed using Hugging Face's Transformer model, and a marketing portfolio is generated from the integrated analysis results. Finally, the app suggests optimal advertising measures to the user based on the generated portfolio.

[1426] An example of a prompt to be input into a generative AI model is, "Collect and analyze market data related to AI marketing and propose insights based on the following indicators: political influence, economic influence, social influence, technological influence, company situation, competitive trends, and customer demand."

[1427] In this way, the system of the present invention significantly improves the efficiency of marketing operations, enables the utilization of market research data in real time, and can quickly provide optimal engagement measures based on user profiles.

[1428] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1429] Step 1:

[1430] A user launches a smartphone application and enters their login information (username and password). The entered login information is verified on the server side using Firebase Authentication. The server then queries the database and loads the correct user profile. This authenticates the user and allows them to access their individual data.

[1431] Input: Username, Password

[1432] Output: User profile loaded

[1433] Step 2:

[1434] The user enters keywords related to a specific market or product into the application's input fields. The keywords are sent to the server, which collects real-time market data from Google Trends API and other market data sources. The terminal sends an API request to collect the data and receives the results.

[1435] Input:keyword

[1436] Output: Market data collection

[1437] Step 3:

[1438] The server receives the collected market data and filters it using a Python script. The market research data filtering method extracts reliable information based on specific criteria. The filtered data contains only highly accurate information and then moves on to the next step.

[1439] Input: Collected market data

[1440] Output: Filtered market data

[1441] Step 4:

[1442] The server analyzes the filtered data using generative AI models (e.g., the Hugging Face Transformer model), and performs PEST and 3C analyses using prompt statements to gain insights into various factors (political, economic, social, technological, company, competitors, and customers).

[1443] Input: Filtered market data

[1444] Output: Analysis results (PEST analysis and 3C analysis insights)

[1445] Step 5:

[1446] The server then integrates the resulting analysis results and automatically generates a marketing portfolio, which details a marketing strategy and action plan based on a wide range of factors.

[1447] Input: Analysis results

[1448] Output: Marketing portfolio

[1449] Step 6:

[1450] The generated marketing portfolio is displayed on the user's dashboard by the server, allowing the user to view the portfolio within the application and check the current state and forecast of the specific market.

[1451] Enter: Marketing Portfolio

[1452] Output: Marketing portfolio displayed in a dashboard

[1453] Step 7:

[1454] The server then proposes engagement strategies based on the user profile. Taking into account the user's demographic information and past data, a personalized targeting and messaging strategy is automatically generated and presented to the user.

[1455] Input: User profile, historical data

[1456] Output: Proposal of engagement measures

[1457] As a concrete example, when a user enters "AI marketing" as a keyword, the following process is executed: After user authentication using Firebase Authentication, trend data related to "AI marketing" is collected from the Google Trends API. The data is filtered using Python, and PEST and 3C analysis is performed using the Hugging Face Transformer model. The obtained insights are integrated to generate a marketing portfolio and display it on a dashboard. Finally, optimal engagement measures are proposed based on the user profile.

[1458] Example prompt: "Collect and analyze market data on AI marketing and propose insights based on the following metrics: political influence, economic influence, social influence, technological influence, company situation, competitive trends, and customer demand."

[1459] 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.

[1460] The present invention is a system for improving the efficiency of marketing operations, and includes a user authentication means, a market research data collection means, a market research data filtering means, an analysis means using a generative AI model, a means for integrating analysis results, a means for generating a marketing portfolio, a means for displaying the generated marketing portfolio, a means for proposing engagement measures based on a user profile, and an emotion engine for recognizing user emotions.

[1461] The user enters login information. The user enters their username and password on the system's login screen. The server checks the information against a database to verify the user's authenticity. If the match is successful, the user profile is loaded.

[1462] Next, the user inputs keywords related to market research. The user then inputs search keywords for creating a marketing portfolio into the device. The device collects market data from external data sources (such as APIs) based on the keywords. The server retrieves the collected data and filters it based on specific criteria to extract relevant information.

[1463] The server then uses the generative AI model to perform PEST and 3C analyses on the filtered market data. PEST analysis analyzes political, economic, social, and technological factors, while 3C analysis analyzes company, competitor, and customer factors. This provides a deep understanding of a wide range of factors.

[1464] The server integrates the analysis results and automatically creates a marketing portfolio, which details marketing strategies and action plans. The server then displays the created marketing portfolio on the user's dashboard, where the user can view it.

[1465] Additionally, the server will suggest engagement strategies based on the user profile, including targeting and messaging strategies using user demographics and historical data, resulting in optimal marketing strategies for the user.

[1466] The present invention introduces an emotion engine that analyzes a user's emotional state to further enhance marketing initiatives. Specifically, by analyzing a user's inputs and operations and identifying their emotional state, more personalized recommendations can be made. The emotion engine analyzes the user's text inputs, click patterns, mouse movements, etc. to determine whether the user is excited, confused, or calm. Based on this, engagement initiatives can be appropriately adjusted. For example, if it is determined that the user is dissatisfied, special offers or support information can be presented to alleviate the dissatisfaction.

[1467] For example, if a user types in the keywords "AI" and "marketing," the sentiment engine analyzes the user's reaction to the search results. If the user responds positively to the search results, the server can further increase the user's engagement by suggesting new marketing campaigns or powerful strategies against competitors.

[1468] This invention significantly reduces the work required to create one report, which previously took approximately 50 hours, thereby reducing the burden on marketing portfolio managers. Furthermore, by incorporating an emotion engine, it becomes possible to respond flexibly to user emotions, resulting in higher engagement and more effective marketing strategies.

[1469] The processing flow will be explained below.

[1470] Step 1:

[1471] The user enters login information. The user enters their username and password on the system login screen.

[1472] Step 2:

[1473] The server authenticates the login information. It checks the entered username and password against a database to verify the user's identity. If the match is successful, it loads the user profile.

[1474] Step 3:

[1475] The user inputs market research keywords. The user inputs search keywords for creating a marketing portfolio into the terminal. For example, let's use keywords such as "AI" and "marketing."

[1476] Step 4:

[1477] The device collects market research data. It uses input keywords to retrieve market data from external APIs, which then return relevant articles, reports, and statistics.

[1478] Step 5:

[1479] The server filters the collected market data. The server sorts the acquired market data based on criteria and extracts reliable and up-to-date information.

[1480] Step 6:

[1481] The server performs a PEST analysis, analyzing political, economic, social, and technological factors against market data filtered using a generative AI model. For example, it evaluates government AI policies, economic impact, social acceptance, and technological advances.

[1482] Step 7:

[1483] The server performs the 3C analysis. It also uses a generative AI model to analyze the company, competitors, and customers. For example, it evaluates the company's strengths and weaknesses, competitor trends, customer needs, and market trends.

[1484] Step 8:

[1485] The server integrates the results of the analysis. The server integrates the results of the PEST analysis and the 3C analysis to obtain comprehensive insights. Marketing strategies and action plans are determined based on these insights.

[1486] Step 9:

[1487] The server automatically generates a marketing portfolio based on the integrated analysis results. The portfolio includes specific strategic items, market forecasts, and measures.

[1488] Step 10:

[1489] The server displays the marketing portfolio. The server displays the generated marketing portfolio on the user's dashboard. The user views it and checks the required information.

[1490] Step 11:

[1491] The server starts an emotion engine to analyze the user's reaction. The emotion engine analyzes the user's input, operation, mouse movement, etc. to identify the user's emotional state.

[1492] Step 12:

[1493] The server proposes appropriate engagement strategies based on the user's emotional state and user profile, as recognized by the emotion engine. For example, if the user is dissatisfied, the server may present special offers or support information.

[1494] By going through each step in this way, users can not only create a marketing portfolio efficiently, but also respond flexibly according to their emotional state.

[1495] Example 2

[1496] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1497] Traditional marketing operations require a great deal of time and effort, and there is a need for greater efficiency, particularly in market research and data analysis. Another issue is the difficulty of proposing personalized engagement measures that take user emotions into account. There is a need for a system that can solve these problems, improve operational efficiency, and realize effective marketing measures.

[1498] 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.

[1499] In this invention, the server includes a user authentication means, a market research data collection means, a market research data filtering means, an analysis means using a generative AI model, a means for integrating analysis results, a means for generating a marketing portfolio, a means for displaying the generated marketing portfolio, a means for proposing engagement measures based on a user profile, a means for analyzing user emotions, and a means for adjusting the engagement measures based on the analyzed emotions. This makes it possible to improve the efficiency of marketing operations and realize flexible marketing measures that correspond to user emotions.

[1500] "User authentication means" is a means used by a user to log in to a system, and is a function that authenticates the user's legitimacy by inputting a user name and password.

[1501] The "market research data collection means" is a function that allows the terminal or server to collect market data from external data sources based on keywords entered by the user.

[1502] The "market research data filtering means" is a function that filters collected market data based on specific criteria and extracts only highly relevant data.

[1503] An "analysis means using a generative artificial intelligence model" is a means for analyzing market data using a generative AI model (e.g., a generative language model) and performing a specific analysis.

[1504] The "means for integrating analysis results" is a function for integrating the analysis results obtained individually and generating a comprehensive report.

[1505] The "means for generating a marketing portfolio" is a function for automatically generating a portfolio including a marketing strategy and an action plan based on the integrated analysis results.

[1506] The "means for displaying the generated marketing portfolio" is a function that displays the generated marketing portfolio on the user's dashboard and allows the user to view or download it.

[1507] "Means to propose engagement measures based on user profiles" is a function that proposes targeting and messaging strategies based on user attribute information and past data.

[1508] The "means for analyzing user emotions" is a function that analyzes the user's inputs and operations and identifies the user's emotional state.

[1509] The "means for adjusting engagement measures based on analyzed emotions" is a function that appropriately adjusts engagement measures and provides appropriate offers and support information based on the analyzed emotional state of the user.

[1510] The present invention provides a system for improving the efficiency of marketing operations and providing flexible engagement measures that correspond to user emotions. Specific embodiments will be described below.

[1511] User Authentication

[1512] The user enters a username and password on the system's login screen. The terminal sends this information to the server, which checks the database to verify the user's authenticity. If authentication is successful, the server loads the user profile and returns a login success message to the user.

[1513] Market research data collection

[1514] The user inputs keywords for market research. The device sends these keywords to the server. The server accesses external data sources (e.g., APIs, databases) and collects market data based on the specified keywords. This data is temporarily stored on the server.

[1515] Filtering Market Research Data

[1516] The server filters the collected data according to certain criteria, which involves extracting reliable data and filtering out less relevant data.

[1517] Data analysis

[1518] The server performs PEST analysis (political, economic, social, technological) and 3C analysis (company, competitors, customers) on the market data collected and filtered using a generative artificial intelligence model (e.g., GPT-4). The prompt is as follows:

[1519] Conduct a PEST analysis and 3C analysis based on the following data: Data: {Collected market data}

[1520] Integration of analysis results

[1521] The server consolidates the results of each analysis and generates a comprehensive report containing insights and conclusions that form the basis of marketing strategies.

[1522] Marketing portfolio generation

[1523] The server automatically generates a marketing portfolio based on the integrated analysis results, which describes a detailed marketing strategy and action plan.

[1524] View the generated marketing portfolio

[1525] The server displays the generated marketing portfolio on the user's dashboard, where the user can view it and download or print it as needed.

[1526] Proposing engagement measures

[1527] The server then proposes engagement strategies based on the user profile, including targeting and messaging strategies, leveraging the user's historical data and demographic information to provide personalized recommendations.

[1528] Emotion analysis

[1529] The server runs an emotion engine based on the user's input and operations to analyze the user's emotional state. For example, it analyzes the user's click patterns and text input to identify the user's emotional state (e.g., excited, frustrated, calm).

[1530] Tailoring engagement strategies based on emotions

[1531] The server can then adjust engagement strategies appropriately based on the analyzed emotional state: for example, if a user expresses dissatisfaction, it can increase engagement by providing special discount offers or additional support information.

[1532] As described above, the present invention makes it possible to improve the efficiency of marketing operations and provide flexible and effective marketing measures that respond to the emotions of users.

[1533] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1534] Step 1:

[1535] User Authentication

[1536] Input: The user enters their username and password on the login screen.

[1537] Specific operation: The terminal sends the entered username and password to the server.

[1538] Data processing: The server checks the received username and password against a database to verify the user's authenticity.

[1539] Output: If authentication is successful, the server loads the user profile and sends a login successful message to the user.

[1540] Step 2:

[1541] Market research data collection

[1542] Input: The user inputs keywords into the terminal to create a marketing portfolio.

[1543] Specific operation: The terminal sends the input keyword to the server.

[1544] Data Processing: The server accesses external data sources (APIs, databases) and collects market data based on the specified keywords.

[1545] Output: The collected market data is temporarily stored on the server.

[1546] Step 3:

[1547] Filtering Market Research Data

[1548] Input: Market data temporarily stored on the server.

[1549] Specific Actions: The server filters the data based on certain criteria (e.g., data reliability, date, relevance).

[1550] Data processing: Filtered market data removes irrelevant data and extracts only the necessary data.

[1551] Output: The filtered market data is passed on to the next analytical step.

[1552] Step 4:

[1553] Data analysis (PEST analysis and 3C analysis)

[1554] Input: Filtered market data.

[1555] Specific operation: The server generates prompt sentences using a generative artificial intelligence model (e.g., GPT-4) and performs analysis.

[1556] example:

[1557] Conduct a PEST and 3C analysis based on the following data: Data: {Filtered Market Data}

[1558] Data calculation: The generative AI model performs PEST and 3C analysis on the input market data and analyzes each element.

[1559] Output: The analysis results are saved on the server.

[1560] Step 5:

[1561] Integration of analysis results

[1562] Input: Results of PEST analysis and 3C analysis.

[1563] Specific operation: The server integrates the results of each analysis.

[1564] Data Processing: Comprehensive reports are generated from the integrated analysis results.

[1565] Output: A comprehensive report is generated and passed to the next step, Marketing Portfolio Generation.

[1566] Step 6:

[1567] Marketing portfolio generation

[1568] Input: Consolidated analysis results.

[1569] Specific operation: The server automatically generates a marketing portfolio based on the comprehensive report.

[1570] Data Processing: A detailed portfolio is generated, including marketing strategies and action plans.

[1571] Output: The generated marketing portfolio is saved on the server.

[1572] Step 7:

[1573] View the generated marketing portfolio

[1574] Input: The generated marketing portfolio.

[1575] Specific operation: The server displays the generated portfolio on the user's dashboard.

[1576] Output: Users can view the portfolio and download or print it as needed.

[1577] Step 8:

[1578] Proposing engagement measures

[1579] Input: User profile and historical data.

[1580] Specific operation: The server proposes optimal engagement measures based on the user profile.

[1581] Data processing: Analyze historical data and attribute information to develop targeting and messaging strategies.

[1582] Output: The user is notified of the suggested engagement measures.

[1583] Step 9:

[1584] Emotion analysis

[1585] Input: User input and operation data (click patterns, text input, etc.).

[1586] Specific operation: The server uses the emotion engine to analyze the user's emotional state in real time.

[1587] Data calculation: Analyzes user operation data and identifies emotional states such as excitement, frustration, and calmness.

[1588] Output: The analyzed emotional state is used to adjust the next policy.

[1589] Step 10:

[1590] Tailoring engagement strategies based on emotions

[1591] Input: Parsed emotional state.

[1592] Specific behavior: The server adjusts engagement strategies based on emotional state.

[1593] Data processing: Special offers and support information are generated according to the user's emotional state.

[1594] Output: The tailored engagement measures are provided to the user.

[1595] (Application example 2)

[1596] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1597] Conventional marketing systems mainly respond only to users' online actions and inputs, and lack mechanisms for utilizing customer behavior data in physical stores. This makes it difficult to immediately provide appropriate marketing measures based on customers' emotions and behavior in physical stores. Furthermore, the lack of real-time personalized information notifications leads to a decline in customer engagement. The present invention aims to solve these issues by analyzing customer behavior data and emotional states in physical stores in real time and providing personalized measures based on the results.

[1598] 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.

[1599] In this invention, the server includes means for analyzing the emotional state of a user, means for adjusting marketing measures based on the analyzed emotional state, means for collecting customer behavior data in a physical store and analyzing the emotional state in real time, and means for notifying personalized information in real time according to the customer's movements and behavior in the physical store. This makes it possible to instantly provide appropriate marketing measures based on the customer's behavior and emotions in the physical store, and is expected to improve customer engagement.

[1600] "User authentication" refers to the method or process by which a user verifies their identity and gains access to a system.

[1601] "Market research data collection tools" are methods and techniques for collecting information about market trends and consumer preferences.

[1602] "Market research data filtering means" refers to methods or techniques for selecting reliable information from collected market data based on specific criteria.

[1603] "Analytical methods using generative artificial intelligence models" are methods or techniques that use generative AI models to analyze data and derive insights or conclusions.

[1604] "Means for synthesizing analytical results" are methods or techniques for bringing together the results of separate data analyses and producing a single, unified conclusion or report.

[1605] "Means for generating a marketing portfolio" refers to methods and techniques for creating specific marketing strategies and action plans based on the results of analysis.

[1606] The "means for displaying the generated marketing portfolio" refers to a method or technology that allows a user to view the generated marketing portfolio.

[1607] "Means for proposing engagement measures based on user profiles" refers to methods and technologies for proposing appropriate marketing measures and messages based on user attribute information and past behavioral data.

[1608] A "means for analyzing a user's emotional state" is a method or technique for monitoring a user's actions or inputs and identifying the user's emotional state therefrom.

[1609] "Means for adjusting marketing strategies based on the analyzed emotional state" refers to methods and technologies for changing and adapting marketing strategies and messages according to the emotional state of the user.

[1610] "Means for collecting customer behavioral data in a physical store and analyzing their emotional state in real time" refers to methods and technologies for instantly obtaining customer behavioral data in a physical store and analyzing the emotional state of customers based on that data.

[1611] "Means of notifying customers of personalized information in real time according to their movements and actions within a physical store" refers to methods and technologies that instantly notify customers of individually optimized information and offers when they move to a specific area or take a specific action within a physical store.

[1612] The present invention is a system for improving the efficiency of marketing operations, and includes the following means.

[1613] User authentication method

[1614] A user launches an application and hits a login screen, where they enter their username and password, and the server validates the user by checking that information against a database, and then loads the user profile accordingly.

[1615] Market research data collection methods

[1616] The user inputs search keywords for creating a marketing portfolio into the device. The device collects market data from external data sources (such as APIs) based on the keywords. The server retrieves and stores the collected data.

[1617] Market research data filtering tools

[1618] The server filters the collected market data based on specific criteria to extract reliable information, thereby retaining the most relevant data.

[1619] Analytical tools using generative artificial intelligence models

[1620] The server performs PEST analysis (analysis of political, economic, social, and technological factors) and 3C analysis (analysis of company, competitor, and customer factors) on the market data filtered using the generative AI model.

[1621] A means of synthesizing analytical results

[1622] The analysis results are integrated by the server, and a marketing portfolio is automatically created. The integrated data details marketing strategies and action plans.

[1623] A means to create and display marketing portfolios

[1624] The created marketing portfolio is displayed on the user's dashboard and can be viewed by the user.

[1625] A means of proposing engagement measures based on user profiles

[1626] The server recommends engagement strategies based on the user profile, including targeting and messaging strategies using user demographics and historical data.

[1627] A means of analyzing the user's emotional state

[1628] The server uses an emotion engine that analyzes user input and actions to identify their emotional state. This is achieved by analyzing the user's text input, click patterns, mouse movements, etc.

[1629] A means of adjusting marketing efforts based on analyzed emotional states

[1630] After the emotion engine identifies the user's emotional state, the server can use this information to adjust marketing efforts appropriately. For example, if it determines that the user is excited, it will display information about new campaigns. However, if it determines that the user is confused, it will prioritize displaying help and support information.

[1631] In-store operation

[1632] Working in a physical store, the application collects real-time customer behavior data (e.g., product scanning, feedback input, in-app chat), which is used by an emotion engine to analyze the customer's emotional state.

[1633] Real-time notifications of new information

[1634] When a customer moves to a specific area or takes a specific action in a physical store, the system will send them personalized information in real time. For example, when a customer scans a specific product, they will instantly receive push notifications about related campaigns and special offers.

[1635] Hardware and software used

[1636] The hardware used to implement this invention includes a smartphone (compatible with iOS / Android), a server (cloud server), and a database (MySQL, MongoDB). The software used includes a smartphone app development framework (React Native), a generative AI model, and a sentiment analysis engine.

[1637] Examples of prompt sentences

[1638] The following prompts are used:

[1639] "We will explain how to analyze customer behavior data to make personalized product suggestions to increase customer engagement in physical stores. When a customer scans a product, the data is sent to a server in real time and analyzed by a sentiment analysis engine. If the customer responds positively, a generative AI model is used to generate targeted product and campaign information, which is then displayed on the app dashboard."

[1640] As described above, the present invention aims to improve customer engagement by analyzing customer behavioral data and emotional states in physical stores in real time and providing personalized measures based on that data.

[1641] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1642] Step 1:

[1643] User Authentication

[1644] A user launches an application and enters their username and password at the login screen. The server receives this input and checks it against the authentication information in its database.

[1645] Input: Username, Password

[1646] Processing: Database Matching

[1647] Output: Authentication result, user profile

[1648] Step 2:

[1649] Market research data collection

[1650] The user inputs search keywords for creating a marketing portfolio into the device, which then collects market data via external data sources (such as APIs) based on the keywords and sends it to the server.

[1651] Input: Search keyword

[1652] Processing: Data collection from external data sources

[1653] Output: Market data

[1654] Step 3:

[1655] Market Research Data Filtering

[1656] The server filters the collected market data based on specific criteria to extract reliable information.

[1657] Input: Market Data

[1658] Processing: Data Filtering

[1659] Output: Filtered market data

[1660] Step 4:

[1661] Data analysis using generative artificial intelligence models

[1662] The server uses generative AI models to perform PEST and 3C analysis on the filtered market data.

[1663] Input: Filtered market data

[1664] Processing: PEST analysis, 3C analysis

[1665] Output: Analysis results

[1666] Step 5:

[1667] Integration of analysis results

[1668] The server integrates the generated analysis results and automatically generates a marketing portfolio.

[1669] Input: Analysis results

[1670] Processing: Data Integration

[1671] Output: Marketing portfolio

[1672] Step 6:

[1673] View Marketing Portfolio

[1674] The generated marketing portfolio is displayed on the user's dashboard, where the user can view it.

[1675] Enter: Marketing Portfolio

[1676] Processing: Data display

[1677] Output: Marketing portfolio on a dashboard

[1678] Step 7:

[1679] Proposing engagement measures

[1680] The server proposes engagement measures based on the user profile.

[1681] Input: User profile, marketing portfolio

[1682] Processing: Targeting, Messaging

[1683] Output: Proposed engagement measures

[1684] Step 8:

[1685] Analyzing the user's emotional state

[1686] The server analyzes emotions from the user's inputs and actions (e.g., text input, click patterns, mouse movements) to identify their emotional state.

[1687] Input: User input and operation data

[1688] Processing: Sentiment Analysis

[1689] Output: User's emotional state

[1690] Step 9:

[1691] Adjusting policies based on analyzed emotional states

[1692] Based on the analysis results of the emotion engine, the server dynamically adjusts marketing measures.

[1693] Input: User's emotional state

[1694] Action: Adjustment of measures

[1695] Output: Coordinated marketing efforts

[1696] Step 10:

[1697] Collecting customer behavior data in physical stores and analyzing it in real time

[1698] Customer behavioral data (e.g., product scanning, feedback input) is collected in real time within physical stores and their emotional state is analyzed.

[1699] Input: Customer behavior data

[1700] Processing: Real-time data collection, sentiment analysis

[1701] Output: Customer emotional state and behavior data

[1702] Step 11:

[1703] Real-time personalized notifications

[1704] The server then sends personalized information in real time based on customer behavior, such as pushing relevant information when scanning a specific product or entering a specific area.

[1705] Input: Customer behavior data, emotional state

[1706] Processing: Information generation, notification

[1707] Output: Personalized push notification

[1708] Through these steps, the system is able to instantly provide appropriate marketing measures based on customer behavior and emotions in physical stores.

[1709] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1710] 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.

[1711] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1712] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1713] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1714] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1715] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1716] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1717] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1718] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1719] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1720] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1721] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1722] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1723] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1724] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1725] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1726] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1727] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1728] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1729] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1730] The following is further disclosed regarding the above embodiment.

[1731] (Claim 1)

[1732] User authentication means;

[1733] Market research data collection means;

[1734] market research data filtering means;

[1735] an analytical means using a generative artificial intelligence model;

[1736] a means of synthesizing the results of the analysis;

[1737] a means for generating a marketing portfolio;

[1738] a means for displaying the generated marketing portfolio;

[1739] A means for proposing engagement measures based on user profiles;

[1740] A system including:

[1741] (Claim 2)

[1742] 2. The system according to claim 1, wherein the market research data filtering means is a means for extracting reliable data based on specific criteria.

[1743] (Claim 3)

[1744] 2. The system according to claim 1, wherein the analysis means using the generative artificial intelligence model includes means for analyzing political, economic, social, and technological factors.

[1745] (Claim 4)

[1746] 2. The system according to claim 1, wherein the analysis means using the generative artificial intelligence model includes means for analyzing company, competitor, and customer factors.

[1747] "Example 1"

[1748] (Claim 1)

[1749] User authentication means;

[1750] A means of obtaining market research data;

[1751] Market research data selection means;

[1752] An analysis method using a generative artificial intelligence model;

[1753] a means of integrating the results of the analysis;

[1754] a means for generating a marketing strategy document;

[1755] a means for displaying the generated marketing strategy document;

[1756] a means for suggesting engagement strategies based on a user profile;

[1757] A system including:

[1758] (Claim 2)

[1759] 2. The system according to claim 1, wherein the market research data selection means extracts reliable data based on specific criteria.

[1760] (Claim 3)

[1761] 2. The system according to claim 1, wherein the analysis means using a generative artificial intelligence model analyzes political, economic, social, and technological elements.

[1762] "Application Example 1"

[1763] (Claim 1)

[1764] User authentication means;

[1765] Market research data collection means;

[1766] market research data filtering means;

[1767] an analytical means using a generative artificial intelligence model;

[1768] a means of synthesizing the results of the analysis;

[1769] a means for generating a marketing portfolio;

[1770] a means for displaying the generated marketing portfolio;

[1771] A means for proposing engagement measures based on user profiles;

[1772] Collecting real-time market research data and using that information to propose advertising strategies.

[1773] A system including:

[1774] (Claim 2)

[1775] 2. The system according to claim 1, wherein the market research data filtering means is a means for extracting reliable data based on specific criteria.

[1776] (Claim 3)

[1777] 2. The system according to claim 1, wherein the analysis means using the generative artificial intelligence model includes means for analyzing political, economic, social, and technological factors.

[1778] "Example 2: Combining Emotion Engines"

[1779] (Claim 1)

[1780] User authentication means;

[1781] Market research data collection means;

[1782] market research data filtering means;

[1783] an analytical means using a generative artificial intelligence model;

[1784] a means of synthesizing the results of the analysis;

[1785] a means for generating a marketing portfolio;

[1786] a means for displaying the generated marketing portfolio;

[1787] A means for proposing engagement measures based on user profiles;

[1788] means for analyzing user emotions;

[1789] A means for adjusting engagement measures based on the analyzed sentiment; and

[1790] A system including:

[1791] (Claim 2)

[1792] 2. The system according to claim 1, wherein the market research data filtering means is a means for extracting reliable data based on specific criteria.

[1793] (Claim 3)

[1794] 2. The system according to claim 1, wherein the analysis means using the generative artificial intelligence model includes means for analyzing political, economic, social, and technological factors.

[1795] "Application example 2 when combining emotion engines"

[1796] (Claim 1)

[1797] User authentication means;

[1798] Market research data collection means;

[1799] market research data filtering means;

[1800] an analytical means using a generative artificial intelligence model;

[1801] a means of synthesizing the results of the analysis;

[1802] a means for generating a marketing portfolio;

[1803] a means for displaying the generated marketing portfolio;

[1804] A means for proposing engagement measures based on user profiles;

[1805] means for analyzing the emotional state of a user;

[1806] means for adjusting marketing efforts based on the analyzed emotional state;

[1807] A means of collecting customer behavior data in physical stores and analyzing their emotional state in real time;

[1808] A means to deliver personalized information in real time according to customer movements and behavior within a physical store,

[1809] A system including:

[1810] (Claim 2)

[1811] 2. The system according to claim 1, wherein the market research data filtering means is a means for extracting reliable data based on specific criteria.

[1812] (Claim 3)

[1813] 2. The system according to claim 1, wherein the analysis means using the generative artificial intelligence model includes means for analyzing political, economic, social, and technological factors. [Explanation of symbols]

[1814] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. User authentication means; Market research data collection means; market research data filtering means; an analytical means using a generative artificial intelligence model; a means of synthesizing the results of the analysis; a means for generating a marketing portfolio; a means for displaying the generated marketing portfolio; A means for proposing engagement measures based on user profiles; A system including:

2. 2. The system according to claim 1, wherein the market research data filtering means is a means for extracting reliable data based on specific criteria.

3. 2. The system according to claim 1, wherein the analysis means using the generative artificial intelligence model includes means for analyzing political, economic, social, and technological factors.

4. 2. The system according to claim 1, wherein the analysis means using the generative artificial intelligence model includes means for analyzing company, competitor, and customer factors.

Images