System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting desired user profile information; means for automatically generating a persona based on the user profile information; means for automatically generating a list of survey questions based on the generated persona; means for providing the list of survey questions to a user; means for conducting a survey by the user; means for aggregating and analyzing the survey results; and means for visualizing and presenting the analysis results to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] The present invention aims to solve the problem of high costs involved in user surveys. The costs and time involved increase especially when targeting users with diverse backgrounds, including nationality, culture, gender, and age. Furthermore, conventional survey methods pose a challenge, as they require time to generate personas and design surveys, making it difficult to respond to market demands quickly. There is a need for a means to solve these problems and conduct diverse user surveys efficiently and at low cost. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by the following means. The system includes a means for inputting desired user profile information, a means for automatically generating a persona based on the user profile information, a means for automatically generating a survey question list based on the generated persona, a means for providing the survey question list to a user, a means for the user to conduct a survey, a means for aggregating and analyzing the survey results, and a means for visualizing and presenting the analysis results to the user. This makes it possible to conduct multinational and multicultural user surveys efficiently and at low cost. Furthermore, by using generation AI to quickly generate personas and designing surveys based on those personas, rapid market response is possible.

[0006] "User profile information" is age, gender, occupation, nationality, and other relevant information that a user enters to generate a persona.

[0007] A "persona" is a detailed profile of a virtual user that is automatically generated by AI based on user profile information.

[0008] "Generative AI" is artificial intelligence that uses specific algorithms to analyze data and automatically generate personas from user profile information.

[0009] The "survey question list" is a list of specific questions for conducting a user survey, which is automatically generated based on the generated persona.

[0010] "User" means an individual or organization that intends to use the System to achieve a specific research objective.

[0011] A "database" is a storage device within the system used to manage and store information such as generated personas, survey questionnaires, and survey results.

[0012] The "data analysis module" is a software module for analyzing aggregated survey results and extracting statistical data and insights.

[0013] A "visualization module" is a software module that visualizes analysis results and provides them to users in the form of graphs, charts, etc. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] MODE FOR CARRYING OUT THE INVENTION

[0036] This invention is a system that automatically generates personas using generation AI based on user profile information and automatically provides a list of survey questions based on those personas in order to conduct user surveys efficiently and at low cost.

[0037] Program processing overview

[0038] 1. User enters persona information

[0039] The user logs in to the system and accesses the persona information input screen.

[0040] Users enter profile information such as age, gender, occupation, nationality, hobbies, and lifestyle.

[0041] After inputting, the user clicks the "Submit" button to send the information to the system.

[0042] 2. The server automatically generates personas

[0043] The server receives the profile information sent by the user.

[0044] The server's generation AI analyzes the profile information and generates a detailed persona.

[0045] This persona includes name, hobbies, lifestyle, values, needs, challenges, etc.

[0046] The generated personas are stored in a database.

[0047] 3. The server automatically generates a list of survey questions

[0048] A list of survey questions is automatically generated based on the persona generated by the server.

[0049] Your research questionnaire will include questions that address the needs and challenges of your personas.

[0050] 4. Users conduct surveys

[0051] The user receives a list of survey questions provided by the server.

[0052] The user selects the survey format (online survey, interview, etc.).

[0053] The user conducts the survey in the selected format.

[0054] 5. The device aggregates and analyzes the results

[0055] The survey results are stored in a database.

[0056] The device aggregates and analyzes the survey results using a data analysis module.

[0057] The analysis results are visualized through the visualization module and provided to the user.

[0058] Specific examples

[0059] 1. User enters persona information

[0060] As an example, a user wants to conduct target market research for a new product.

[0061] The user enters the following persona information:

[0062] Age: 30

[0063] Gender: Female

[0064] Occupation: Marketing Manager

[0065] Nationality: Japanese

[0066] 2. The server automatically generates personas

[0067] Based on the information received by the server, the generation AI generates a persona.

[0068] The generated personas include the following details:

[0069] Name: Yamada Hanako (fictional character)

[0070] Hobbies: Fitness, cooking

[0071] Lifestyle: City work, active weekends

[0072] Values: Health-conscious, efficiency-oriented

[0073] Needs: Seeking efficient tools and services to balance work and personal life

[0074] Challenge: Busy work schedule and limited leisure time

[0075] 3. The server automatically generates a list of survey questions

[0076] A list of survey questions is automatically generated based on the persona generated by the server.

[0077] The survey questionnaire includes questions such as:

[0078] “How did you learn about new health tools and services?”

[0079] "How often do you devote time to fitness?"

[0080] "What applications and devices do you use to stay healthy efficiently?"

[0081] 4. Users conduct surveys

[0082] The user distributes a list of questions to target users in the form of an online survey.

[0083] The user collects the survey results and stores them in a database.

[0084] 5. The device aggregates and analyzes the results

[0085] The survey results aggregated across devices are analyzed using a data analysis module.

[0086] The analysis results are visualized in graphs and charts in the visualization module.

[0087] Users can gain further insights based on the results of this analysis.

[0088] In this way, this system makes it possible to conduct efficient user surveys that are multinational and multicultural at low cost.

[0089] The processing flow will be explained below.

[0090] Step 1:

[0091] A user logs in to the system.

[0092] The user accesses the persona information input screen.

[0093] Step 2:

[0094] The user enters the following persona information:

[0095] age

[0096] sex

[0097] Occupation

[0098] nationality

[0099] Other options (hobbies, lifestyle, etc.)

[0100] Step 3:

[0101] The user checks the input and clicks the "Submit" button.

[0102] Step 4:

[0103] The server receives the persona information sent by the user.

[0104] A generation AI module within the server analyzes the persona information.

[0105] Step 5:

[0106] The server generates a realistic persona profile based on the given information.

[0107] Name (fictitious)

[0108] Detailed profile (hobbies, lifestyle, values, etc.)

[0109] Estimating needs and challenges

[0110] Step 6:

[0111] The server stores the generated personas in a database.

[0112] Step 7:

[0113] Based on the persona generated by the server, a list of questions for user surveys is generated.

[0114] Selecting questions based on the persona's needs and challenges

[0115] Step 8:

[0116] The server provides a survey template along with a list of questions.

[0117] Step 9:

[0118] The terminal receives the question list and template provided by the server.

[0119] Step 10:

[0120] The user checks the list of questions provided by the terminal.

[0121] Step 11:

[0122] The user selects the survey format (online survey, interview, etc.).

[0123] Step 12:

[0124] The survey is conducted in a format selected by the user.

[0125] For online surveys, distribute the survey link to target users.

[0126] In the case of interviews, schedules will be arranged with the interviewees.

[0127] Step 13:

[0128] The device stores the survey results in a database.

[0129] Step 14:

[0130] The device aggregates the survey results using a data aggregation module.

[0131] Step 15:

[0132] The device generates graphs and charts to visualize the aggregated results.

[0133] Step 16:

[0134] The device analyzes the analysis results and extracts key insights.

[0135] Step 17:

[0136] The device provides the user with analysis results and insights in the form of a report.

[0137] The above are the specific processing steps of the system program.

[0138] Example 1

[0139] 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."

[0140] In modern market research, it is important to efficiently generate accurate personas based on user profile information and create survey question lists based on them. However, traditional methods require manually creating personas and building survey question lists, which is time-consuming and costly. Furthermore, the process of analyzing and visualizing survey results is cumbersome, making it difficult to make quick decisions.

[0141] 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.

[0142] In this invention, the server includes: means for inputting desired user profile information; means for automatically generating a persona using a generative AI model based on the user profile information; means for automatically generating a survey question list based on the generated persona; means for providing the survey question list to a user; means for the user to conduct a survey in the form of an online questionnaire or interview; means for aggregating and analyzing the survey results using a data analysis module; and means for visualizing the data analysis results using a visualization module and presenting them to the user. This enables the server to quickly and efficiently generate a persona and survey question list from user profile information, and then analyze, visualize, and provide the results. "User profile information" refers to attribute information entered by the user, such as age, gender, occupation, nationality, hobbies, and lifestyle.

[0143] A "generative AI model" is an artificial intelligence algorithm used to generate detailed personas from input user profile information.

[0144] A "persona" is a fictional user image that is automatically generated based on user profile information using a generative AI model.

[0145] A "survey question list" is a list of questions that are automatically generated based on the needs and challenges of the generated persona.

[0146] "Data Analysis Module" means a software module for analyzing aggregated survey results.

[0147] A "visualization module" is a software module for visually representing analyzed data.

[0148] An "online survey" is a form of survey conducted via the Internet.

[0149] The "interview format" is a survey format in which data is collected through interviews.

[0150] This invention is a system that automatically generates a persona using a generative AI model based on user profile information and automatically provides a survey question list based on that persona. This system can quickly and efficiently generate a persona and a survey question list from the profile information entered by the user, and then analyze and visualize the results before providing them.

[0151] Hardware and software used

[0152] The main hardware and software required to implement this system are:

[0153] 1. Server: A computer with a high-performance processor and a large amount of memory is required. For example, AWS (Amazon Web Services) or Google Cloud Platform can be used.

[0154] 2. Generative AI models: We need AI models that are specialized for natural language processing and generative tasks, such as OpenAI's GPT-4 model.

[0155] 3. Database: Used to store user profile information, generated personas, survey questions, and survey results. For example, a relational database such as PostgreSQL or MySQL can be used.

[0156] 4. Data analysis module: Survey results are compiled and analyzed using Python's Pandas library and statistical software R.

[0157] 5. Visualization module: Visualize data analysis results using tools such as Python's Matplotlib and Tableau.

[0158] Program processing overview

[0159] 1. User enters persona information

[0160] A user logs in to the system interface and accesses the persona information input screen, where they enter profile information such as age, gender, occupation, nationality, hobbies, and lifestyle, and then clicks the "Submit" button to send the information to the server.

[0161] 2. The server automatically generates personas

[0162] Based on the profile information received by the server, a detailed persona is generated using a generative AI model (e.g., GPT-4). This process uses the following prompt:

[0163] "We have received the following profile information from a user: Age: 30, Gender: Female, Occupation: Marketing Manager, Nationality: Japanese. Please generate a detailed persona based on this information. The persona should include name, hobbies, lifestyle, values, needs, and challenges."

[0164] The generated personas are stored in a database.

[0165] 3. The server automatically generates a list of survey questions

[0166] Based on the personas generated by the server, a generative AI model is used to automatically generate a list of survey questions, using the following prompts in the process:

[0167] “Generate a list of research questions that will work for this persona. Create questions based on their needs and challenges.”

[0168] The generated survey question list is stored on the server side and provided to the user.

[0169] 4. Users conduct surveys

[0170] Users obtain a list of survey questions provided by the system and conduct the survey in the form of an online questionnaire (e.g., Google Forms) or an interview. After the survey results are collected, they are stored in a database.

[0171] 5. The device aggregates and analyzes the results

[0172] The survey results are analyzed using a data analysis module, such as Python's Pandas library, to clean the data and perform statistical analysis. The analysis results are then visualized in graphs and charts using a visualization module (e.g., Tableau or Matplotlib) and provided to the user.

[0173] Specific examples

[0174] For example, consider a user conducting target market research for a new product. The user logs into the system and enters the following persona information: Age: 30, Gender: Female, Occupation: Marketing Manager, Nationality: Japanese. The server uses this information to generate the following detailed persona using a generative AI model:

[0175] Name: Yamada Hanako (fictional character)

[0176] Hobbies: Fitness, cooking

[0177] Lifestyle: City work, active weekends

[0178] Values: Health-conscious, efficiency-oriented

[0179] Needs: Seeking efficient tools and services to balance work and personal life

[0180] Challenge: Busy work schedule and limited leisure time

[0181] The server then generates the following list of survey questions based on this persona:

[0182] “How did you learn about new health tools and services?”

[0183] "How often do you devote time to fitness?"

[0184] "What applications and devices do you use to stay healthy efficiently?"

[0185] Users conduct surveys in the form of online questionnaires and collect the results. The terminal analyzes the survey results using the data analysis module and visualizes them in graphs and charts using the visualization module. The analysis results are provided to users to gain more specific market insights.

[0186] The above is a detailed description of an embodiment of this system.

[0187] The flow of the specific processing in the first embodiment will be described with reference to FIG. 11.

[0188] Step 1:

[0189] The user enters persona information. Specifically, the user logs in to the system interface, accesses the persona information input screen, and enters profile information such as age, gender, occupation, nationality, hobbies, and lifestyle into the form. After completing the input, the user clicks the "Submit" button to send the information to the server. The input data is sent to the server as the user's profile information.

[0190] Input: User persona information (age, gender, occupation, nationality, hobbies, lifestyle, etc.)

[0191] Output: User profile information data sent to the server

[0192] Step 2:

[0193] The server automatically generates a persona. The server receives profile information sent by the user. Based on this information, the server calls the generative AI model to create a prompt sentence and input it into the AI ​​model. The generative AI model generates a detailed persona based on the user's profile information and stores the generated persona data in the server's database.

[0194] Input: User profile information data

[0195] Output: Generated persona data (name, hobbies, lifestyle, values, needs, challenges, etc.)

[0196] Step 3:

[0197] The server automatically generates a list of survey questions. Based on the generated persona data, the server uses a generative AI model to generate a list of survey questions. This prompt sentence is created and input into the generative AI model to generate a list of questions. The generated list of survey questions is stored in the server's database.

[0198] Input: Generated persona data

[0199] Output: Generated survey questionnaire

[0200] Step 4:

[0201] The server provides the survey questionnaire to the user. The server provides a link or download option that the user can access and presents the survey questionnaire to the user. The user retrieves it and saves it in a usable form.

[0202] Input: Generated survey questionnaire list

[0203] Output: Survey questionnaire link or file provided to the user

[0204] Step 5:

[0205] The user conducts the survey. The user receives the survey question list provided by the server and conducts the survey in the form of an online questionnaire (e.g., Google Forms) or an interview. The collected data is then uploaded back to the server by the user.

[0206] Input: Survey Questionnaire

[0207] Output: Collected survey data

[0208] Step 6:

[0209] The server aggregates the survey results and performs analysis on the terminal. The server provides the survey data received from the user to the data analysis module, which aggregates and cleans the data and performs statistical analysis. The results are stored in a database.

[0210] Input: Collected survey data

[0211] Output: Aggregated and analyzed data

[0212] Step 7:

[0213] The terminal visualizes the aggregated and analyzed results, and the server presents them to the user. The terminal uses a visualization module to visualize the aggregated and analyzed data in graphs and charts, which the server presents to the user, providing the visualized results.

[0214] Input: Aggregated and analyzed data

[0215] Output: Visualized data (graphs and charts)

[0216] Based on the specific actions taken at each step and the input / output data, the system can quickly and effectively generate personas and survey question lists from user profile information, analyze and visualize the results, and provide them to users.

[0217] (Application example 1)

[0218] 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."

[0219] In recent years, formulating effective marketing and advertising strategies that take into account diverse user profiles has become difficult and requires a great deal of time and money. Furthermore, there is a lack of methods for accurately grasping the detailed needs and values ​​of target users and automatically designing optimal advertising content and formats based on that. This has led to problems such as advertisers being unable to quickly formulate appropriate strategies, resulting in reduced advertising effectiveness.

[0220] 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.

[0221] In this invention, the server includes a means for inputting desired user profile information, a means for automatically generating a persona based on the user profile information, and a means for automatically generating a survey question list based on the generated persona, thereby enabling a target effect.

[0222] By building a system that includes a means for optimizing advertising content and format based on the generated personas and survey question list, it becomes possible to quickly and low-costly develop effective advertising strategies that cater to diverse user profiles.

[0223] "User profile information" is a general term for data that includes attribute information about a specific user, such as age, sex, occupation, nationality, hobbies, and lifestyle.

[0224] A "persona" is a fictional image of a user that is generated based on user profile information and that reflects in detail the user's typical needs, values, behavioral patterns, and challenges.

[0225] A "survey questionnaire" is a list of questions that includes questions that address the needs, values, and challenges of users based on the generated persona.

[0226] "Means for optimizing advertising content and formats" refers to algorithms and tools that automatically design and select the most effective advertising messages and formats for target users based on the generated personas and survey question lists.

[0227] The "data analysis module" is a software component for aggregating and analyzing survey results, and analyzes data using methods such as statistical analysis and machine learning.

[0228] The "visualization module" is a software component for visually displaying the results of the analysis performed by the data analysis module, and visualizing them in the form of graphs, charts, etc.

[0229] A "generative AI model" is an AI model that automatically generates personas and survey question lists based on user profile information, and uses technologies such as natural language processing.

[0230] An "interface" is an input means or screen that allows a user to input information to a system, or a means for receiving output.

[0231] "Database" means a data storage system for storing and managing survey questionnaires and survey results.

[0232] This invention is a system that automatically generates personas based on user profile information using generation AI and automatically provides a list of survey questions based on the personas in order to conduct user surveys efficiently and at low cost. Advertising content and format can then be optimized based on the generated personas and survey questions. The program and processing required to realize this system are described below.

[0233] First, a user accesses the system and enters user profile information through an interface that is compatible with a variety of devices, including smartphones and head-mounted displays, and allows users to enter information such as age, gender, occupation, nationality, hobbies, and lifestyle.

[0234] The server automatically generates a persona based on the user profile information using a generative AI model (e.g., OpenAI's GPT-3). This persona includes details such as name, hobbies, lifestyle, values, needs, and challenges, creating a detailed image of the user.

[0235] The server then automatically generates a survey questionnaire based on the personas. This questionnaire contains specific questions that address the needs, values, and challenges of the target users. The survey questionnaire is then saved in a database for future reference.

[0236] Users can conduct surveys based on a list of survey questions provided by the server. The survey format can be selected, such as an online questionnaire or an interview. The resulting survey data is stored in a database.

[0237] The data analysis module then aggregates the survey data, and the visualization module visualizes the analyzed results, helping users gain insights into the data.

[0238] Furthermore, based on the generated personas and survey question list, the server provides a means to optimize advertising content and formats by using a generative AI model to automatically design and select the most effective advertising messages and formats.

[0239] Examples:

[0240] For example, let's say a marketer for a health food brand launching a new product uses this system to research the target market. The marketer would enter the following profile information:

[0241] Age: 30

[0242] Gender: Female

[0243] Occupation: Marketing Manager

[0244] Nationality: Japanese

[0245] Hobbies: Fitness, cooking

[0246] Based on this information, the generative AI model generates a persona like this:

[0247] Name: Yamada Hanako (fictional character)

[0248] Lifestyle: City work, active weekends

[0249] Values: Health-conscious, efficiency-oriented

[0250] Based on the persona, a list of survey questions is generated, such as:

[0251] “How did you learn about new health tools and services?”

[0252] "How often do you devote time to fitness?"

[0253] This information provides specific steps and results that generate optimal strategies for optimizing advertising content and formats.

[0254] Example prompt sentence:

[0255] Age: 30

[0256] Gender: Female

[0257] Occupation: Marketing Manager

[0258] Nationality: Japanese

[0259] Hobbies: Fitness, cooking

[0260] Based on the user profile information above, generate the following persona information:

[0261] name

[0262] hobby

[0263] Lifestyle

[0264] values

[0265] needs

[0266] assignment

[0267] In this way, by using this system, advertisers can efficiently and effectively optimize their targeted advertising.

[0268] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0269] Step 1:

[0270] A user logs in to the system and enters user profile information through the interface, including age, gender, occupation, nationality, hobbies, lifestyle, etc., so that the system can obtain the required profile data.

[0271] Input: User profile information (age, gender, occupation, nationality, hobbies, lifestyle)

[0272] Output: Retrieved user profile information

[0273] Step 2:

[0274] The server receives the acquired user profile information and passes it to a generative AI model to automatically generate a persona. The generative AI model (e.g., OpenAI's GPT-3) analyzes the input data and generates a persona. This persona includes a fictitious name, hobbies, lifestyle, values, needs, challenges, etc.

[0275] Input: User profile information

[0276] Output: Generated personas

[0277] Data processing: Persona generation using generative AI models

[0278] Step 3:

[0279] The server generates a list of survey questions based on the generated persona, and automatically creates specific survey questions based on the attributes, needs, and issues of the generated persona.

[0280] Input: Generated persona

[0281] Output: Survey Question List

[0282] Data Calculation: Question List Generation Based on Persona Information

[0283] Step 4:

[0284] The server provides the user with a list of survey questions, which are then stored in a database for future reference. The user then selects the survey format (online questionnaire, interview, etc.) based on the provided list of questions.

[0285] Input: Survey Questionnaire

[0286] Output: Provided survey question list

[0287] Specific operation: The user selects the survey format

[0288] Step 5:

[0289] Users conduct surveys in the format of their choice, and the survey results are stored in a database for later analysis.

[0290] Input: Survey response

[0291] Output: Saved survey results

[0292] Specific operation: Saving to the database

[0293] Step 6:

[0294] The server uses a data analysis module to analyze the collected survey results, which extracts user behavior patterns, needs, values, etc.

[0295] Input: Saved Survey Results

[0296] Output: Analysis results

[0297] Data calculations: Analysis of survey results

[0298] Step 7:

[0299] The server visualizes the analysis results through a visualization module, allowing users to receive the analysis results in a visually easy-to-understand format.

[0300] Input: Analysis results

[0301] Output: Visualized analysis results

[0302] Specific behavior: Generating graphs and charts

[0303] Step 8:

[0304] Based on the generated personas and survey question list, the server uses methods to optimize the advertising content and format, utilizing a generative AI model to automatically design and select the optimal advertising message and format for the target.

[0305] Input: Persona, survey question list, analysis results

[0306] Output: Optimized ad content and format

[0307] Data processing: Ad optimization with generative AI models

[0308] Through the above processing steps, this system can accommodate a variety of profiles and can formulate effective advertising strategies at low cost and efficiently.

[0309] 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.

[0310] MODE FOR CARRYING OUT THE INVENTION

[0311] This invention is a system that automatically generates personas using generative AI based on user profile information, and then combines it with an emotion engine that recognizes the user's emotions to provide a list of survey questions and analyze and visualize the survey results.

[0312] Program processing overview

[0313] 1. User enters persona information

[0314] The user logs in to the system and accesses the persona information input screen.

[0315] Users enter profile information such as age, gender, occupation, nationality, hobbies, and lifestyle.

[0316] After inputting, the user clicks the "Submit" button to send the information to the system.

[0317] 2. The server automatically generates personas

[0318] The server receives the profile information sent by the user.

[0319] The server's generation AI module analyzes the profile information and generates a detailed persona.

[0320] This persona includes name, hobbies, lifestyle, values, needs, challenges, etc.

[0321] The generated personas are stored in a database.

[0322] 3. The server automatically generates a list of survey questions

[0323] A list of survey questions is automatically generated based on the persona generated by the server.

[0324] Your research questionnaire will include questions that address the needs and challenges of your personas.

[0325] 4. The server utilizes the emotion engine

[0326] The server starts the emotion engine and collects the user's emotion data through the user's input information and interface operations.

[0327] The emotional data includes the user's emotional state, such as happiness, sadness, or anger.

[0328] 5. The server uses emotion data

[0329] The server analyzes the collected emotional data and further refines the generated persona profile.

[0330] The emotional data is reflected in the questions in the survey questionnaire.

[0331] 6. Users conduct surveys

[0332] The user receives a list of survey questions provided by the server.

[0333] The user selects the survey format (online survey, interview, etc.).

[0334] The user conducts the survey in the selected format.

[0335] 7. The device aggregates and analyzes the results

[0336] The survey results are stored in a database.

[0337] The device aggregates and analyzes the survey results using a data analysis module.

[0338] The analysis results are visualized through the visualization module and provided to the user.

[0339] Specific examples

[0340] 1. User enters persona information

[0341] As an example, a user wants to conduct target market research for a new product.

[0342] The user enters the following persona information:

[0343] Age: 30

[0344] Gender: Female

[0345] Occupation: Marketing Manager

[0346] Nationality: Japanese

[0347] 2. The server automatically generates personas

[0348] The generation AI generates a persona based on the information received by the server.

[0349] The generated personas include the following details:

[0350] Name: Yamada Hanako (fictional character)

[0351] Hobbies: Fitness, cooking

[0352] Lifestyle: City work, active weekends

[0353] Values: Health-conscious, efficiency-oriented

[0354] Needs: Seeking efficient tools and services to balance work and personal life

[0355] Challenge: Busy work schedule and limited leisure time

[0356] 3. The server automatically generates a list of survey questions

[0357] A list of survey questions is automatically generated based on the persona generated by the server.

[0358] The survey questionnaire includes questions such as:

[0359] “How did you learn about new health tools and services?”

[0360] "How often do you devote time to fitness?"

[0361] "What applications and devices do you use to stay healthy efficiently?"

[0362] 4. The server utilizes the emotion engine

[0363] The server starts the emotion engine and collects emotion data based on the user's input information.

[0364] For example, it is analyzed whether the user feels stressed while answering a questionnaire.

[0365] 5. The server uses emotion data

[0366] The server analyzes the emotional data and further refines the persona profile.

[0367] Incorporate sentiment data into your survey question list and tailor questions accordingly.

[0368] 6. Users conduct surveys

[0369] The user distributes a list of questions to target users in the form of an online survey.

[0370] The user collects the survey results and stores them in a database.

[0371] 7. The device aggregates and analyzes the results

[0372] The survey results aggregated across devices are analyzed using a data analysis module.

[0373] The analysis results are visualized in graphs and charts in the visualization module.

[0374] Users can gain further insights based on the results of this analysis.

[0375] As described above, by utilizing the emotion engine, this system can obtain deeper insights and conduct efficient user surveys that are multinational and multicultural at low cost.

[0376] The processing flow will be explained below.

[0377] Step 1:

[0378] A user logs in to the system.

[0379] The user accesses the persona information input screen.

[0380] Step 2:

[0381] The user enters the following persona information:

[0382] age

[0383] sex

[0384] Occupation

[0385] nationality

[0386] Other options (hobbies, lifestyle, etc.)

[0387] Step 3:

[0388] The user checks the input and clicks the "Submit" button.

[0389] Step 4:

[0390] The server receives the persona information sent by the user.

[0391] A generation AI module within the server analyzes the persona information.

[0392] Step 5:

[0393] The server generates a realistic persona profile based on the given information.

[0394] Name (fictitious)

[0395] Detailed profile (hobbies, lifestyle, values, etc.)

[0396] Estimating needs and challenges

[0397] Step 6:

[0398] The server stores the generated personas in a database.

[0399] Step 7:

[0400] Based on the persona generated by the server, a list of questions for user surveys is generated.

[0401] Selecting questions based on the persona's needs and challenges

[0402] Step 8:

[0403] The server starts the emotion engine and collects the user's emotion data through the user's input information and interface operations.

[0404] The emotion data includes the user's happiness, sadness, anger, etc.

[0405] Step 9:

[0406] The server analyzes the emotional data and further refines the generated persona profile.

[0407] For example, if a user is feeling stressed, add that emotion to the persona's characteristics.

[0408] Step 10:

[0409] The server adjusts the questions in the survey questionnaire based on the emotional data.

[0410] Add or change emotion-based questions.

[0411] Step 11:

[0412] The server provides a survey template along with a list of survey questions.

[0413] The terminal receives the question list and template provided by the server.

[0414] Step 12:

[0415] The user checks the list of questions provided by the terminal.

[0416] The user selects the survey format (online survey, interview, etc.).

[0417] Step 13:

[0418] The survey is conducted in a format selected by the user.

[0419] For online surveys, distribute the survey link to target users.

[0420] In the case of interviews, schedules will be arranged with the interviewees.

[0421] Step 14:

[0422] The device stores the survey results in a database.

[0423] The device aggregates the survey results using a data aggregation module.

[0424] Step 15:

[0425] The device generates graphs and charts to visualize the aggregated results.

[0426] Step 16:

[0427] The device analyzes the analysis results and extracts key insights.

[0428] Step 17:

[0429] The device provides the user with analysis results and insights in the form of a report.

[0430] The above are the specific processing steps of the system program.

[0431] Example 2

[0432] 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."

[0433] Conventional survey systems have had difficulty efficiently completing the process from inputting user profile information to generating personas, creating survey question lists, and compiling and analyzing survey results. In particular, there were no systems that could refine personas and survey questions by taking user emotional data into account, which led to problems with reduced survey accuracy and reliability.

[0434] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting desired user profile information, a means for automatically generating a persona based on the user profile information, a means for automatically generating a survey question list based on the generated persona, a means for collecting emotional data from user input information and operation data, a means for analyzing the emotional data and refining the profile of the generated persona, a means for aggregating and analyzing the survey results, and a means for visualizing the analysis results and presenting them to the user. This solves the conventional problems and enables highly accurate surveys that take user emotional data into consideration.

[0435] "User profile information" is information about personal attributes and characteristics such as the user's age, sex, occupation, nationality, hobbies, and lifestyle.

[0436] A "persona" is a fictional character created based on user profile information, including a detailed profile, values, needs, and challenges.

[0437] "Generative AI" refers to algorithms and systems that use artificial intelligence to automatically generate personas and survey question lists.

[0438] A "survey questionnaire" is a list of questions that are automatically generated to address the needs and challenges of a persona.

[0439] "Emotion data" refers to data relating to emotional states such as joy, sadness, and anger, which are collected through user input information and interface operations.

[0440] A "database" is a system for storing and managing data such as persona information, survey question lists, and survey results.

[0441] "Data analysis module" refers to the functions and software that aggregate and analyze survey results to gain useful insights.

[0442] A "visualization module" is a function or tool for visually displaying the results of data analysis, using graphs, charts, etc.

[0443] An "emotion engine" is an algorithm or system that analyzes user input information and behavioral data to detect and evaluate emotional states.

[0444] An "interface" is a screen, form, or other means by which a user inputs information into a system.

[0445] MODE FOR CARRYING OUT THE INVENTION

[0446] This system uses a generative AI to automatically generate a persona based on user profile information, and then combines it with an emotion engine that recognizes the user's emotions to provide a list of survey questions and analyze and visualize the survey results. A specific embodiment is described below.

[0447] System Configuration

[0448] 1. User enters persona information

[0449] Users log in to the system using devices such as PCs or smartphones.

[0450] Users enter profile information such as age, gender, occupation, nationality, hobbies, and lifestyle on a dedicated persona information input screen.

[0451] For example, enter information such as "Age: 30," "Gender: Female," "Occupation: Marketing Manager," "Nationality: Japanese," "Hobbies: Fitness," and "Lifestyle: Works in an urban area and spends weekends actively," and click the "Submit" button to send the information to the server.

[0452] 2. The server automatically generates personas

[0453] The server receives the profile information sent by the user.

[0454] A generative AI module installed on the server analyzes the input information and generates a detailed persona.

[0455] The generated persona will include the following elements:

[0456] Name (e.g. "Yamada Hanako")

[0457] Hobbies (fitness, cooking)

[0458] Lifestyle (working in the city and being active on the weekends)

[0459] Values ​​(health-conscious, efficiency-oriented)

[0460] Needs (efficient tools and services to balance work and personal life)

[0461] Challenges (busy work schedule and limited leisure time)

[0462] The server stores the generated persona information in a database.

[0463] 3. The server automatically generates a list of survey questions

[0464] The server automatically generates a list of survey questions based on the persona information stored in the database.

[0465] The prompt sent to the generative AI is, "Create effective survey questions relevant to this persona."

[0466] Specific questions include:

[0467] “How did you learn about new health tools and services?”

[0468] "How often do you devote time to fitness?"

[0469] "What applications and devices do you use to stay healthy efficiently?"

[0470] The server stores the automatically generated survey questionnaire in a database.

[0471] 4. The server utilizes the emotion engine

[0472] The server starts the emotion engine and collects user input information and data from interface operations.

[0473] The data collected includes the user's emotional state, such as happiness, sadness, or anger.

[0474] For example, emotional data is analyzed based on facial expressions, tone of voice, click behavior, etc. when a user answers a question.

[0475] 5. The server uses emotion data

[0476] The server analyzes the collected emotional data to further refine the persona profile.

[0477] The emotional data is reflected in the survey questions, and the questions in the survey list are adjusted accordingly. For example, questions that are likely to cause stress to users are softened.

[0478] 6. Users conduct surveys

[0479] The user reviews the list of survey questions provided by the server.

[0480] Users choose an appropriate format to conduct the survey, such as an online survey or an interview.

[0481] Conduct a survey in the format of your choice and set up the system to store each response in a database.

[0482] 7. The device aggregates and analyzes the results

[0483] The survey results are compiled into a database.

[0484] The terminal launches a data analysis module to analyze the aggregated results.

[0485] The analyzed data can be visualized as follows:

[0486] Use graphs, charts, and other visualization techniques.

[0487] Users gain new insights based on the visualized results.

[0488] Prompt Sentence Examples

[0489] "A 30-year-old female marketing manager is looking for a new health tool. Please generate a persona for her."

[0490] "Generate personas based on the user's age (30 years old), gender (female), occupation (marketing manager), and nationality (Japanese), and create a list of relevant survey questions."

[0491] In this way, the system combines a generative AI model with an emotion engine to enable efficient and in-depth user research.

[0492] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0493] System program processing flow

[0494] Step 1:

[0495] User enters persona information

[0496] Input: Users log in to the system using a device such as a PC or smartphone and enter profile information such as age, gender, occupation, nationality, hobbies, and lifestyle on the persona information input screen.

[0497] Data processing: The entered information is temporarily stored in the device's memory and any necessary validation (e.g., checking the input format and required fields) is performed before transmission.

[0498] Output: When the user clicks the "Submit" button, the input information is sent to the server. Examples include "Age: 30," "Gender: Female," "Occupation: Marketing Manager," "Nationality: Japanese," "Hobbies: Fitness," and "Lifestyle: Works in a city and spends weekends actively."

[0499] Step 2:

[0500] The server automatically generates a persona

[0501] Input: The server receives the profile information sent by the user and temporarily stores it in a database.

[0502] Data calculation: The generative AI module installed on the server analyzes the received information. During the analysis, statistical and machine learning models are used to extract user characteristics based on the input data, and a persona is generated.

[0503] Output: The generated persona information is saved in a database. The generated persona includes information such as name (e.g., Hanako Yamada), hobbies (fitness, cooking), lifestyle (works in an urban area, spends weekends actively), values ​​(health-conscious, emphasizes efficiency), needs (efficient tools and services for balancing work and personal life), and challenges (busy work, limited leisure time).

[0504] Step 3:

[0505] The server automatically generates a list of survey questions.

[0506] Input: The server retrieves the persona information stored in the database.

[0507] Data calculation: The server sends a prompt to the generation AI module, saying, "Please create effective survey questions related to this persona." The generation AI analyzes the persona information and generates a corresponding list of survey questions.

[0508] Output: The server stores the automatically generated survey questions in a database. Specific example questions include, "How do you learn about new health tools and services?", "How often do you devote time to fitness?", and "What applications and devices do you use to effectively maintain your health?"

[0509] Step 4:

[0510] The server utilizes the emotion engine

[0511] Input: Collects user input and interface interaction data, such as facial expressions, tone of voice, and click behavior when answering questions.

[0512] Data calculation: The emotion engine analyzes the collected data and detects the user's emotional state (happiness, sadness, anger, etc.).

[0513] Output: The detected emotion data is stored in a database for further analysis and profile refinement.

[0514] Step 5:

[0515] The server uses the emotion data

[0516] Input: Obtain emotional data and persona information stored in the database.

[0517] Data calculation: The persona profile is further refined based on emotional data. For example, questions that are likely to cause stress to the user may be softened, and the content and order of questions may be adjusted according to the user's emotions.

[0518] Output: Updated personas and survey question list saved to database.

[0519] Step 6:

[0520] A user conducts a survey

[0521] Input: Get the latest list of survey questions provided by the server.

[0522] Specific operation: The user selects an appropriate survey format (such as an online questionnaire or interview) and conducts the survey with target users. The survey questions are displayed on the user's device, and the user enters the answers.

[0523] Output: Each answer is saved in a database in real time.

[0524] Step 7:

[0525] The device aggregates and analyzes the results

[0526] Input: Retrieve survey results stored in the database.

[0527] Data calculation: The data analysis module installed on the device aggregates and analyzes the survey results. Analysis methods include statistical analysis and text mining.

[0528] Output: The analysis results are visualized through the visualization module. The results are displayed in graphs, charts, etc. and provided to the user, allowing the user to gain new insights.

[0529] As described above, this system realizes efficient and precise user research through each processing step.

[0530] (Application example 2)

[0531] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0532] Many modern commercial systems struggle to provide personalized services that accurately reflect customer emotions and behavioral characteristics. This results in low customer satisfaction and a lack of improvement in user experience. Furthermore, conventional systems are unable to accurately analyze survey results by analyzing user emotions and respond to diverse customer needs. A method to resolve these issues and provide more effective and personalized customer service is needed.

[0533] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting desired user profile information, means for automatically generating a persona based on the user profile information, means for automatically generating a survey question list based on the generated persona, means for providing the survey question list to the user, means for the user to conduct the survey, means for further refining the survey results using an emotion engine that analyzes the user's emotions, means for aggregating and analyzing the survey results, and means for visualizing the analysis results and presenting them to the user. This makes it possible to analyze user emotion data and refine the survey results. This makes it possible to provide more personalized and effective customer service and improve customer satisfaction.

[0534] "User profile information" refers to personal information such as a user's age, gender, occupation, hobbies, and lifestyle.

[0535] A "persona" is a virtual image of a user that is generated based on user profile information, and includes the user's name, hobbies, lifestyle, values, needs, challenges, etc.

[0536] "Survey Question List" refers to a list of questions that are automatically generated based on the generated personas.

[0537] An "emotion engine" refers to technology that analyzes a user's facial expressions, tone of voice, text input, etc., to recognize emotional states such as joy, sadness, and anger in real time.

[0538] "Aggregation" refers to the compilation of survey results obtained from users.

[0539] "Analysis" refers to the analysis of aggregated data.

[0540] "Visualization" refers to presenting analytical results in a visual format such as graphs or charts and providing them to users.

[0541] "Interface" refers to the means by which a user inputs information into or receives information from a system.

[0542] "Generative AI" refers to artificial intelligence technology that automatically generates content and information based on given data.

[0543] A "database" refers to a system for storing and managing information in an organized manner.

[0544] "Data Analysis Module" refers to a software component for analyzing collected data.

[0545] "Visualization Module" refers to a software component for visually representing the results of data analysis.

[0546] MODE FOR CARRYING OUT THE INVENTION

[0547] This invention is a system that automatically generates personas based on user profile information, incorporates an emotion engine that recognizes user emotions, provides a survey question list, and analyzes and visualizes survey results. The system of this invention operates based on information entered by the user.

[0548] Program Generation

[0549] The server executes the system program according to the following procedure.

[0550] A natural language description of what the program does

[0551] 1. Enter your user profile information:

[0552] Users log in from a device such as a smartphone or tablet and enter profile information such as their name, age, gender, occupation, hobbies, and lifestyle. This information is accepted through the interface and sent to the server.

[0553] 2. Automatic persona generation:

[0554] Based on the received user profile information, the server uses a generative AI model (e.g., GPT-3) to automatically generate a detailed persona, which includes detailed information about the user's hobbies, lifestyle, values, and needs.

[0555] 3. Automatic generation of survey questions:

[0556] The server automatically generates a list of survey questions based on the persona. For example, if the persona is a "30-year-old woman interested in fitness," a survey list containing fitness-related questions will be generated.

[0557] 4. Leveraging the Emotion Engine:

[0558] During the survey, the server activates an emotion engine to collect real-time emotional data from the user's facial expressions and tone of voice, allowing analysis of the emotional state in which the user provided their answers.

[0559] 5. Elaboration and analysis of findings:

[0560] The server uses the collected emotional data to further refine the generated persona information. It also reflects the emotional data in the questions in the survey questionnaire to make more appropriate adjustments. Once the survey is complete, the user's responses are stored in a database and analyzed by the data analysis module.

[0561] 6. Visualizing the results:

[0562] The analysis results are visualized in the form of graphs and charts through the visualization module and provided to users, allowing them to intuitively understand the survey results.

[0563] Specific examples

[0564] For example, a user enters the following profile information:

[0565] Age: 35

[0566] Gender: Male

[0567] Occupation: Software Engineer

[0568] Hobbies: Reading, games

[0569] Lifestyle: Urban areas, many work from home

[0570] An example of a prompt for a generative AI model is:

[0571] "35-year-old male, software engineer, hobbies include reading and gaming, lifestyle is urban, mostly working from home. Please generate a persona based on this information."

[0572] The persona generated in this way can depict a specific character, such as "an engineer who loves games and places importance on ways to relax while working from home." A list of fitness and relaxation-related survey questions is automatically generated based on this persona, and an emotion engine is used to collect and analyze emotional data in real time, allowing for a personalized experience tailored to the user.

[0573] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0574] Step 1:

[0575] A user logs in from a device such as a smartphone or tablet and enters profile information such as name, age, gender, occupation, hobbies, and lifestyle. The entered information is sent to the server through the interface, allowing the server to receive (input) the user profile information. Specifically, the user enters information into a form and presses the "Submit" button.

[0576] Step 2:

[0577] The server automatically generates a detailed persona using a generative AI model (e.g., GPT-3) based on the received user profile information. At this time, it analyzes the input profile information and provides a prompt to the generative AI model (data processing). For example, if the profile information is "35-year-old male, software engineer, hobbies are reading and gaming," the prompt would be "35-year-old male, software engineer, hobbies are reading and gaming. Please generate a persona based on this information." The generated persona is returned to the device (output).

[0578] Step 3:

[0579] The server automatically generates a list of survey questions based on the generated persona. At this time, it analyzes the information of the generated persona and generates questions that suit the persona's tastes and needs (data calculation). For example, if the persona is a "30-year-old woman who is interested in fitness," a list including fitness-related questions will be generated. The generated list of survey questions is provided to the terminal (output).

[0580] Step 4:

[0581] The user receives a list of survey questions provided by the terminal. The user conducts the survey based on the list and answers the questions. At this time, the user's answers are sent (input) to the server through the interface. Specifically, the user answers the questions in a questionnaire format and presses the "send" button.

[0582] Step 5:

[0583] The server activates the emotion engine and collects emotional data in real time from the user's facial expressions and tone of voice. The server recognizes the user's emotional state based on the user's input information and interface operations, and stores the analysis results in a database (data calculation). For example, the server collects and analyzes the user's facial expressions and tone of voice when answering questions using a camera or microphone.

[0584] Step 6:

[0585] The server uses the collected emotional data to further refine the generated persona information. Furthermore, it reflects the emotional data in the questions in the survey questionnaire to make more appropriate adjustments (data calculation). For example, if the user is feeling stressed, it changes the questions in the questionnaire to reflect that emotion more gently. The refined persona and the adjusted questionnaire are saved in the database (output).

[0586] Step 7:

[0587] Once the survey is complete, the server collects the responses from users and stores them in a database (input). The server then analyzes the responses using a data analysis module, analyzing the aggregated data and performing statistical processing (data calculations). For example, the response data may be aggregated and analyzed for trends and patterns.

[0588] Step 8:

[0589] The server uses a visualization module to visualize the analysis results. At this time, the analysis results are converted into visual formats such as graphs and charts and provided to the user (output). Specifically, the analysis results are converted into diagrams and charts, and dashboards and reports are generated. This allows the user to intuitively understand the investigation results.

[0590] 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.

[0591] 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.

[0592] 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.

[0593] [Second embodiment]

[0594] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0595] 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.

[0596] 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).

[0597] 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.

[0598] 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.

[0599] 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).

[0600] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0601] 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.

[0602] 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.

[0603] 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.

[0604] 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.

[0605] 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."

[0606] MODE FOR CARRYING OUT THE INVENTION

[0607] This invention is a system that automatically generates personas using generation AI based on user profile information and automatically provides a list of survey questions based on those personas in order to conduct user surveys efficiently and at low cost.

[0608] Program processing overview

[0609] 1. User enters persona information

[0610] The user logs in to the system and accesses the persona information input screen.

[0611] Users enter profile information such as age, gender, occupation, nationality, hobbies, and lifestyle.

[0612] After inputting, the user clicks the "Submit" button to send the information to the system.

[0613] 2. The server automatically generates personas

[0614] The server receives the profile information sent by the user.

[0615] The server's generation AI analyzes the profile information and generates a detailed persona.

[0616] This persona includes name, hobbies, lifestyle, values, needs, challenges, etc.

[0617] The generated personas are stored in a database.

[0618] 3. The server automatically generates a list of survey questions

[0619] A list of survey questions is automatically generated based on the persona generated by the server.

[0620] Your research questionnaire will include questions that address the needs and challenges of your personas.

[0621] 4. Users conduct surveys

[0622] The user receives a list of survey questions provided by the server.

[0623] The user selects the survey format (online survey, interview, etc.).

[0624] The user conducts the survey in the selected format.

[0625] 5. The device aggregates and analyzes the results

[0626] The survey results are stored in a database.

[0627] The device aggregates and analyzes the survey results using a data analysis module.

[0628] The analysis results are visualized through the visualization module and provided to the user.

[0629] Specific examples

[0630] 1. User enters persona information

[0631] As an example, a user wants to conduct target market research for a new product.

[0632] The user enters the following persona information:

[0633] Age: 30

[0634] Gender: Female

[0635] Occupation: Marketing Manager

[0636] Nationality: Japanese

[0637] 2. The server automatically generates personas

[0638] Based on the information received by the server, the generation AI generates a persona.

[0639] The generated personas include the following details:

[0640] Name: Yamada Hanako (fictional character)

[0641] Hobbies: Fitness, cooking

[0642] Lifestyle: City work, active weekends

[0643] Values: Health-conscious, efficiency-oriented

[0644] Needs: Seeking efficient tools and services to balance work and personal life

[0645] Challenge: Busy work schedule and limited leisure time

[0646] 3. The server automatically generates a list of survey questions

[0647] A list of survey questions is automatically generated based on the persona generated by the server.

[0648] The survey questionnaire includes questions such as:

[0649] “How did you learn about new health tools and services?”

[0650] "How often do you devote time to fitness?"

[0651] "What applications and devices do you use to stay healthy efficiently?"

[0652] 4. Users conduct surveys

[0653] The user distributes a list of questions to target users in the form of an online survey.

[0654] The user collects the survey results and stores them in a database.

[0655] 5. The device aggregates and analyzes the results

[0656] The survey results aggregated across devices are analyzed using a data analysis module.

[0657] The analysis results are visualized in graphs and charts in the visualization module.

[0658] Users can gain further insights based on the results of this analysis.

[0659] In this way, this system makes it possible to conduct efficient user surveys that are multinational and multicultural at low cost.

[0660] The processing flow will be explained below.

[0661] Step 1:

[0662] A user logs in to the system.

[0663] The user accesses the persona information input screen.

[0664] Step 2:

[0665] The user enters the following persona information:

[0666] age

[0667] sex

[0668] Occupation

[0669] nationality

[0670] Other options (hobbies, lifestyle, etc.)

[0671] Step 3:

[0672] The user checks the input and clicks the "Submit" button.

[0673] Step 4:

[0674] The server receives the persona information sent by the user.

[0675] A generation AI module within the server analyzes the persona information.

[0676] Step 5:

[0677] The server generates a realistic persona profile based on the given information.

[0678] Name (fictitious)

[0679] Detailed profile (hobbies, lifestyle, values, etc.)

[0680] Estimating needs and challenges

[0681] Step 6:

[0682] The server stores the generated personas in a database.

[0683] Step 7:

[0684] Based on the persona generated by the server, a list of questions for user surveys is generated.

[0685] Selecting questions based on the persona's needs and challenges

[0686] Step 8:

[0687] The server provides a survey template along with a list of questions.

[0688] Step 9:

[0689] The terminal receives the question list and template provided by the server.

[0690] Step 10:

[0691] The user checks the list of questions provided by the terminal.

[0692] Step 11:

[0693] The user selects the survey format (online survey, interview, etc.).

[0694] Step 12:

[0695] The survey is conducted in a format selected by the user.

[0696] For online surveys, distribute the survey link to target users.

[0697] In the case of interviews, schedules will be arranged with the interviewees.

[0698] Step 13:

[0699] The device stores the survey results in a database.

[0700] Step 14:

[0701] The device aggregates the survey results using a data aggregation module.

[0702] Step 15:

[0703] The device generates graphs and charts to visualize the aggregated results.

[0704] Step 16:

[0705] The device analyzes the analysis results and extracts key insights.

[0706] Step 17:

[0707] The device provides the user with analysis results and insights in the form of a report.

[0708] The above are the specific processing steps of the system program.

[0709] Example 1

[0710] 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."

[0711] In modern market research, it is important to efficiently generate accurate personas based on user profile information and create survey question lists based on them. However, traditional methods require manually creating personas and building survey question lists, which is time-consuming and costly. Furthermore, the process of analyzing and visualizing survey results is cumbersome, making it difficult to make quick decisions.

[0712] 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.

[0713] In this invention, the server includes: means for inputting desired user profile information; means for automatically generating a persona using a generative AI model based on the user profile information; means for automatically generating a survey question list based on the generated persona; means for providing the survey question list to a user; means for the user to conduct a survey in the form of an online questionnaire or interview; means for aggregating and analyzing the survey results using a data analysis module; and means for visualizing the data analysis results using a visualization module and presenting them to the user. This enables the server to quickly and efficiently generate a persona and survey question list from user profile information, and then analyze, visualize, and provide the results. "User profile information" refers to attribute information entered by the user, such as age, gender, occupation, nationality, hobbies, and lifestyle.

[0714] A "generative AI model" is an artificial intelligence algorithm used to generate detailed personas from input user profile information.

[0715] A "persona" is a fictional user image that is automatically generated based on user profile information using a generative AI model.

[0716] A "survey question list" is a list of questions that are automatically generated based on the needs and challenges of the generated persona.

[0717] "Data Analysis Module" means a software module for analyzing aggregated survey results.

[0718] A "visualization module" is a software module for visually representing analyzed data.

[0719] An "online survey" is a form of survey conducted via the Internet.

[0720] The "interview format" is a survey format in which data is collected through interviews.

[0721] This invention is a system that automatically generates a persona using a generative AI model based on user profile information and automatically provides a survey question list based on that persona. This system can quickly and efficiently generate a persona and a survey question list from the profile information entered by the user, and then analyze and visualize the results before providing them.

[0722] Hardware and software used

[0723] The main hardware and software required to implement this system are:

[0724] 1. Server: A computer with a high-performance processor and a large amount of memory is required. For example, AWS (Amazon Web Services) or Google Cloud Platform can be used.

[0725] 2. Generative AI models: We need AI models that are specialized for natural language processing and generative tasks, such as OpenAI's GPT-4 model.

[0726] 3. Database: Used to store user profile information, generated personas, survey questions, and survey results. For example, a relational database such as PostgreSQL or MySQL can be used.

[0727] 4. Data analysis module: Survey results are compiled and analyzed using Python's Pandas library and statistical software R.

[0728] 5. Visualization module: Visualize data analysis results using tools such as Python's Matplotlib and Tableau.

[0729] Program processing overview

[0730] 1. User enters persona information

[0731] A user logs in to the system interface and accesses the persona information input screen, where they enter profile information such as age, gender, occupation, nationality, hobbies, and lifestyle, and then clicks the "Submit" button to send the information to the server.

[0732] 2. The server automatically generates personas

[0733] Based on the profile information received by the server, a detailed persona is generated using a generative AI model (e.g., GPT-4). This process uses the following prompt:

[0734] "We have received the following profile information from a user: Age: 30, Gender: Female, Occupation: Marketing Manager, Nationality: Japanese. Please generate a detailed persona based on this information. The persona should include name, hobbies, lifestyle, values, needs, and challenges."

[0735] The generated personas are stored in a database.

[0736] 3. The server automatically generates a list of survey questions

[0737] Based on the personas generated by the server, a generative AI model is used to automatically generate a list of survey questions, using the following prompts in the process:

[0738] “Generate a list of research questions that will work for this persona. Create questions based on their needs and challenges.”

[0739] The generated survey question list is stored on the server side and provided to the user.

[0740] 4. Users conduct surveys

[0741] Users obtain a list of survey questions provided by the system and conduct the survey in the form of an online questionnaire (e.g., Google Forms) or an interview. After the survey results are collected, they are stored in a database.

[0742] 5. The device aggregates and analyzes the results

[0743] The survey results are analyzed using a data analysis module, such as Python's Pandas library, to clean the data and perform statistical analysis. The analysis results are then visualized in graphs and charts using a visualization module (e.g., Tableau or Matplotlib) and provided to the user.

[0744] Specific examples

[0745] For example, consider a user conducting target market research for a new product. The user logs into the system and enters the following persona information: Age: 30, Gender: Female, Occupation: Marketing Manager, Nationality: Japanese. The server uses this information to generate the following detailed persona using a generative AI model:

[0746] Name: Yamada Hanako (fictional character)

[0747] Hobbies: Fitness, cooking

[0748] Lifestyle: City work, active weekends

[0749] Values: Health-conscious, efficiency-oriented

[0750] Needs: Seeking efficient tools and services to balance work and personal life

[0751] Challenge: Busy work schedule and limited leisure time

[0752] The server then generates the following list of survey questions based on this persona:

[0753] “How did you learn about new health tools and services?”

[0754] "How often do you devote time to fitness?"

[0755] "What applications and devices do you use to stay healthy efficiently?"

[0756] Users conduct surveys in the form of online questionnaires and collect the results. The terminal analyzes the survey results using the data analysis module and visualizes them in graphs and charts using the visualization module. The analysis results are provided to users to gain more specific market insights.

[0757] The above is a detailed description of an embodiment of this system.

[0758] The flow of the specific processing in the first embodiment will be described with reference to FIG. 11.

[0759] Step 1:

[0760] The user enters persona information. Specifically, the user logs in to the system interface, accesses the persona information input screen, and enters profile information such as age, gender, occupation, nationality, hobbies, and lifestyle into the form. After completing the input, the user clicks the "Submit" button to send the information to the server. The input data is sent to the server as the user's profile information.

[0761] Input: User persona information (age, gender, occupation, nationality, hobbies, lifestyle, etc.)

[0762] Output: User profile information data sent to the server

[0763] Step 2:

[0764] The server automatically generates a persona. The server receives profile information sent by the user. Based on this information, the server calls the generative AI model to create a prompt sentence and input it into the AI ​​model. The generative AI model generates a detailed persona based on the user's profile information and stores the generated persona data in the server's database.

[0765] Input: User profile information data

[0766] Output: Generated persona data (name, hobbies, lifestyle, values, needs, challenges, etc.)

[0767] Step 3:

[0768] The server automatically generates a list of survey questions. Based on the generated persona data, the server uses a generative AI model to generate a list of survey questions. This prompt sentence is created and input into the generative AI model to generate a list of questions. The generated list of survey questions is stored in the server's database.

[0769] Input: Generated persona data

[0770] Output: Generated survey questionnaire

[0771] Step 4:

[0772] The server provides the survey questionnaire to the user. The server provides a link or download option that the user can access and presents the survey questionnaire to the user. The user retrieves it and saves it in a usable form.

[0773] Input: Generated survey questionnaire list

[0774] Output: Survey questionnaire link or file provided to the user

[0775] Step 5:

[0776] The user conducts the survey. The user receives the survey question list provided by the server and conducts the survey in the form of an online questionnaire (e.g., Google Forms) or an interview. The collected data is then uploaded back to the server by the user.

[0777] Input: Survey Questionnaire

[0778] Output: Collected survey data

[0779] Step 6:

[0780] The server aggregates the survey results and performs analysis on the terminal. The server provides the survey data received from the user to the data analysis module, which aggregates and cleans the data and performs statistical analysis. The results are stored in a database.

[0781] Input: Collected survey data

[0782] Output: Aggregated and analyzed data

[0783] Step 7:

[0784] The terminal visualizes the aggregated and analyzed results, and the server presents them to the user. The terminal uses a visualization module to visualize the aggregated and analyzed data in graphs and charts, which the server presents to the user, providing the visualized results.

[0785] Input: Aggregated and analyzed data

[0786] Output: Visualized data (graphs and charts)

[0787] Based on the specific actions taken at each step and the input / output data, the system can quickly and effectively generate personas and survey question lists from user profile information, analyze and visualize the results, and provide them to users.

[0788] (Application example 1)

[0789] 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."

[0790] In recent years, formulating effective marketing and advertising strategies that take into account diverse user profiles has become difficult and requires a great deal of time and money. Furthermore, there is a lack of methods for accurately grasping the detailed needs and values ​​of target users and automatically designing optimal advertising content and formats based on that. This has led to problems such as advertisers being unable to quickly formulate appropriate strategies, resulting in reduced advertising effectiveness.

[0791] 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.

[0792] In this invention, the server includes a means for inputting desired user profile information, a means for automatically generating a persona based on the user profile information, and a means for automatically generating a survey question list based on the generated persona, thereby enabling a target effect.

[0793] By building a system that includes a means for optimizing advertising content and format based on the generated personas and survey question list, it becomes possible to quickly and low-costly develop effective advertising strategies that cater to diverse user profiles.

[0794] "User profile information" is a general term for data that includes attribute information about a specific user, such as age, sex, occupation, nationality, hobbies, and lifestyle.

[0795] A "persona" is a fictional image of a user that is generated based on user profile information and that reflects in detail the user's typical needs, values, behavioral patterns, and challenges.

[0796] A "survey questionnaire" is a list of questions that includes questions that address the needs, values, and challenges of users based on the generated persona.

[0797] "Means for optimizing advertising content and formats" refers to algorithms and tools that automatically design and select the most effective advertising messages and formats for target users based on the generated personas and survey question lists.

[0798] The "data analysis module" is a software component for aggregating and analyzing survey results, and analyzes data using methods such as statistical analysis and machine learning.

[0799] The "visualization module" is a software component for visually displaying the results of the analysis performed by the data analysis module, and visualizing them in the form of graphs, charts, etc.

[0800] A "generative AI model" is an AI model that automatically generates personas and survey question lists based on user profile information, and uses technologies such as natural language processing.

[0801] An "interface" is an input means or screen that allows a user to input information to a system, or a means for receiving output.

[0802] "Database" means a data storage system for storing and managing survey questionnaires and survey results.

[0803] This invention is a system that automatically generates personas based on user profile information using generation AI and automatically provides a list of survey questions based on the personas in order to conduct user surveys efficiently and at low cost. Advertising content and format can then be optimized based on the generated personas and survey questions. The program and processing required to realize this system are described below.

[0804] First, a user accesses the system and enters user profile information through an interface that is compatible with a variety of devices, including smartphones and head-mounted displays, and allows users to enter information such as age, gender, occupation, nationality, hobbies, and lifestyle.

[0805] The server automatically generates a persona based on the user profile information using a generative AI model (e.g., OpenAI's GPT-3). This persona includes details such as name, hobbies, lifestyle, values, needs, and challenges, creating a detailed image of the user.

[0806] The server then automatically generates a survey questionnaire based on the personas. This questionnaire contains specific questions that address the needs, values, and challenges of the target users. The survey questionnaire is then saved in a database for future reference.

[0807] Users can conduct surveys based on a list of survey questions provided by the server. The survey format can be selected, such as an online questionnaire or an interview. The resulting survey data is stored in a database.

[0808] The data analysis module then aggregates the survey data, and the visualization module visualizes the analyzed results, helping users gain insights into the data.

[0809] Furthermore, based on the generated personas and survey question list, the server provides a means to optimize advertising content and formats by using a generative AI model to automatically design and select the most effective advertising messages and formats.

[0810] Examples:

[0811] For example, let's say a marketer for a health food brand launching a new product uses this system to research the target market. The marketer would enter the following profile information:

[0812] Age: 30

[0813] Gender: Female

[0814] Occupation: Marketing Manager

[0815] Nationality: Japanese

[0816] Hobbies: Fitness, cooking

[0817] Based on this information, the generative AI model generates a persona like this:

[0818] Name: Yamada Hanako (fictional character)

[0819] Lifestyle: City work, active weekends

[0820] Values: Health-conscious, efficiency-oriented

[0821] Based on the persona, a list of survey questions is generated, such as:

[0822] “How did you learn about new health tools and services?”

[0823] "How often do you devote time to fitness?"

[0824] This information provides specific steps and results that generate optimal strategies for optimizing advertising content and formats.

[0825] Example prompt sentence:

[0826] Age: 30

[0827] Gender: Female

[0828] Occupation: Marketing Manager

[0829] Nationality: Japanese

[0830] Hobbies: Fitness, cooking

[0831] Based on the user profile information above, generate the following persona information:

[0832] name

[0833] hobby

[0834] Lifestyle

[0835] values

[0836] needs

[0837] assignment

[0838] In this way, by using this system, advertisers can efficiently and effectively optimize their targeted advertising.

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

[0840] Step 1:

[0841] A user logs in to the system and enters user profile information through the interface, including age, gender, occupation, nationality, hobbies, lifestyle, etc., so that the system can obtain the required profile data.

[0842] Input: User profile information (age, gender, occupation, nationality, hobbies, lifestyle)

[0843] Output: Retrieved user profile information

[0844] Step 2:

[0845] The server receives the acquired user profile information and passes it to a generative AI model to automatically generate a persona. The generative AI model (e.g., OpenAI's GPT-3) analyzes the input data and generates a persona. This persona includes a fictitious name, hobbies, lifestyle, values, needs, challenges, etc.

[0846] Input: User profile information

[0847] Output: Generated personas

[0848] Data processing: Persona generation using generative AI models

[0849] Step 3:

[0850] The server generates a list of survey questions based on the generated persona, and automatically creates specific survey questions based on the attributes, needs, and issues of the generated persona.

[0851] Input: Generated persona

[0852] Output: Survey Question List

[0853] Data Calculation: Question List Generation Based on Persona Information

[0854] Step 4:

[0855] The server provides the user with a list of survey questions, which are then stored in a database for future reference. The user then selects the survey format (online questionnaire, interview, etc.) based on the provided list of questions.

[0856] Input: Survey Questionnaire

[0857] Output: Provided survey question list

[0858] Specific operation: The user selects the survey format

[0859] Step 5:

[0860] Users conduct surveys in the format of their choice, and the survey results are stored in a database for later analysis.

[0861] Input: Survey response

[0862] Output: Saved survey results

[0863] Specific operation: Saving to the database

[0864] Step 6:

[0865] The server uses a data analysis module to analyze the collected survey results, which extracts user behavior patterns, needs, values, etc.

[0866] Input: Saved Survey Results

[0867] Output: Analysis results

[0868] Data calculations: Analysis of survey results

[0869] Step 7:

[0870] The server visualizes the analysis results through a visualization module, allowing users to receive the analysis results in a visually easy-to-understand format.

[0871] Input: Analysis results

[0872] Output: Visualized analysis results

[0873] Specific behavior: Generating graphs and charts

[0874] Step 8:

[0875] Based on the generated personas and survey question list, the server uses methods to optimize the advertising content and format, utilizing a generative AI model to automatically design and select the optimal advertising message and format for the target.

[0876] Input: Persona, survey question list, analysis results

[0877] Output: Optimized ad content and format

[0878] Data processing: Ad optimization with generative AI models

[0879] Through the above processing steps, this system can accommodate a variety of profiles and can formulate effective advertising strategies at low cost and efficiently.

[0880] 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.

[0881] MODE FOR CARRYING OUT THE INVENTION

[0882] This invention is a system that automatically generates personas using generative AI based on user profile information, and then combines it with an emotion engine that recognizes the user's emotions to provide a list of survey questions and analyze and visualize the survey results.

[0883] Program processing overview

[0884] 1. User enters persona information

[0885] The user logs in to the system and accesses the persona information input screen.

[0886] Users enter profile information such as age, gender, occupation, nationality, hobbies, and lifestyle.

[0887] After inputting, the user clicks the "Submit" button to send the information to the system.

[0888] 2. The server automatically generates personas

[0889] The server receives the profile information sent by the user.

[0890] The server's generation AI module analyzes the profile information and generates a detailed persona.

[0891] This persona includes name, hobbies, lifestyle, values, needs, challenges, etc.

[0892] The generated personas are stored in a database.

[0893] 3. The server automatically generates a list of survey questions

[0894] A list of survey questions is automatically generated based on the persona generated by the server.

[0895] Your research questionnaire will include questions that address the needs and challenges of your personas.

[0896] 4. The server utilizes the emotion engine

[0897] The server starts the emotion engine and collects the user's emotion data through the user's input information and interface operations.

[0898] The emotional data includes the user's emotional state, such as happiness, sadness, or anger.

[0899] 5. The server uses emotion data

[0900] The server analyzes the collected emotional data and further refines the generated persona profile.

[0901] The emotional data is reflected in the questions in the survey questionnaire.

[0902] 6. Users conduct surveys

[0903] The user receives a list of survey questions provided by the server.

[0904] The user selects the survey format (online survey, interview, etc.).

[0905] The user conducts the survey in the selected format.

[0906] 7. The device aggregates and analyzes the results

[0907] The survey results are stored in a database.

[0908] The device aggregates and analyzes the survey results using a data analysis module.

[0909] The analysis results are visualized through the visualization module and provided to the user.

[0910] Specific examples

[0911] 1. User enters persona information

[0912] As an example, a user wants to conduct target market research for a new product.

[0913] The user enters the following persona information:

[0914] Age: 30

[0915] Gender: Female

[0916] Occupation: Marketing Manager

[0917] Nationality: Japanese

[0918] 2. The server automatically generates personas

[0919] The generation AI generates a persona based on the information received by the server.

[0920] The generated personas include the following details:

[0921] Name: Yamada Hanako (fictional character)

[0922] Hobbies: Fitness, cooking

[0923] Lifestyle: City work, active weekends

[0924] Values: Health-conscious, efficiency-oriented

[0925] Needs: Seeking efficient tools and services to balance work and personal life

[0926] Challenge: Busy work schedule and limited leisure time

[0927] 3. The server automatically generates a list of survey questions

[0928] A list of survey questions is automatically generated based on the persona generated by the server.

[0929] The survey questionnaire includes questions such as:

[0930] “How did you learn about new health tools and services?”

[0931] "How often do you devote time to fitness?"

[0932] "What applications and devices do you use to stay healthy efficiently?"

[0933] 4. The server utilizes the emotion engine

[0934] The server starts the emotion engine and collects emotion data based on the user's input information.

[0935] For example, it is analyzed whether the user feels stressed while answering a questionnaire.

[0936] 5. The server uses emotion data

[0937] The server analyzes the emotional data and further refines the persona profile.

[0938] Incorporate sentiment data into your survey question list and tailor questions accordingly.

[0939] 6. Users conduct surveys

[0940] The user distributes a list of questions to target users in the form of an online survey.

[0941] The user collects the survey results and stores them in a database.

[0942] 7. The device aggregates and analyzes the results

[0943] The survey results aggregated across devices are analyzed using a data analysis module.

[0944] The analysis results are visualized in graphs and charts in the visualization module.

[0945] Users can gain further insights based on the results of this analysis.

[0946] As described above, by utilizing the emotion engine, this system can obtain deeper insights and conduct efficient user surveys that are multinational and multicultural at low cost.

[0947] The processing flow will be explained below.

[0948] Step 1:

[0949] A user logs in to the system.

[0950] The user accesses the persona information input screen.

[0951] Step 2:

[0952] The user enters the following persona information:

[0953] age

[0954] sex

[0955] Occupation

[0956] nationality

[0957] Other options (hobbies, lifestyle, etc.)

[0958] Step 3:

[0959] The user checks the input and clicks the "Submit" button.

[0960] Step 4:

[0961] The server receives the persona information sent by the user.

[0962] A generation AI module within the server analyzes the persona information.

[0963] Step 5:

[0964] The server generates a realistic persona profile based on the given information.

[0965] Name (fictitious)

[0966] Detailed profile (hobbies, lifestyle, values, etc.)

[0967] Estimating needs and challenges

[0968] Step 6:

[0969] The server stores the generated personas in a database.

[0970] Step 7:

[0971] Based on the persona generated by the server, a list of questions for user surveys is generated.

[0972] Selecting questions based on the persona's needs and challenges

[0973] Step 8:

[0974] The server starts the emotion engine and collects the user's emotion data through the user's input information and interface operations.

[0975] The emotion data includes the user's happiness, sadness, anger, etc.

[0976] Step 9:

[0977] The server analyzes the emotional data and further refines the generated persona profile.

[0978] For example, if a user is feeling stressed, add that emotion to the persona's characteristics.

[0979] Step 10:

[0980] The server adjusts the questions in the survey questionnaire based on the emotional data.

[0981] Add or change emotion-based questions.

[0982] Step 11:

[0983] The server provides a survey template along with a list of survey questions.

[0984] The terminal receives the question list and template provided by the server.

[0985] Step 12:

[0986] The user checks the list of questions provided by the terminal.

[0987] The user selects the survey format (online survey, interview, etc.).

[0988] Step 13:

[0989] The survey is conducted in a format selected by the user.

[0990] For online surveys, distribute the survey link to target users.

[0991] In the case of interviews, schedules will be arranged with the interviewees.

[0992] Step 14:

[0993] The device stores the survey results in a database.

[0994] The device aggregates the survey results using a data aggregation module.

[0995] Step 15:

[0996] The device generates graphs and charts to visualize the aggregated results.

[0997] Step 16:

[0998] The device analyzes the analysis results and extracts key insights.

[0999] Step 17:

[1000] The device provides the user with analysis results and insights in the form of a report.

[1001] The above are the specific processing steps of the system program.

[1002] Example 2

[1003] 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."

[1004] Conventional survey systems have had difficulty efficiently completing the process from inputting user profile information to generating personas, creating survey question lists, and compiling and analyzing survey results. In particular, there were no systems that could refine personas and survey questions by taking user emotional data into account, which led to problems with reduced survey accuracy and reliability.

[1005] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting desired user profile information, a means for automatically generating a persona based on the user profile information, a means for automatically generating a survey question list based on the generated persona, a means for collecting emotional data from user input information and operation data, a means for analyzing the emotional data and refining the profile of the generated persona, a means for aggregating and analyzing the survey results, and a means for visualizing the analysis results and presenting them to the user. This solves the conventional problems and enables highly accurate surveys that take user emotional data into consideration.

[1006] "User profile information" is information about personal attributes and characteristics such as the user's age, sex, occupation, nationality, hobbies, and lifestyle.

[1007] A "persona" is a fictional character created based on user profile information, including a detailed profile, values, needs, and challenges.

[1008] "Generative AI" refers to algorithms and systems that use artificial intelligence to automatically generate personas and survey question lists.

[1009] A "survey questionnaire" is a list of questions that are automatically generated to address the needs and challenges of a persona.

[1010] "Emotion data" refers to data relating to emotional states such as joy, sadness, and anger, which are collected through user input information and interface operations.

[1011] A "database" is a system for storing and managing data such as persona information, survey question lists, and survey results.

[1012] "Data analysis module" refers to the functions and software that aggregate and analyze survey results to gain useful insights.

[1013] A "visualization module" is a function or tool for visually displaying the results of data analysis, using graphs, charts, etc.

[1014] An "emotion engine" is an algorithm or system that analyzes user input information and behavioral data to detect and evaluate emotional states.

[1015] An "interface" is a screen, form, or other means by which a user inputs information into a system.

[1016] MODE FOR CARRYING OUT THE INVENTION

[1017] This system uses a generative AI to automatically generate a persona based on user profile information, and then combines it with an emotion engine that recognizes the user's emotions to provide a list of survey questions and analyze and visualize the survey results. A specific embodiment is described below.

[1018] System Configuration

[1019] 1. User enters persona information

[1020] Users log in to the system using devices such as PCs or smartphones.

[1021] Users enter profile information such as age, gender, occupation, nationality, hobbies, and lifestyle on a dedicated persona information input screen.

[1022] For example, enter information such as "Age: 30," "Gender: Female," "Occupation: Marketing Manager," "Nationality: Japanese," "Hobbies: Fitness," and "Lifestyle: Works in an urban area and spends weekends actively," and click the "Submit" button to send the information to the server.

[1023] 2. The server automatically generates personas

[1024] The server receives the profile information sent by the user.

[1025] A generative AI module installed on the server analyzes the input information and generates a detailed persona.

[1026] The generated persona will include the following elements:

[1027] Name (e.g. "Yamada Hanako")

[1028] Hobbies (fitness, cooking)

[1029] Lifestyle (working in the city and being active on the weekends)

[1030] Values ​​(health-conscious, efficiency-oriented)

[1031] Needs (efficient tools and services to balance work and personal life)

[1032] Challenges (busy work schedule and limited leisure time)

[1033] The server stores the generated persona information in a database.

[1034] 3. The server automatically generates a list of survey questions

[1035] The server automatically generates a list of survey questions based on the persona information stored in the database.

[1036] The prompt sent to the generative AI is, "Create effective survey questions relevant to this persona."

[1037] Specific questions include:

[1038] “How did you learn about new health tools and services?”

[1039] "How often do you devote time to fitness?"

[1040] "What applications and devices do you use to stay healthy efficiently?"

[1041] The server stores the automatically generated survey questionnaire in a database.

[1042] 4. The server utilizes the emotion engine

[1043] The server starts the emotion engine and collects user input information and data from interface operations.

[1044] The data collected includes the user's emotional state, such as happiness, sadness, or anger.

[1045] For example, emotional data is analyzed based on facial expressions, tone of voice, click behavior, etc. when a user answers a question.

[1046] 5. The server uses emotion data

[1047] The server analyzes the collected emotional data to further refine the persona profile.

[1048] The emotional data is reflected in the survey questions, and the questions in the survey list are adjusted accordingly. For example, questions that are likely to cause stress to users are softened.

[1049] 6. Users conduct surveys

[1050] The user reviews the list of survey questions provided by the server.

[1051] Users choose an appropriate format to conduct the survey, such as an online survey or an interview.

[1052] Conduct a survey in the format of your choice and set up the system to store each response in a database.

[1053] 7. The device aggregates and analyzes the results

[1054] The survey results are compiled into a database.

[1055] The terminal launches a data analysis module to analyze the aggregated results.

[1056] The analyzed data can be visualized as follows:

[1057] Use graphs, charts, and other visualization techniques.

[1058] Users gain new insights based on the visualized results.

[1059] Prompt Sentence Examples

[1060] "A 30-year-old female marketing manager is looking for a new health tool. Please generate a persona for her."

[1061] "Generate personas based on the user's age (30 years old), gender (female), occupation (marketing manager), and nationality (Japanese), and create a list of relevant survey questions."

[1062] In this way, the system combines a generative AI model with an emotion engine to enable efficient and in-depth user research.

[1063] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1064] System program processing flow

[1065] Step 1:

[1066] User enters persona information

[1067] Input: Users log in to the system using a device such as a PC or smartphone and enter profile information such as age, gender, occupation, nationality, hobbies, and lifestyle on the persona information input screen.

[1068] Data processing: The entered information is temporarily stored in the device's memory and any necessary validation (e.g., checking the input format and required fields) is performed before transmission.

[1069] Output: When the user clicks the "Submit" button, the input information is sent to the server. Examples include "Age: 30," "Gender: Female," "Occupation: Marketing Manager," "Nationality: Japanese," "Hobbies: Fitness," and "Lifestyle: Works in a city and spends weekends actively."

[1070] Step 2:

[1071] The server automatically generates a persona

[1072] Input: The server receives the profile information sent by the user and temporarily stores it in a database.

[1073] Data calculation: The generative AI module installed on the server analyzes the received information. During the analysis, statistical and machine learning models are used to extract user characteristics based on the input data, and a persona is generated.

[1074] Output: The generated persona information is saved in a database. The generated persona includes information such as name (e.g., Hanako Yamada), hobbies (fitness, cooking), lifestyle (works in an urban area, spends weekends actively), values ​​(health-conscious, emphasizes efficiency), needs (efficient tools and services for balancing work and personal life), and challenges (busy work, limited leisure time).

[1075] Step 3:

[1076] The server automatically generates a list of survey questions.

[1077] Input: The server retrieves the persona information stored in the database.

[1078] Data calculation: The server sends a prompt to the generation AI module, saying, "Please create effective survey questions related to this persona." The generation AI analyzes the persona information and generates a corresponding list of survey questions.

[1079] Output: The server stores the automatically generated survey questions in a database. Specific example questions include, "How do you learn about new health tools and services?", "How often do you devote time to fitness?", and "What applications and devices do you use to effectively maintain your health?"

[1080] Step 4:

[1081] The server utilizes the emotion engine

[1082] Input: Collects user input and interface interaction data, such as facial expressions, tone of voice, and click behavior when answering questions.

[1083] Data calculation: The emotion engine analyzes the collected data and detects the user's emotional state (happiness, sadness, anger, etc.).

[1084] Output: The detected emotion data is stored in a database for further analysis and profile refinement.

[1085] Step 5:

[1086] The server uses the emotion data

[1087] Input: Obtain emotional data and persona information stored in the database.

[1088] Data calculation: The persona profile is further refined based on emotional data. For example, questions that are likely to cause stress to the user may be softened, and the content and order of questions may be adjusted according to the user's emotions.

[1089] Output: Updated personas and survey question list saved to database.

[1090] Step 6:

[1091] A user conducts a survey

[1092] Input: Get the latest list of survey questions provided by the server.

[1093] Specific operation: The user selects an appropriate survey format (such as an online questionnaire or interview) and conducts the survey with target users. The survey questions are displayed on the user's device, and the user enters the answers.

[1094] Output: Each answer is saved in a database in real time.

[1095] Step 7:

[1096] The device aggregates and analyzes the results

[1097] Input: Retrieve survey results stored in the database.

[1098] Data calculation: The data analysis module installed on the device aggregates and analyzes the survey results. Analysis methods include statistical analysis and text mining.

[1099] Output: The analysis results are visualized through the visualization module. The results are displayed in graphs, charts, etc. and provided to the user, allowing the user to gain new insights.

[1100] As described above, this system realizes efficient and precise user research through each processing step.

[1101] (Application example 2)

[1102] 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."

[1103] Many modern commercial systems struggle to provide personalized services that accurately reflect customer emotions and behavioral characteristics. This results in low customer satisfaction and a lack of improvement in user experience. Furthermore, conventional systems are unable to accurately analyze survey results by analyzing user emotions and respond to diverse customer needs. A method to resolve these issues and provide more effective and personalized customer service is needed.

[1104] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting desired user profile information, means for automatically generating a persona based on the user profile information, means for automatically generating a survey question list based on the generated persona, means for providing the survey question list to the user, means for the user to conduct the survey, means for further refining the survey results using an emotion engine that analyzes the user's emotions, means for aggregating and analyzing the survey results, and means for visualizing the analysis results and presenting them to the user. This makes it possible to analyze user emotion data and refine the survey results. This makes it possible to provide more personalized and effective customer service and improve customer satisfaction.

[1105] "User profile information" refers to personal information such as a user's age, gender, occupation, hobbies, and lifestyle.

[1106] A "persona" is a virtual image of a user that is generated based on user profile information, and includes the user's name, hobbies, lifestyle, values, needs, challenges, etc.

[1107] "Survey Question List" refers to a list of questions that are automatically generated based on the generated personas.

[1108] An "emotion engine" refers to technology that analyzes a user's facial expressions, tone of voice, text input, etc., to recognize emotional states such as joy, sadness, and anger in real time.

[1109] "Aggregation" refers to the compilation of survey results obtained from users.

[1110] "Analysis" refers to the analysis of aggregated data.

[1111] "Visualization" refers to presenting analytical results in a visual format such as graphs or charts and providing them to users.

[1112] "Interface" refers to the means by which a user inputs information into or receives information from a system.

[1113] "Generative AI" refers to artificial intelligence technology that automatically generates content and information based on given data.

[1114] A "database" refers to a system for storing and managing information in an organized manner.

[1115] "Data Analysis Module" refers to a software component for analyzing collected data.

[1116] "Visualization Module" refers to a software component for visually representing the results of data analysis.

[1117] MODE FOR CARRYING OUT THE INVENTION

[1118] This invention is a system that automatically generates personas based on user profile information, incorporates an emotion engine that recognizes user emotions, provides a survey question list, and analyzes and visualizes survey results. The system of this invention operates based on information entered by the user.

[1119] Program Generation

[1120] The server executes the system program according to the following procedure.

[1121] A natural language description of what the program does

[1122] 1. Enter your user profile information:

[1123] Users log in from a device such as a smartphone or tablet and enter profile information such as their name, age, gender, occupation, hobbies, and lifestyle. This information is accepted through the interface and sent to the server.

[1124] 2. Automatic persona generation:

[1125] Based on the received user profile information, the server uses a generative AI model (e.g., GPT-3) to automatically generate a detailed persona, which includes detailed information about the user's hobbies, lifestyle, values, and needs.

[1126] 3. Automatic generation of survey questions:

[1127] The server automatically generates a list of survey questions based on the persona. For example, if the persona is a "30-year-old woman interested in fitness," a survey list containing fitness-related questions will be generated.

[1128] 4. Leveraging the Emotion Engine:

[1129] During the survey, the server activates an emotion engine to collect real-time emotional data from the user's facial expressions and tone of voice, allowing analysis of the emotional state in which the user provided their answers.

[1130] 5. Elaboration and analysis of findings:

[1131] The server uses the collected emotional data to further refine the generated persona information. It also reflects the emotional data in the questions in the survey questionnaire to make more appropriate adjustments. Once the survey is complete, the user's responses are stored in a database and analyzed by the data analysis module.

[1132] 6. Visualizing the results:

[1133] The analysis results are visualized in the form of graphs and charts through the visualization module and provided to users, allowing them to intuitively understand the survey results.

[1134] Specific examples

[1135] For example, a user enters the following profile information:

[1136] Age: 35

[1137] Gender: Male

[1138] Occupation: Software Engineer

[1139] Hobbies: Reading, games

[1140] Lifestyle: Urban areas, many work from home

[1141] An example of a prompt for a generative AI model is:

[1142] "35-year-old male, software engineer, hobbies include reading and gaming, lifestyle is urban, mostly working from home. Please generate a persona based on this information."

[1143] The persona generated in this way can depict a specific character, such as "an engineer who loves games and places importance on ways to relax while working from home." A list of fitness and relaxation-related survey questions is automatically generated based on this persona, and an emotion engine is used to collect and analyze emotional data in real time, allowing for a personalized experience tailored to the user.

[1144] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1145] Step 1:

[1146] A user logs in from a device such as a smartphone or tablet and enters profile information such as name, age, gender, occupation, hobbies, and lifestyle. The entered information is sent to the server through the interface, allowing the server to receive (input) the user profile information. Specifically, the user enters information into a form and presses the "Submit" button.

[1147] Step 2:

[1148] The server automatically generates a detailed persona using a generative AI model (e.g., GPT-3) based on the received user profile information. At this time, it analyzes the input profile information and provides a prompt to the generative AI model (data processing). For example, if the profile information is "35-year-old male, software engineer, hobbies are reading and gaming," the prompt would be "35-year-old male, software engineer, hobbies are reading and gaming. Please generate a persona based on this information." The generated persona is returned to the device (output).

[1149] Step 3:

[1150] The server automatically generates a list of survey questions based on the generated persona. At this time, it analyzes the information of the generated persona and generates questions that suit the persona's tastes and needs (data calculation). For example, if the persona is a "30-year-old woman who is interested in fitness," a list including fitness-related questions will be generated. The generated list of survey questions is provided to the terminal (output).

[1151] Step 4:

[1152] The user receives a list of survey questions provided by the terminal. The user conducts the survey based on the list and answers the questions. At this time, the user's answers are sent (input) to the server through the interface. Specifically, the user answers the questions in a questionnaire format and presses the "send" button.

[1153] Step 5:

[1154] The server activates the emotion engine and collects emotional data in real time from the user's facial expressions and tone of voice. The server recognizes the user's emotional state based on the user's input information and interface operations, and stores the analysis results in a database (data calculation). For example, the server collects and analyzes the user's facial expressions and tone of voice when answering questions using a camera or microphone.

[1155] Step 6:

[1156] The server uses the collected emotional data to further refine the generated persona information. Furthermore, it reflects the emotional data in the questions in the survey questionnaire to make more appropriate adjustments (data calculation). For example, if the user is feeling stressed, it changes the questions in the questionnaire to reflect that emotion more gently. The refined persona and the adjusted questionnaire are saved in the database (output).

[1157] Step 7:

[1158] Once the survey is complete, the server collects the responses from users and stores them in a database (input). The server then analyzes the responses using a data analysis module, analyzing the aggregated data and performing statistical processing (data calculations). For example, the response data may be aggregated and analyzed for trends and patterns.

[1159] Step 8:

[1160] The server uses a visualization module to visualize the analysis results. At this time, the analysis results are converted into visual formats such as graphs and charts and provided to the user (output). Specifically, the analysis results are converted into diagrams and charts, and dashboards and reports are generated. This allows the user to intuitively understand the investigation results.

[1161] 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.

[1162] 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.

[1163] 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.

[1164] [Third embodiment]

[1165] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1166] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1167] 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).

[1168] 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.

[1169] 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.

[1170] 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).

[1171] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1172] 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.

[1173] 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.

[1174] 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.

[1175] 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.

[1176] 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."

[1177] MODE FOR CARRYING OUT THE INVENTION

[1178] This invention is a system that automatically generates personas using generation AI based on user profile information and automatically provides a list of survey questions based on those personas in order to conduct user surveys efficiently and at low cost.

[1179] Program processing overview

[1180] 1. User enters persona information

[1181] The user logs in to the system and accesses the persona information input screen.

[1182] Users enter profile information such as age, gender, occupation, nationality, hobbies, and lifestyle.

[1183] After inputting, the user clicks the "Submit" button to send the information to the system.

[1184] 2. The server automatically generates personas

[1185] The server receives the profile information sent by the user.

[1186] The server's generation AI analyzes the profile information and generates a detailed persona.

[1187] This persona includes name, hobbies, lifestyle, values, needs, challenges, etc.

[1188] The generated personas are stored in a database.

[1189] 3. The server automatically generates a list of survey questions

[1190] A list of survey questions is automatically generated based on the persona generated by the server.

[1191] Your research questionnaire will include questions that address the needs and challenges of your personas.

[1192] 4. Users conduct surveys

[1193] The user receives a list of survey questions provided by the server.

[1194] The user selects the survey format (online survey, interview, etc.).

[1195] The user conducts the survey in the selected format.

[1196] 5. The device aggregates and analyzes the results

[1197] The survey results are stored in a database.

[1198] The device aggregates and analyzes the survey results using a data analysis module.

[1199] The analysis results are visualized through the visualization module and provided to the user.

[1200] Specific examples

[1201] 1. User enters persona information

[1202] As an example, a user wants to conduct target market research for a new product.

[1203] The user enters the following persona information:

[1204] Age: 30

[1205] Gender: Female

[1206] Occupation: Marketing Manager

[1207] Nationality: Japanese

[1208] 2. The server automatically generates personas

[1209] Based on the information received by the server, the generation AI generates a persona.

[1210] The generated personas include the following details:

[1211] Name: Yamada Hanako (fictional character)

[1212] Hobbies: Fitness, cooking

[1213] Lifestyle: City work, active weekends

[1214] Values: Health-conscious, efficiency-oriented

[1215] Needs: Seeking efficient tools and services to balance work and personal life

[1216] Challenge: Busy work schedule and limited leisure time

[1217] 3. The server automatically generates a list of survey questions

[1218] A list of survey questions is automatically generated based on the persona generated by the server.

[1219] The survey questionnaire includes questions such as:

[1220] “How did you learn about new health tools and services?”

[1221] "How often do you devote time to fitness?"

[1222] "What applications and devices do you use to stay healthy efficiently?"

[1223] 4. Users conduct surveys

[1224] The user distributes a list of questions to target users in the form of an online survey.

[1225] The user collects the survey results and stores them in a database.

[1226] 5. The device aggregates and analyzes the results

[1227] The survey results aggregated across devices are analyzed using a data analysis module.

[1228] The analysis results are visualized in graphs and charts in the visualization module.

[1229] Users can gain further insights based on the results of this analysis.

[1230] In this way, this system makes it possible to conduct efficient user surveys that are multinational and multicultural at low cost.

[1231] The processing flow will be explained below.

[1232] Step 1:

[1233] A user logs in to the system.

[1234] The user accesses the persona information input screen.

[1235] Step 2:

[1236] The user enters the following persona information:

[1237] age

[1238] sex

[1239] Occupation

[1240] nationality

[1241] Other options (hobbies, lifestyle, etc.)

[1242] Step 3:

[1243] The user checks the input and clicks the "Submit" button.

[1244] Step 4:

[1245] The server receives the persona information sent by the user.

[1246] A generation AI module within the server analyzes the persona information.

[1247] Step 5:

[1248] The server generates a realistic persona profile based on the given information.

[1249] Name (fictitious)

[1250] Detailed profile (hobbies, lifestyle, values, etc.)

[1251] Estimating needs and challenges

[1252] Step 6:

[1253] The server stores the generated personas in a database.

[1254] Step 7:

[1255] Based on the persona generated by the server, a list of questions for user surveys is generated.

[1256] Selecting questions based on the persona's needs and challenges

[1257] Step 8:

[1258] The server provides a survey template along with a list of questions.

[1259] Step 9:

[1260] The terminal receives the question list and template provided by the server.

[1261] Step 10:

[1262] The user checks the list of questions provided by the terminal.

[1263] Step 11:

[1264] The user selects the survey format (online survey, interview, etc.).

[1265] Step 12:

[1266] The survey is conducted in a format selected by the user.

[1267] For online surveys, distribute the survey link to target users.

[1268] In the case of interviews, schedules will be arranged with the interviewees.

[1269] Step 13:

[1270] The device stores the survey results in a database.

[1271] Step 14:

[1272] The device aggregates the survey results using a data aggregation module.

[1273] Step 15:

[1274] The device generates graphs and charts to visualize the aggregated results.

[1275] Step 16:

[1276] The device analyzes the analysis results and extracts key insights.

[1277] Step 17:

[1278] The device provides the user with analysis results and insights in the form of a report.

[1279] The above are the specific processing steps of the system program.

[1280] Example 1

[1281] 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."

[1282] In modern market research, it is important to efficiently generate accurate personas based on user profile information and create survey question lists based on them. However, traditional methods require manually creating personas and building survey question lists, which is time-consuming and costly. Furthermore, the process of analyzing and visualizing survey results is cumbersome, making it difficult to make quick decisions.

[1283] 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.

[1284] In this invention, the server includes: means for inputting desired user profile information; means for automatically generating a persona using a generative AI model based on the user profile information; means for automatically generating a survey question list based on the generated persona; means for providing the survey question list to a user; means for the user to conduct a survey in the form of an online questionnaire or interview; means for aggregating and analyzing the survey results using a data analysis module; and means for visualizing the data analysis results using a visualization module and presenting them to the user. This enables the server to quickly and efficiently generate a persona and survey question list from user profile information, and then analyze, visualize, and provide the results. "User profile information" refers to attribute information entered by the user, such as age, gender, occupation, nationality, hobbies, and lifestyle.

[1285] A "generative AI model" is an artificial intelligence algorithm used to generate detailed personas from input user profile information.

[1286] A "persona" is a fictional user image that is automatically generated based on user profile information using a generative AI model.

[1287] A "survey question list" is a list of questions that are automatically generated based on the needs and challenges of the generated persona.

[1288] "Data Analysis Module" means a software module for analyzing aggregated survey results.

[1289] A "visualization module" is a software module for visually representing analyzed data.

[1290] An "online survey" is a form of survey conducted via the Internet.

[1291] The "interview format" is a survey format in which data is collected through interviews.

[1292] This invention is a system that automatically generates a persona using a generative AI model based on user profile information and automatically provides a survey question list based on that persona. This system can quickly and efficiently generate a persona and a survey question list from the profile information entered by the user, and then analyze and visualize the results before providing them.

[1293] Hardware and software used

[1294] The main hardware and software required to implement this system are:

[1295] 1. Server: A computer with a high-performance processor and a large amount of memory is required. For example, AWS (Amazon Web Services) or Google Cloud Platform can be used.

[1296] 2. Generative AI models: We need AI models that are specialized for natural language processing and generative tasks, such as OpenAI's GPT-4 model.

[1297] 3. Database: Used to store user profile information, generated personas, survey questions, and survey results. For example, a relational database such as PostgreSQL or MySQL can be used.

[1298] 4. Data analysis module: Survey results are compiled and analyzed using Python's Pandas library and statistical software R.

[1299] 5. Visualization module: Visualize data analysis results using tools such as Python's Matplotlib and Tableau.

[1300] Program processing overview

[1301] 1. User enters persona information

[1302] A user logs in to the system interface and accesses the persona information input screen, where they enter profile information such as age, gender, occupation, nationality, hobbies, and lifestyle, and then clicks the "Submit" button to send the information to the server.

[1303] 2. The server automatically generates personas

[1304] Based on the profile information received by the server, a detailed persona is generated using a generative AI model (e.g., GPT-4). This process uses the following prompt:

[1305] "We have received the following profile information from a user: Age: 30, Gender: Female, Occupation: Marketing Manager, Nationality: Japanese. Please generate a detailed persona based on this information. The persona should include name, hobbies, lifestyle, values, needs, and challenges."

[1306] The generated personas are stored in a database.

[1307] 3. The server automatically generates a list of survey questions

[1308] Based on the personas generated by the server, a generative AI model is used to automatically generate a list of survey questions, using the following prompts in the process:

[1309] “Generate a list of research questions that will work for this persona. Create questions based on their needs and challenges.”

[1310] The generated survey question list is stored on the server side and provided to the user.

[1311] 4. Users conduct surveys

[1312] Users obtain a list of survey questions provided by the system and conduct the survey in the form of an online questionnaire (e.g., Google Forms) or an interview. After the survey results are collected, they are stored in a database.

[1313] 5. The device aggregates and analyzes the results

[1314] The survey results are analyzed using a data analysis module, such as Python's Pandas library, to clean the data and perform statistical analysis. The analysis results are then visualized in graphs and charts using a visualization module (e.g., Tableau or Matplotlib) and provided to the user.

[1315] Specific examples

[1316] For example, consider a user conducting target market research for a new product. The user logs into the system and enters the following persona information: Age: 30, Gender: Female, Occupation: Marketing Manager, Nationality: Japanese. The server uses this information to generate the following detailed persona using a generative AI model:

[1317] Name: Yamada Hanako (fictional character)

[1318] Hobbies: Fitness, cooking

[1319] Lifestyle: City work, active weekends

[1320] Values: Health-conscious, efficiency-oriented

[1321] Needs: Seeking efficient tools and services to balance work and personal life

[1322] Challenge: Busy work schedule and limited leisure time

[1323] The server then generates the following list of survey questions based on this persona:

[1324] “How did you learn about new health tools and services?”

[1325] "How often do you devote time to fitness?"

[1326] "What applications and devices do you use to stay healthy efficiently?"

[1327] Users conduct surveys in the form of online questionnaires and collect the results. The terminal analyzes the survey results using the data analysis module and visualizes them in graphs and charts using the visualization module. The analysis results are provided to users to gain more specific market insights.

[1328] The above is a detailed description of an embodiment of this system.

[1329] The flow of the specific processing in the first embodiment will be described with reference to FIG. 11.

[1330] Step 1:

[1331] The user enters persona information. Specifically, the user logs in to the system interface, accesses the persona information input screen, and enters profile information such as age, gender, occupation, nationality, hobbies, and lifestyle into the form. After completing the input, the user clicks the "Submit" button to send the information to the server. The input data is sent to the server as the user's profile information.

[1332] Input: User persona information (age, gender, occupation, nationality, hobbies, lifestyle, etc.)

[1333] Output: User profile information data sent to the server

[1334] Step 2:

[1335] The server automatically generates a persona. The server receives profile information sent by the user. Based on this information, the server calls the generative AI model to create a prompt sentence and input it into the AI ​​model. The generative AI model generates a detailed persona based on the user's profile information and stores the generated persona data in the server's database.

[1336] Input: User profile information data

[1337] Output: Generated persona data (name, hobbies, lifestyle, values, needs, challenges, etc.)

[1338] Step 3:

[1339] The server automatically generates a list of survey questions. Based on the generated persona data, the server uses a generative AI model to generate a list of survey questions. This prompt sentence is created and input into the generative AI model to generate a list of questions. The generated list of survey questions is stored in the server's database.

[1340] Input: Generated persona data

[1341] Output: Generated survey questionnaire

[1342] Step 4:

[1343] The server provides the survey questionnaire to the user. The server provides a link or download option that the user can access and presents the survey questionnaire to the user. The user retrieves it and saves it in a usable form.

[1344] Input: Generated survey questionnaire list

[1345] Output: Survey questionnaire link or file provided to the user

[1346] Step 5:

[1347] The user conducts the survey. The user receives the survey question list provided by the server and conducts the survey in the form of an online questionnaire (e.g., Google Forms) or an interview. The collected data is then uploaded back to the server by the user.

[1348] Input: Survey Questionnaire

[1349] Output: Collected survey data

[1350] Step 6:

[1351] The server aggregates the survey results and performs analysis on the terminal. The server provides the survey data received from the user to the data analysis module, which aggregates and cleans the data and performs statistical analysis. The results are stored in a database.

[1352] Input: Collected survey data

[1353] Output: Aggregated and analyzed data

[1354] Step 7:

[1355] The terminal visualizes the aggregated and analyzed results, and the server presents them to the user. The terminal uses a visualization module to visualize the aggregated and analyzed data in graphs and charts, which the server presents to the user, providing the visualized results.

[1356] Input: Aggregated and analyzed data

[1357] Output: Visualized data (graphs and charts)

[1358] Based on the specific actions taken at each step and the input / output data, the system can quickly and effectively generate personas and survey question lists from user profile information, analyze and visualize the results, and provide them to users.

[1359] (Application example 1)

[1360] 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."

[1361] In recent years, formulating effective marketing and advertising strategies that take into account diverse user profiles has become difficult and requires a great deal of time and money. Furthermore, there is a lack of methods for accurately grasping the detailed needs and values ​​of target users and automatically designing optimal advertising content and formats based on that. This has led to problems such as advertisers being unable to quickly formulate appropriate strategies, resulting in reduced advertising effectiveness.

[1362] 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.

[1363] In this invention, the server includes a means for inputting desired user profile information, a means for automatically generating a persona based on the user profile information, and a means for automatically generating a survey question list based on the generated persona, thereby enabling a target effect.

[1364] By building a system that includes a means for optimizing advertising content and format based on the generated personas and survey question list, it becomes possible to quickly and low-costly develop effective advertising strategies that cater to diverse user profiles.

[1365] "User profile information" is a general term for data that includes attribute information about a specific user, such as age, sex, occupation, nationality, hobbies, and lifestyle.

[1366] A "persona" is a fictional image of a user that is generated based on user profile information and that reflects in detail the user's typical needs, values, behavioral patterns, and challenges.

[1367] A "survey questionnaire" is a list of questions that includes questions that address the needs, values, and challenges of users based on the generated persona.

[1368] "Means for optimizing advertising content and formats" refers to algorithms and tools that automatically design and select the most effective advertising messages and formats for target users based on the generated personas and survey question lists.

[1369] The "data analysis module" is a software component for aggregating and analyzing survey results, and analyzes data using methods such as statistical analysis and machine learning.

[1370] The "visualization module" is a software component for visually displaying the results of the analysis performed by the data analysis module, and visualizing them in the form of graphs, charts, etc.

[1371] A "generative AI model" is an AI model that automatically generates personas and survey question lists based on user profile information, and uses technologies such as natural language processing.

[1372] An "interface" is an input means or screen that allows a user to input information to a system, or a means for receiving output.

[1373] "Database" means a data storage system for storing and managing survey questionnaires and survey results.

[1374] This invention is a system that automatically generates personas based on user profile information using generation AI and automatically provides a list of survey questions based on the personas in order to conduct user surveys efficiently and at low cost. Advertising content and format can then be optimized based on the generated personas and survey questions. The program and processing required to realize this system are described below.

[1375] First, a user accesses the system and enters user profile information through an interface that is compatible with a variety of devices, including smartphones and head-mounted displays, and allows users to enter information such as age, gender, occupation, nationality, hobbies, and lifestyle.

[1376] The server automatically generates a persona based on the user profile information using a generative AI model (e.g., OpenAI's GPT-3). This persona includes details such as name, hobbies, lifestyle, values, needs, and challenges, creating a detailed image of the user.

[1377] The server then automatically generates a survey questionnaire based on the personas. This questionnaire contains specific questions that address the needs, values, and challenges of the target users. The survey questionnaire is then saved in a database for future reference.

[1378] Users can conduct surveys based on a list of survey questions provided by the server. The survey format can be selected, such as an online questionnaire or an interview. The resulting survey data is stored in a database.

[1379] The data analysis module then aggregates the survey data, and the visualization module visualizes the analyzed results, helping users gain insights into the data.

[1380] Furthermore, based on the generated personas and survey question list, the server provides a means to optimize advertising content and formats by using a generative AI model to automatically design and select the most effective advertising messages and formats.

[1381] Examples:

[1382] For example, let's say a marketer for a health food brand launching a new product uses this system to research the target market. The marketer would enter the following profile information:

[1383] Age: 30

[1384] Gender: Female

[1385] Occupation: Marketing Manager

[1386] Nationality: Japanese

[1387] Hobbies: Fitness, cooking

[1388] Based on this information, the generative AI model generates a persona like this:

[1389] Name: Yamada Hanako (fictional character)

[1390] Lifestyle: City work, active weekends

[1391] Values: Health-conscious, efficiency-oriented

[1392] Based on the persona, a list of survey questions is generated, such as:

[1393] “How did you learn about new health tools and services?”

[1394] "How often do you devote time to fitness?"

[1395] This information provides specific steps and results that generate optimal strategies for optimizing advertising content and formats.

[1396] Example prompt sentence:

[1397] Age: 30

[1398] Gender: Female

[1399] Occupation: Marketing Manager

[1400] Nationality: Japanese

[1401] Hobbies: Fitness, cooking

[1402] Based on the user profile information above, generate the following persona information:

[1403] name

[1404] hobby

[1405] Lifestyle

[1406] values

[1407] needs

[1408] assignment

[1409] In this way, by using this system, advertisers can efficiently and effectively optimize their targeted advertising.

[1410] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1411] Step 1:

[1412] A user logs in to the system and enters user profile information through the interface, including age, gender, occupation, nationality, hobbies, lifestyle, etc., so that the system can obtain the required profile data.

[1413] Input: User profile information (age, gender, occupation, nationality, hobbies, lifestyle)

[1414] Output: Retrieved user profile information

[1415] Step 2:

[1416] The server receives the acquired user profile information and passes it to a generative AI model to automatically generate a persona. The generative AI model (e.g., OpenAI's GPT-3) analyzes the input data and generates a persona. This persona includes a fictitious name, hobbies, lifestyle, values, needs, challenges, etc.

[1417] Input: User profile information

[1418] Output: Generated personas

[1419] Data processing: Persona generation using generative AI models

[1420] Step 3:

[1421] The server generates a list of survey questions based on the generated persona, and automatically creates specific survey questions based on the attributes, needs, and issues of the generated persona.

[1422] Input: Generated persona

[1423] Output: Survey Question List

[1424] Data Calculation: Question List Generation Based on Persona Information

[1425] Step 4:

[1426] The server provides the user with a list of survey questions, which are then stored in a database for future reference. The user then selects the survey format (online questionnaire, interview, etc.) based on the provided list of questions.

[1427] Input: Survey Questionnaire

[1428] Output: Provided survey question list

[1429] Specific operation: The user selects the survey format

[1430] Step 5:

[1431] Users conduct surveys in the format of their choice, and the survey results are stored in a database for later analysis.

[1432] Input: Survey response

[1433] Output: Saved survey results

[1434] Specific operation: Saving to the database

[1435] Step 6:

[1436] The server uses a data analysis module to analyze the collected survey results, which extracts user behavior patterns, needs, values, etc.

[1437] Input: Saved Survey Results

[1438] Output: Analysis results

[1439] Data calculations: Analysis of survey results

[1440] Step 7:

[1441] The server visualizes the analysis results through a visualization module, allowing users to receive the analysis results in a visually easy-to-understand format.

[1442] Input: Analysis results

[1443] Output: Visualized analysis results

[1444] Specific behavior: Generating graphs and charts

[1445] Step 8:

[1446] Based on the generated personas and survey question list, the server uses methods to optimize the advertising content and format, utilizing a generative AI model to automatically design and select the optimal advertising message and format for the target.

[1447] Input: Persona, survey question list, analysis results

[1448] Output: Optimized ad content and format

[1449] Data processing: Ad optimization with generative AI models

[1450] Through the above processing steps, this system can accommodate a variety of profiles and can formulate effective advertising strategies at low cost and efficiently.

[1451] 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.

[1452] MODE FOR CARRYING OUT THE INVENTION

[1453] This invention is a system that automatically generates personas using generative AI based on user profile information, and then combines it with an emotion engine that recognizes the user's emotions to provide a list of survey questions and analyze and visualize the survey results.

[1454] Program processing overview

[1455] 1. User enters persona information

[1456] The user logs in to the system and accesses the persona information input screen.

[1457] Users enter profile information such as age, gender, occupation, nationality, hobbies, and lifestyle.

[1458] After inputting, the user clicks the "Submit" button to send the information to the system.

[1459] 2. The server automatically generates personas

[1460] The server receives the profile information sent by the user.

[1461] The server's generation AI module analyzes the profile information and generates a detailed persona.

[1462] This persona includes name, hobbies, lifestyle, values, needs, challenges, etc.

[1463] The generated personas are stored in a database.

[1464] 3. The server automatically generates a list of survey questions

[1465] A list of survey questions is automatically generated based on the persona generated by the server.

[1466] Your research questionnaire will include questions that address the needs and challenges of your personas.

[1467] 4. The server utilizes the emotion engine

[1468] The server starts the emotion engine and collects the user's emotion data through the user's input information and interface operations.

[1469] The emotional data includes the user's emotional state, such as happiness, sadness, or anger.

[1470] 5. The server uses emotion data

[1471] The server analyzes the collected emotional data and further refines the generated persona profile.

[1472] The emotional data is reflected in the questions in the survey questionnaire.

[1473] 6. Users conduct surveys

[1474] The user receives a list of survey questions provided by the server.

[1475] The user selects the survey format (online survey, interview, etc.).

[1476] The user conducts the survey in the selected format.

[1477] 7. The device aggregates and analyzes the results

[1478] The survey results are stored in a database.

[1479] The device aggregates and analyzes the survey results using a data analysis module.

[1480] The analysis results are visualized through the visualization module and provided to the user.

[1481] Specific examples

[1482] 1. User enters persona information

[1483] As an example, a user wants to conduct target market research for a new product.

[1484] The user enters the following persona information:

[1485] Age: 30

[1486] Gender: Female

[1487] Occupation: Marketing Manager

[1488] Nationality: Japanese

[1489] 2. The server automatically generates personas

[1490] The generation AI generates a persona based on the information received by the server.

[1491] The generated personas include the following details:

[1492] Name: Yamada Hanako (fictional character)

[1493] Hobbies: Fitness, cooking

[1494] Lifestyle: City work, active weekends

[1495] Values: Health-conscious, efficiency-oriented

[1496] Needs: Seeking efficient tools and services to balance work and personal life

[1497] Challenge: Busy work schedule and limited leisure time

[1498] 3. The server automatically generates a list of survey questions

[1499] A list of survey questions is automatically generated based on the persona generated by the server.

[1500] The survey questionnaire includes questions such as:

[1501] “How did you learn about new health tools and services?”

[1502] "How often do you devote time to fitness?"

[1503] "What applications and devices do you use to stay healthy efficiently?"

[1504] 4. The server utilizes the emotion engine

[1505] The server starts the emotion engine and collects emotion data based on the user's input information.

[1506] For example, it is analyzed whether the user feels stressed while answering a questionnaire.

[1507] 5. The server uses emotion data

[1508] The server analyzes the emotional data and further refines the persona profile.

[1509] Incorporate sentiment data into your survey question list and tailor questions accordingly.

[1510] 6. Users conduct surveys

[1511] The user distributes a list of questions to target users in the form of an online survey.

[1512] The user collects the survey results and stores them in a database.

[1513] 7. The device aggregates and analyzes the results

[1514] The survey results aggregated across devices are analyzed using a data analysis module.

[1515] The analysis results are visualized in graphs and charts in the visualization module.

[1516] Users can gain further insights based on the results of this analysis.

[1517] As described above, by utilizing the emotion engine, this system can obtain deeper insights and conduct efficient user surveys that are multinational and multicultural at low cost.

[1518] The processing flow will be explained below.

[1519] Step 1:

[1520] A user logs in to the system.

[1521] The user accesses the persona information input screen.

[1522] Step 2:

[1523] The user enters the following persona information:

[1524] age

[1525] sex

[1526] Occupation

[1527] nationality

[1528] Other options (hobbies, lifestyle, etc.)

[1529] Step 3:

[1530] The user checks the input and clicks the "Submit" button.

[1531] Step 4:

[1532] The server receives the persona information sent by the user.

[1533] A generation AI module within the server analyzes the persona information.

[1534] Step 5:

[1535] The server generates a realistic persona profile based on the given information.

[1536] Name (fictitious)

[1537] Detailed profile (hobbies, lifestyle, values, etc.)

[1538] Estimating needs and challenges

[1539] Step 6:

[1540] The server stores the generated personas in a database.

[1541] Step 7:

[1542] Based on the persona generated by the server, a list of questions for user surveys is generated.

[1543] Selecting questions based on the persona's needs and challenges

[1544] Step 8:

[1545] The server starts the emotion engine and collects the user's emotion data through the user's input information and interface operations.

[1546] The emotion data includes the user's happiness, sadness, anger, etc.

[1547] Step 9:

[1548] The server analyzes the emotional data and further refines the generated persona profile.

[1549] For example, if a user is feeling stressed, add that emotion to the persona's characteristics.

[1550] Step 10:

[1551] The server adjusts the questions in the survey questionnaire based on the emotional data.

[1552] Add or change emotion-based questions.

[1553] Step 11:

[1554] The server provides a survey template along with a list of survey questions.

[1555] The terminal receives the question list and template provided by the server.

[1556] Step 12:

[1557] The user checks the list of questions provided by the terminal.

[1558] The user selects the survey format (online survey, interview, etc.).

[1559] Step 13:

[1560] The survey is conducted in a format selected by the user.

[1561] For online surveys, distribute the survey link to target users.

[1562] In the case of interviews, schedules will be arranged with the interviewees.

[1563] Step 14:

[1564] The device stores the survey results in a database.

[1565] The device aggregates the survey results using a data aggregation module.

[1566] Step 15:

[1567] The device generates graphs and charts to visualize the aggregated results.

[1568] Step 16:

[1569] The device analyzes the analysis results and extracts key insights.

[1570] Step 17:

[1571] The device provides the user with analysis results and insights in the form of a report.

[1572] The above are the specific processing steps of the system program.

[1573] Example 2

[1574] 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."

[1575] Conventional survey systems have had difficulty efficiently completing the process from inputting user profile information to generating personas, creating survey question lists, and compiling and analyzing survey results. In particular, there were no systems that could refine personas and survey questions by taking user emotional data into account, which led to problems with reduced survey accuracy and reliability.

[1576] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting desired user profile information, a means for automatically generating a persona based on the user profile information, a means for automatically generating a survey question list based on the generated persona, a means for collecting emotional data from user input information and operation data, a means for analyzing the emotional data and refining the profile of the generated persona, a means for aggregating and analyzing the survey results, and a means for visualizing the analysis results and presenting them to the user. This solves the conventional problems and enables highly accurate surveys that take user emotional data into consideration.

[1577] "User profile information" is information about personal attributes and characteristics such as the user's age, sex, occupation, nationality, hobbies, and lifestyle.

[1578] A "persona" is a fictional character created based on user profile information, including a detailed profile, values, needs, and challenges.

[1579] "Generative AI" refers to algorithms and systems that use artificial intelligence to automatically generate personas and survey question lists.

[1580] A "survey questionnaire" is a list of questions that are automatically generated to address the needs and challenges of a persona.

[1581] "Emotion data" refers to data relating to emotional states such as joy, sadness, and anger, which are collected through user input information and interface operations.

[1582] A "database" is a system for storing and managing data such as persona information, survey question lists, and survey results.

[1583] "Data analysis module" refers to the functions and software that aggregate and analyze survey results to gain useful insights.

[1584] A "visualization module" is a function or tool for visually displaying the results of data analysis, using graphs, charts, etc.

[1585] An "emotion engine" is an algorithm or system that analyzes user input information and behavioral data to detect and evaluate emotional states.

[1586] An "interface" is a screen, form, or other means by which a user inputs information into a system.

[1587] MODE FOR CARRYING OUT THE INVENTION

[1588] This system uses a generative AI to automatically generate a persona based on user profile information, and then combines it with an emotion engine that recognizes the user's emotions to provide a list of survey questions and analyze and visualize the survey results. A specific embodiment is described below.

[1589] System Configuration

[1590] 1. User enters persona information

[1591] Users log in to the system using devices such as PCs or smartphones.

[1592] Users enter profile information such as age, gender, occupation, nationality, hobbies, and lifestyle on a dedicated persona information input screen.

[1593] For example, enter information such as "Age: 30," "Gender: Female," "Occupation: Marketing Manager," "Nationality: Japanese," "Hobbies: Fitness," and "Lifestyle: Works in an urban area and spends weekends actively," and click the "Submit" button to send the information to the server.

[1594] 2. The server automatically generates personas

[1595] The server receives the profile information sent by the user.

[1596] A generative AI module installed on the server analyzes the input information and generates a detailed persona.

[1597] The generated persona will include the following elements:

[1598] Name (e.g. "Yamada Hanako")

[1599] Hobbies (fitness, cooking)

[1600] Lifestyle (working in the city and being active on the weekends)

[1601] Values ​​(health-conscious, efficiency-oriented)

[1602] Needs (efficient tools and services to balance work and personal life)

[1603] Challenges (busy work schedule and limited leisure time)

[1604] The server stores the generated persona information in a database.

[1605] 3. The server automatically generates a list of survey questions

[1606] The server automatically generates a list of survey questions based on the persona information stored in the database.

[1607] The prompt sent to the generative AI is, "Create effective survey questions relevant to this persona."

[1608] Specific questions include:

[1609] “How did you learn about new health tools and services?”

[1610] "How often do you devote time to fitness?"

[1611] "What applications and devices do you use to stay healthy efficiently?"

[1612] The server stores the automatically generated survey questionnaire in a database.

[1613] 4. The server utilizes the emotion engine

[1614] The server starts the emotion engine and collects user input information and data from interface operations.

[1615] The data collected includes the user's emotional state, such as happiness, sadness, or anger.

[1616] For example, emotional data is analyzed based on facial expressions, tone of voice, click behavior, etc. when a user answers a question.

[1617] 5. The server uses emotion data

[1618] The server analyzes the collected emotional data to further refine the persona profile.

[1619] The emotional data is reflected in the survey questions, and the questions in the survey list are adjusted accordingly. For example, questions that are likely to cause stress to users are softened.

[1620] 6. Users conduct surveys

[1621] The user reviews the list of survey questions provided by the server.

[1622] Users choose an appropriate format to conduct the survey, such as an online survey or an interview.

[1623] Conduct a survey in the format of your choice and set up the system to store each response in a database.

[1624] 7. The device aggregates and analyzes the results

[1625] The survey results are compiled into a database.

[1626] The terminal launches a data analysis module to analyze the aggregated results.

[1627] The analyzed data can be visualized as follows:

[1628] Use graphs, charts, and other visualization techniques.

[1629] Users gain new insights based on the visualized results.

[1630] Prompt Sentence Examples

[1631] "A 30-year-old female marketing manager is looking for a new health tool. Please generate a persona for her."

[1632] "Generate personas based on the user's age (30 years old), gender (female), occupation (marketing manager), and nationality (Japanese), and create a list of relevant survey questions."

[1633] In this way, the system combines a generative AI model with an emotion engine to enable efficient and in-depth user research.

[1634] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1635] System program processing flow

[1636] Step 1:

[1637] User enters persona information

[1638] Input: Users log in to the system using a device such as a PC or smartphone and enter profile information such as age, gender, occupation, nationality, hobbies, and lifestyle on the persona information input screen.

[1639] Data processing: The entered information is temporarily stored in the device's memory and any necessary validation (e.g., checking the input format and required fields) is performed before transmission.

[1640] Output: When the user clicks the "Submit" button, the input information is sent to the server. Examples include "Age: 30," "Gender: Female," "Occupation: Marketing Manager," "Nationality: Japanese," "Hobbies: Fitness," and "Lifestyle: Works in a city and spends weekends actively."

[1641] Step 2:

[1642] The server automatically generates a persona

[1643] Input: The server receives the profile information sent by the user and temporarily stores it in a database.

[1644] Data calculation: The generative AI module installed on the server analyzes the received information. During the analysis, statistical and machine learning models are used to extract user characteristics based on the input data, and a persona is generated.

[1645] Output: The generated persona information is saved in a database. The generated persona includes information such as name (e.g., Hanako Yamada), hobbies (fitness, cooking), lifestyle (works in an urban area, spends weekends actively), values ​​(health-conscious, emphasizes efficiency), needs (efficient tools and services for balancing work and personal life), and challenges (busy work, limited leisure time).

[1646] Step 3:

[1647] The server automatically generates a list of survey questions.

[1648] Input: The server retrieves the persona information stored in the database.

[1649] Data calculation: The server sends a prompt to the generation AI module, saying, "Please create effective survey questions related to this persona." The generation AI analyzes the persona information and generates a corresponding list of survey questions.

[1650] Output: The server stores the automatically generated survey questions in a database. Specific example questions include, "How do you learn about new health tools and services?", "How often do you devote time to fitness?", and "What applications and devices do you use to effectively maintain your health?"

[1651] Step 4:

[1652] The server utilizes the emotion engine

[1653] Input: Collects user input and interface interaction data, such as facial expressions, tone of voice, and click behavior when answering questions.

[1654] Data calculation: The emotion engine analyzes the collected data and detects the user's emotional state (happiness, sadness, anger, etc.).

[1655] Output: The detected emotion data is stored in a database for further analysis and profile refinement.

[1656] Step 5:

[1657] The server uses the emotion data

[1658] Input: Obtain emotional data and persona information stored in the database.

[1659] Data calculation: The persona profile is further refined based on emotional data. For example, questions that are likely to cause stress to the user may be softened, and the content and order of questions may be adjusted according to the user's emotions.

[1660] Output: Updated personas and survey question list saved to database.

[1661] Step 6:

[1662] A user conducts a survey

[1663] Input: Get the latest list of survey questions provided by the server.

[1664] Specific operation: The user selects an appropriate survey format (such as an online questionnaire or interview) and conducts the survey with target users. The survey questions are displayed on the user's device, and the user enters the answers.

[1665] Output: Each answer is saved in a database in real time.

[1666] Step 7:

[1667] The device aggregates and analyzes the results

[1668] Input: Retrieve survey results stored in the database.

[1669] Data calculation: The data analysis module installed on the device aggregates and analyzes the survey results. Analysis methods include statistical analysis and text mining.

[1670] Output: The analysis results are visualized through the visualization module. The results are displayed in graphs, charts, etc. and provided to the user, allowing the user to gain new insights.

[1671] As described above, this system realizes efficient and precise user research through each processing step.

[1672] (Application example 2)

[1673] 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."

[1674] Many modern commercial systems struggle to provide personalized services that accurately reflect customer emotions and behavioral characteristics. This results in low customer satisfaction and a lack of improvement in user experience. Furthermore, conventional systems are unable to accurately analyze survey results by analyzing user emotions and respond to diverse customer needs. A method to resolve these issues and provide more effective and personalized customer service is needed.

[1675] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting desired user profile information, means for automatically generating a persona based on the user profile information, means for automatically generating a survey question list based on the generated persona, means for providing the survey question list to the user, means for the user to conduct the survey, means for further refining the survey results using an emotion engine that analyzes the user's emotions, means for aggregating and analyzing the survey results, and means for visualizing the analysis results and presenting them to the user. This makes it possible to analyze user emotion data and refine the survey results. This makes it possible to provide more personalized and effective customer service and improve customer satisfaction.

[1676] "User profile information" refers to personal information such as a user's age, gender, occupation, hobbies, and lifestyle.

[1677] A "persona" is a virtual image of a user that is generated based on user profile information, and includes the user's name, hobbies, lifestyle, values, needs, challenges, etc.

[1678] "Survey Question List" refers to a list of questions that are automatically generated based on the generated personas.

[1679] An "emotion engine" refers to technology that analyzes a user's facial expressions, tone of voice, text input, etc., to recognize emotional states such as joy, sadness, and anger in real time.

[1680] "Aggregation" refers to the compilation of survey results obtained from users.

[1681] "Analysis" refers to the analysis of aggregated data.

[1682] "Visualization" refers to presenting analytical results in a visual format such as graphs or charts and providing them to users.

[1683] "Interface" refers to the means by which a user inputs information into or receives information from a system.

[1684] "Generative AI" refers to artificial intelligence technology that automatically generates content and information based on given data.

[1685] A "database" refers to a system for storing and managing information in an organized manner.

[1686] "Data Analysis Module" refers to a software component for analyzing collected data.

[1687] "Visualization Module" refers to a software component for visually representing the results of data analysis.

[1688] MODE FOR CARRYING OUT THE INVENTION

[1689] This invention is a system that automatically generates personas based on user profile information, incorporates an emotion engine that recognizes user emotions, provides a survey question list, and analyzes and visualizes survey results. The system of this invention operates based on information entered by the user.

[1690] Program Generation

[1691] The server executes the system program according to the following procedure.

[1692] A natural language description of what the program does

[1693] 1. Enter your user profile information:

[1694] Users log in from a device such as a smartphone or tablet and enter profile information such as their name, age, gender, occupation, hobbies, and lifestyle. This information is accepted through the interface and sent to the server.

[1695] 2. Automatic persona generation:

[1696] Based on the received user profile information, the server uses a generative AI model (e.g., GPT-3) to automatically generate a detailed persona, which includes detailed information about the user's hobbies, lifestyle, values, and needs.

[1697] 3. Automatic generation of survey questions:

[1698] The server automatically generates a list of survey questions based on the persona. For example, if the persona is a "30-year-old woman interested in fitness," a survey list containing fitness-related questions will be generated.

[1699] 4. Leveraging the Emotion Engine:

[1700] During the survey, the server activates an emotion engine to collect real-time emotional data from the user's facial expressions and tone of voice, allowing analysis of the emotional state in which the user provided their answers.

[1701] 5. Elaboration and analysis of findings:

[1702] The server uses the collected emotional data to further refine the generated persona information. It also reflects the emotional data in the questions in the survey questionnaire to make more appropriate adjustments. Once the survey is complete, the user's responses are stored in a database and analyzed by the data analysis module.

[1703] 6. Visualizing the results:

[1704] The analysis results are visualized in the form of graphs and charts through the visualization module and provided to users, allowing them to intuitively understand the survey results.

[1705] Specific examples

[1706] For example, a user enters the following profile information:

[1707] Age: 35

[1708] Gender: Male

[1709] Occupation: Software Engineer

[1710] Hobbies: Reading, games

[1711] Lifestyle: Urban areas, many work from home

[1712] An example of a prompt for a generative AI model is:

[1713] "35-year-old male, software engineer, hobbies include reading and gaming, lifestyle is urban, mostly working from home. Please generate a persona based on this information."

[1714] The persona generated in this way can depict a specific character, such as "an engineer who loves games and places importance on ways to relax while working from home." A list of fitness and relaxation-related survey questions is automatically generated based on this persona, and an emotion engine is used to collect and analyze emotional data in real time, allowing for a personalized experience tailored to the user.

[1715] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1716] Step 1:

[1717] A user logs in from a device such as a smartphone or tablet and enters profile information such as name, age, gender, occupation, hobbies, and lifestyle. The entered information is sent to the server through the interface, allowing the server to receive (input) the user profile information. Specifically, the user enters information into a form and presses the "Submit" button.

[1718] Step 2:

[1719] The server automatically generates a detailed persona using a generative AI model (e.g., GPT-3) based on the received user profile information. At this time, it analyzes the input profile information and provides a prompt to the generative AI model (data processing). For example, if the profile information is "35-year-old male, software engineer, hobbies are reading and gaming," the prompt would be "35-year-old male, software engineer, hobbies are reading and gaming. Please generate a persona based on this information." The generated persona is returned to the device (output).

[1720] Step 3:

[1721] The server automatically generates a list of survey questions based on the generated persona. At this time, it analyzes the information of the generated persona and generates questions that suit the persona's tastes and needs (data calculation). For example, if the persona is a "30-year-old woman who is interested in fitness," a list including fitness-related questions will be generated. The generated list of survey questions is provided to the terminal (output).

[1722] Step 4:

[1723] The user receives a list of survey questions provided by the terminal. The user conducts the survey based on the list and answers the questions. At this time, the user's answers are sent (input) to the server through the interface. Specifically, the user answers the questions in a questionnaire format and presses the "send" button.

[1724] Step 5:

[1725] The server activates the emotion engine and collects emotional data in real time from the user's facial expressions and tone of voice. The server recognizes the user's emotional state based on the user's input information and interface operations, and stores the analysis results in a database (data calculation). For example, the server collects and analyzes the user's facial expressions and tone of voice when answering questions using a camera or microphone.

[1726] Step 6:

[1727] The server uses the collected emotional data to further refine the generated persona information. Furthermore, it reflects the emotional data in the questions in the survey questionnaire to make more appropriate adjustments (data calculation). For example, if the user is feeling stressed, it changes the questions in the questionnaire to reflect that emotion more gently. The refined persona and the adjusted questionnaire are saved in the database (output).

[1728] Step 7:

[1729] Once the survey is complete, the server collects the responses from users and stores them in a database (input). The server then analyzes the responses using a data analysis module, analyzing the aggregated data and performing statistical processing (data calculations). For example, the response data may be aggregated and analyzed for trends and patterns.

[1730] Step 8:

[1731] The server uses a visualization module to visualize the analysis results. At this time, the analysis results are converted into visual formats such as graphs and charts and provided to the user (output). Specifically, the analysis results are converted into diagrams and charts, and dashboards and reports are generated. This allows the user to intuitively understand the investigation results.

[1732] 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.

[1733] 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.

[1734] 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.

[1735] [Fourth embodiment]

[1736] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1737] 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.

[1738] 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).

[1739] 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.

[1740] 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.

[1741] 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).

[1742] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1743] 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.

[1744] 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.

[1745] 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.

[1746] 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.

[1747] 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.

[1748] 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."

[1749] MODE FOR CARRYING OUT THE INVENTION

[1750] This invention is a system that automatically generates personas using generation AI based on user profile information and automatically provides a list of survey questions based on those personas in order to conduct user surveys efficiently and at low cost.

[1751] Program processing overview

[1752] 1. User enters persona information

[1753] The user logs in to the system and accesses the persona information input screen.

[1754] Users enter profile information such as age, gender, occupation, nationality, hobbies, and lifestyle.

[1755] After inputting, the user clicks the "Submit" button to send the information to the system.

[1756] 2. The server automatically generates personas

[1757] The server receives the profile information sent by the user.

[1758] The server's generation AI analyzes the profile information and generates a detailed persona.

[1759] This persona includes name, hobbies, lifestyle, values, needs, challenges, etc.

[1760] The generated personas are stored in a database.

[1761] 3. The server automatically generates a list of survey questions

[1762] A list of survey questions is automatically generated based on the persona generated by the server.

[1763] Your research questionnaire will include questions that address the needs and challenges of your personas.

[1764] 4. Users conduct surveys

[1765] The user receives a list of survey questions provided by the server.

[1766] The user selects the survey format (online survey, interview, etc.).

[1767] The user conducts the survey in the selected format.

[1768] 5. The device aggregates and analyzes the results

[1769] The survey results are stored in a database.

[1770] The device aggregates and analyzes the survey results using a data analysis module.

[1771] The analysis results are visualized through the visualization module and provided to the user.

[1772] Specific examples

[1773] 1. User enters persona information

[1774] As an example, a user wants to conduct target market research for a new product.

[1775] The user enters the following persona information:

[1776] Age: 30

[1777] Gender: Female

[1778] Occupation: Marketing Manager

[1779] Nationality: Japanese

[1780] 2. The server automatically generates personas

[1781] Based on the information received by the server, the generation AI generates a persona.

[1782] The generated personas include the following details:

[1783] Name: Yamada Hanako (fictional character)

[1784] Hobbies: Fitness, cooking

[1785] Lifestyle: City work, active weekends

[1786] Values: Health-conscious, efficiency-oriented

[1787] Needs: Seeking efficient tools and services to balance work and personal life

[1788] Challenge: Busy work schedule and limited leisure time

[1789] 3. The server automatically generates a list of survey questions

[1790] A list of survey questions is automatically generated based on the persona generated by the server.

[1791] The survey questionnaire includes questions such as:

[1792] “How did you learn about new health tools and services?”

[1793] "How often do you devote time to fitness?"

[1794] "What applications and devices do you use to stay healthy efficiently?"

[1795] 4. Users conduct surveys

[1796] The user distributes a list of questions to target users in the form of an online survey.

[1797] The user collects the survey results and stores them in a database.

[1798] 5. The device aggregates and analyzes the results

[1799] The survey results aggregated across devices are analyzed using a data analysis module.

[1800] The analysis results are visualized in graphs and charts in the visualization module.

[1801] Users can gain further insights based on the results of this analysis.

[1802] In this way, this system makes it possible to conduct efficient user surveys that are multinational and multicultural at low cost.

[1803] The processing flow will be explained below.

[1804] Step 1:

[1805] A user logs in to the system.

[1806] The user accesses the persona information input screen.

[1807] Step 2:

[1808] The user enters the following persona information:

[1809] age

[1810] sex

[1811] Occupation

[1812] nationality

[1813] Other options (hobbies, lifestyle, etc.)

[1814] Step 3:

[1815] The user checks the input and clicks the "Submit" button.

[1816] Step 4:

[1817] The server receives the persona information sent by the user.

[1818] A generation AI module within the server analyzes the persona information.

[1819] Step 5:

[1820] The server generates a realistic persona profile based on the given information.

[1821] Name (fictitious)

[1822] Detailed profile (hobbies, lifestyle, values, etc.)

[1823] Estimating needs and challenges

[1824] Step 6:

[1825] The server stores the generated personas in a database.

[1826] Step 7:

[1827] Based on the persona generated by the server, a list of questions for user surveys is generated.

[1828] Selecting questions based on the persona's needs and challenges

[1829] Step 8:

[1830] The server provides a survey template along with a list of questions.

[1831] Step 9:

[1832] The terminal receives the question list and template provided by the server.

[1833] Step 10:

[1834] The user checks the list of questions provided by the terminal.

[1835] Step 11:

[1836] The user selects the survey format (online survey, interview, etc.).

[1837] Step 12:

[1838] The survey is conducted in a format selected by the user.

[1839] For online surveys, distribute the survey link to target users.

[1840] In the case of interviews, schedules will be arranged with the interviewees.

[1841] Step 13:

[1842] The device stores the survey results in a database.

[1843] Step 14:

[1844] The device aggregates the survey results using a data aggregation module.

[1845] Step 15:

[1846] The device generates graphs and charts to visualize the aggregated results.

[1847] Step 16:

[1848] The device analyzes the analysis results and extracts key insights.

[1849] Step 17:

[1850] The device provides the user with analysis results and insights in the form of a report.

[1851] The above are the specific processing steps of the system program.

[1852] Example 1

[1853] 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."

[1854] In modern market research, it is important to efficiently generate accurate personas based on user profile information and create survey question lists based on them. However, traditional methods require manually creating personas and building survey question lists, which is time-consuming and costly. Furthermore, the process of analyzing and visualizing survey results is cumbersome, making it difficult to make quick decisions.

[1855] 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.

[1856] In this invention, the server includes: means for inputting desired user profile information; means for automatically generating a persona using a generative AI model based on the user profile information; means for automatically generating a survey question list based on the generated persona; means for providing the survey question list to a user; means for the user to conduct a survey in the form of an online questionnaire or interview; means for aggregating and analyzing the survey results using a data analysis module; and means for visualizing the data analysis results using a visualization module and presenting them to the user. This enables the server to quickly and efficiently generate a persona and survey question list from user profile information, and then analyze, visualize, and provide the results. "User profile information" refers to attribute information entered by the user, such as age, gender, occupation, nationality, hobbies, and lifestyle.

[1857] A "generative AI model" is an artificial intelligence algorithm used to generate detailed personas from input user profile information.

[1858] A "persona" is a fictional user image that is automatically generated based on user profile information using a generative AI model.

[1859] A "survey question list" is a list of questions that are automatically generated based on the needs and challenges of the generated persona.

[1860] "Data Analysis Module" means a software module for analyzing aggregated survey results.

[1861] A "visualization module" is a software module for visually representing analyzed data.

[1862] An "online survey" is a form of survey conducted via the Internet.

[1863] The "interview format" is a survey format in which data is collected through interviews.

[1864] This invention is a system that automatically generates a persona using a generative AI model based on user profile information and automatically provides a survey question list based on that persona. This system can quickly and efficiently generate a persona and a survey question list from the profile information entered by the user, and then analyze and visualize the results before providing them.

[1865] Hardware and software used

[1866] The main hardware and software required to implement this system are:

[1867] 1. Server: A computer with a high-performance processor and a large amount of memory is required. For example, AWS (Amazon Web Services) or Google Cloud Platform can be used.

[1868] 2. Generative AI models: We need AI models that are specialized for natural language processing and generative tasks, such as OpenAI's GPT-4 model.

[1869] 3. Database: Used to store user profile information, generated personas, survey questions, and survey results. For example, a relational database such as PostgreSQL or MySQL can be used.

[1870] 4. Data analysis module: Survey results are compiled and analyzed using Python's Pandas library and statistical software R.

[1871] 5. Visualization module: Visualize data analysis results using tools such as Python's Matplotlib and Tableau.

[1872] Program processing overview

[1873] 1. User enters persona information

[1874] A user logs in to the system interface and accesses the persona information input screen, where they enter profile information such as age, gender, occupation, nationality, hobbies, and lifestyle, and then clicks the "Submit" button to send the information to the server.

[1875] 2. The server automatically generates personas

[1876] Based on the profile information received by the server, a detailed persona is generated using a generative AI model (e.g., GPT-4). This process uses the following prompt:

[1877] "We have received the following profile information from a user: Age: 30, Gender: Female, Occupation: Marketing Manager, Nationality: Japanese. Please generate a detailed persona based on this information. The persona should include name, hobbies, lifestyle, values, needs, and challenges."

[1878] The generated personas are stored in a database.

[1879] 3. The server automatically generates a list of survey questions

[1880] Based on the personas generated by the server, a generative AI model is used to automatically generate a list of survey questions, using the following prompts in the process:

[1881] “Generate a list of research questions that will work for this persona. Create questions based on their needs and challenges.”

[1882] The generated survey question list is stored on the server side and provided to the user.

[1883] 4. Users conduct surveys

[1884] Users obtain a list of survey questions provided by the system and conduct the survey in the form of an online questionnaire (e.g., Google Forms) or an interview. After the survey results are collected, they are stored in a database.

[1885] 5. The device aggregates and analyzes the results

[1886] The survey results are analyzed using a data analysis module, such as Python's Pandas library, to clean the data and perform statistical analysis. The analysis results are then visualized in graphs and charts using a visualization module (e.g., Tableau or Matplotlib) and provided to the user.

[1887] Specific examples

[1888] For example, consider a user conducting target market research for a new product. The user logs into the system and enters the following persona information: Age: 30, Gender: Female, Occupation: Marketing Manager, Nationality: Japanese. The server uses this information to generate the following detailed persona using a generative AI model:

[1889] Name: Yamada Hanako (fictional character)

[1890] Hobbies: Fitness, cooking

[1891] Lifestyle: City work, active weekends

[1892] Values: Health-conscious, efficiency-oriented

[1893] Needs: Seeking efficient tools and services to balance work and personal life

[1894] Challenge: Busy work schedule and limited leisure time

[1895] The server then generates the following list of survey questions based on this persona:

[1896] “How did you learn about new health tools and services?”

[1897] "How often do you devote time to fitness?"

[1898] "What applications and devices do you use to stay healthy efficiently?"

[1899] Users conduct surveys in the form of online questionnaires and collect the results. The terminal analyzes the survey results using the data analysis module and visualizes them in graphs and charts using the visualization module. The analysis results are provided to users to gain more specific market insights.

[1900] The above is a detailed description of an embodiment of this system.

[1901] The flow of the specific processing in the first embodiment will be described with reference to FIG. 11.

[1902] Step 1:

[1903] The user enters persona information. Specifically, the user logs in to the system interface, accesses the persona information input screen, and enters profile information such as age, gender, occupation, nationality, hobbies, and lifestyle into the form. After completing the input, the user clicks the "Submit" button to send the information to the server. The input data is sent to the server as the user's profile information.

[1904] Input: User persona information (age, gender, occupation, nationality, hobbies, lifestyle, etc.)

[1905] Output: User profile information data sent to the server

[1906] Step 2:

[1907] The server automatically generates a persona. The server receives profile information sent by the user. Based on this information, the server calls the generative AI model to create a prompt sentence and input it into the AI ​​model. The generative AI model generates a detailed persona based on the user's profile information and stores the generated persona data in the server's database.

[1908] Input: User profile information data

[1909] Output: Generated persona data (name, hobbies, lifestyle, values, needs, challenges, etc.)

[1910] Step 3:

[1911] The server automatically generates a list of survey questions. Based on the generated persona data, the server uses a generative AI model to generate a list of survey questions. This prompt sentence is created and input into the generative AI model to generate a list of questions. The generated list of survey questions is stored in the server's database.

[1912] Input: Generated persona data

[1913] Output: Generated survey questionnaire

[1914] Step 4:

[1915] The server provides the survey questionnaire to the user. The server provides a link or download option that the user can access and presents the survey questionnaire to the user. The user retrieves it and saves it in a usable form.

[1916] Input: Generated survey questionnaire list

[1917] Output: Survey questionnaire link or file provided to the user

[1918] Step 5:

[1919] The user conducts the survey. The user receives the survey question list provided by the server and conducts the survey in the form of an online questionnaire (e.g., Google Forms) or an interview. The collected data is then uploaded back to the server by the user.

[1920] Input: Survey Questionnaire

[1921] Output: Collected survey data

[1922] Step 6:

[1923] The server aggregates the survey results and performs analysis on the terminal. The server provides the survey data received from the user to the data analysis module, which aggregates and cleans the data and performs statistical analysis. The results are stored in a database.

[1924] Input: Collected survey data

[1925] Output: Aggregated and analyzed data

[1926] Step 7:

[1927] The terminal visualizes the aggregated and analyzed results, and the server presents them to the user. The terminal uses a visualization module to visualize the aggregated and analyzed data in graphs and charts, which the server presents to the user, providing the visualized results.

[1928] Input: Aggregated and analyzed data

[1929] Output: Visualized data (graphs and charts)

[1930] Based on the specific actions taken at each step and the input / output data, the system can quickly and effectively generate personas and survey question lists from user profile information, analyze and visualize the results, and provide them to users.

[1931] (Application example 1)

[1932] 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."

[1933] In recent years, formulating effective marketing and advertising strategies that take into account diverse user profiles has become difficult and requires a great deal of time and money. Furthermore, there is a lack of methods for accurately grasping the detailed needs and values ​​of target users and automatically designing optimal advertising content and formats based on that. This has led to problems such as advertisers being unable to quickly formulate appropriate strategies, resulting in reduced advertising effectiveness.

[1934] 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.

[1935] In this invention, the server includes a means for inputting desired user profile information, a means for automatically generating a persona based on the user profile information, and a means for automatically generating a survey question list based on the generated persona, thereby enabling a target effect.

[1936] By building a system that includes a means for optimizing advertising content and format based on the generated personas and survey question list, it becomes possible to quickly and low-costly develop effective advertising strategies that cater to diverse user profiles.

[1937] "User profile information" is a general term for data that includes attribute information about a specific user, such as age, sex, occupation, nationality, hobbies, and lifestyle.

[1938] A "persona" is a fictional image of a user that is generated based on user profile information and that reflects in detail the user's typical needs, values, behavioral patterns, and challenges.

[1939] A "survey questionnaire" is a list of questions that includes questions that address the needs, values, and challenges of users based on the generated persona.

[1940] "Means for optimizing advertising content and formats" refers to algorithms and tools that automatically design and select the most effective advertising messages and formats for target users based on the generated personas and survey question lists.

[1941] The "data analysis module" is a software component for aggregating and analyzing survey results, and analyzes data using methods such as statistical analysis and machine learning.

[1942] The "visualization module" is a software component for visually displaying the results of the analysis performed by the data analysis module, and visualizing them in the form of graphs, charts, etc.

[1943] A "generative AI model" is an AI model that automatically generates personas and survey question lists based on user profile information, and uses technologies such as natural language processing.

[1944] An "interface" is an input means or screen that allows a user to input information to a system, or a means for receiving output.

[1945] "Database" means a data storage system for storing and managing survey questionnaires and survey results.

[1946] This invention is a system that automatically generates personas based on user profile information using generation AI and automatically provides a list of survey questions based on the personas in order to conduct user surveys efficiently and at low cost. Advertising content and format can then be optimized based on the generated personas and survey questions. The program and processing required to realize this system are described below.

[1947] First, a user accesses the system and enters user profile information through an interface that is compatible with a variety of devices, including smartphones and head-mounted displays, and allows users to enter information such as age, gender, occupation, nationality, hobbies, and lifestyle.

[1948] The server automatically generates a persona based on the user profile information using a generative AI model (e.g., OpenAI's GPT-3). This persona includes details such as name, hobbies, lifestyle, values, needs, and challenges, creating a detailed image of the user.

[1949] The server then automatically generates a survey questionnaire based on the personas. This questionnaire contains specific questions that address the needs, values, and challenges of the target users. The survey questionnaire is then saved in a database for future reference.

[1950] Users can conduct surveys based on a list of survey questions provided by the server. The survey format can be selected, such as an online questionnaire or an interview. The resulting survey data is stored in a database.

[1951] The data analysis module then aggregates the survey data, and the visualization module visualizes the analyzed results, helping users gain insights into the data.

[1952] Furthermore, based on the generated personas and survey question list, the server provides a means to optimize advertising content and formats by using a generative AI model to automatically design and select the most effective advertising messages and formats.

[1953] Examples:

[1954] For example, let's say a marketer for a health food brand launching a new product uses this system to research the target market. The marketer would enter the following profile information:

[1955] Age: 30

[1956] Gender: Female

[1957] Occupation: Marketing Manager

[1958] Nationality: Japanese

[1959] Hobbies: Fitness, cooking

[1960] Based on this information, the generative AI model generates a persona like this:

[1961] Name: Yamada Hanako (fictional character)

[1962] Lifestyle: City work, active weekends

[1963] Values: Health-conscious, efficiency-oriented

[1964] Based on the persona, a list of survey questions is generated, such as:

[1965] “How did you learn about new health tools and services?”

[1966] "How often do you devote time to fitness?"

[1967] This information provides specific steps and results that generate optimal strategies for optimizing advertising content and formats.

[1968] Example prompt sentence:

[1969] Age: 30

[1970] Gender: Female

[1971] Occupation: Marketing Manager

[1972] Nationality: Japanese

[1973] Hobbies: Fitness, cooking

[1974] Based on the user profile information above, generate the following persona information:

[1975] name

[1976] hobby

[1977] Lifestyle

[1978] values

[1979] needs

[1980] assignment

[1981] In this way, by using this system, advertisers can efficiently and effectively optimize their targeted advertising.

[1982] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1983] Step 1:

[1984] A user logs in to the system and enters user profile information through the interface, including age, gender, occupation, nationality, hobbies, lifestyle, etc., so that the system can obtain the required profile data.

[1985] Input: User profile information (age, gender, occupation, nationality, hobbies, lifestyle)

[1986] Output: Retrieved user profile information

[1987] Step 2:

[1988] The server receives the acquired user profile information and passes it to a generative AI model to automatically generate a persona. The generative AI model (e.g., OpenAI's GPT-3) analyzes the input data and generates a persona. This persona includes a fictitious name, hobbies, lifestyle, values, needs, challenges, etc.

[1989] Input: User profile information

[1990] Output: Generated personas

[1991] Data processing: Persona generation using generative AI models

[1992] Step 3:

[1993] The server generates a list of survey questions based on the generated persona, and automatically creates specific survey questions based on the attributes, needs, and issues of the generated persona.

[1994] Input: Generated persona

[1995] Output: Survey Question List

[1996] Data Calculation: Question List Generation Based on Persona Information

[1997] Step 4:

[1998] The server provides the user with a list of survey questions, which are then stored in a database for future reference. The user then selects the survey format (online questionnaire, interview, etc.) based on the provided list of questions.

[1999] Input: Survey Questionnaire

[2000] Output: Provided survey question list

[2001] Specific operation: The user selects the survey format

[2002] Step 5:

[2003] Users conduct surveys in the format of their choice, and the survey results are stored in a database for later analysis.

[2004] Input: Survey response

[2005] Output: Saved survey results

[2006] Specific operation: Saving to the database

[2007] Step 6:

[2008] The server uses a data analysis module to analyze the collected survey results, which extracts user behavior patterns, needs, values, etc.

[2009] Input: Saved Survey Results

[2010] Output: Analysis results

[2011] Data calculations: Analysis of survey results

[2012] Step 7:

[2013] The server visualizes the analysis results through a visualization module, allowing users to receive the analysis results in a visually easy-to-understand format.

[2014] Input: Analysis results

[2015] Output: Visualized analysis results

[2016] Specific behavior: Generating graphs and charts

[2017] Step 8:

[2018] Based on the generated personas and survey question list, the server uses methods to optimize the advertising content and format, utilizing a generative AI model to automatically design and select the optimal advertising message and format for the target.

[2019] Input: Persona, survey question list, analysis results

[2020] Output: Optimized ad content and format

[2021] Data processing: Ad optimization with generative AI models

[2022] Through the above processing steps, this system can accommodate a variety of profiles and can formulate effective advertising strategies at low cost and efficiently.

[2023] 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.

[2024] MODE FOR CARRYING OUT THE INVENTION

[2025] This invention is a system that automatically generates personas using generative AI based on user profile information, and then combines it with an emotion engine that recognizes the user's emotions to provide a list of survey questions and analyze and visualize the survey results.

[2026] Program processing overview

[2027] 1. User enters persona information

[2028] The user logs in to the system and accesses the persona information input screen.

[2029] Users enter profile information such as age, gender, occupation, nationality, hobbies, and lifestyle.

[2030] After inputting, the user clicks the "Submit" button to send the information to the system.

[2031] 2. The server automatically generates personas

[2032] The server receives the profile information sent by the user.

[2033] The server's generation AI module analyzes the profile information and generates a detailed persona.

[2034] This persona includes name, hobbies, lifestyle, values, needs, challenges, etc.

[2035] The generated personas are stored in a database.

[2036] 3. The server automatically generates a list of survey questions

[2037] A list of survey questions is automatically generated based on the persona generated by the server.

[2038] Your research questionnaire will include questions that address the needs and challenges of your personas.

[2039] 4. The server utilizes the emotion engine

[2040] The server starts the emotion engine and collects the user's emotion data through the user's input information and interface operations.

[2041] The emotional data includes the user's emotional state, such as happiness, sadness, or anger.

[2042] 5. The server uses emotion data

[2043] The server analyzes the collected emotional data and further refines the generated persona profile.

[2044] The emotional data is reflected in the questions in the survey questionnaire.

[2045] 6. Users conduct surveys

[2046] The user receives a list of survey questions provided by the server.

[2047] The user selects the survey format (online survey, interview, etc.).

[2048] The user conducts the survey in the selected format.

[2049] 7. The device aggregates and analyzes the results

[2050] The survey results are stored in a database.

[2051] The device aggregates and analyzes the survey results using a data analysis module.

[2052] The analysis results are visualized through the visualization module and provided to the user.

[2053] Specific examples

[2054] 1. User enters persona information

[2055] As an example, a user wants to conduct target market research for a new product.

[2056] The user enters the following persona information:

[2057] Age: 30

[2058] Gender: Female

[2059] Occupation: Marketing Manager

[2060] Nationality: Japanese

[2061] 2. The server automatically generates personas

[2062] The generation AI generates a persona based on the information received by the server.

[2063] The generated personas include the following details:

[2064] Name: Yamada Hanako (fictional character)

[2065] Hobbies: Fitness, cooking

[2066] Lifestyle: City work, active weekends

[2067] Values: Health-conscious, efficiency-oriented

[2068] Needs: Seeking efficient tools and services to balance work and personal life

[2069] Challenge: Busy work schedule and limited leisure time

[2070] 3. The server automatically generates a list of survey questions

[2071] A list of survey questions is automatically generated based on the persona generated by the server.

[2072] The survey questionnaire includes questions such as:

[2073] “How did you learn about new health tools and services?”

[2074] "How often do you devote time to fitness?"

[2075] "What applications and devices do you use to stay healthy efficiently?"

[2076] 4. The server utilizes the emotion engine

[2077] The server starts the emotion engine and collects emotion data based on the user's input information.

[2078] For example, it is analyzed whether the user feels stressed while answering a questionnaire.

[2079] 5. The server uses emotion data

[2080] The server analyzes the emotional data and further refines the persona profile.

[2081] Incorporate sentiment data into your survey question list and tailor questions accordingly.

[2082] 6. Users conduct surveys

[2083] The user distributes a list of questions to target users in the form of an online survey.

[2084] The user collects the survey results and stores them in a database.

[2085] 7. The device aggregates and analyzes the results

[2086] The survey results aggregated across devices are analyzed using a data analysis module.

[2087] The analysis results are visualized in graphs and charts in the visualization module.

[2088] Users can gain further insights based on the results of this analysis.

[2089] As described above, by utilizing the emotion engine, this system can obtain deeper insights and conduct efficient user surveys that are multinational and multicultural at low cost.

[2090] The processing flow will be explained below.

[2091] Step 1:

[2092] A user logs in to the system.

[2093] The user accesses the persona information input screen.

[2094] Step 2:

[2095] The user enters the following persona information:

[2096] age

[2097] sex

[2098] Occupation

[2099] nationality

[2100] Other options (hobbies, lifestyle, etc.)

[2101] Step 3:

[2102] The user checks the input and clicks the "Submit" button.

[2103] Step 4:

[2104] The server receives the persona information sent by the user.

[2105] A generation AI module within the server analyzes the persona information.

[2106] Step 5:

[2107] The server generates a realistic persona profile based on the given information.

[2108] Name (fictitious)

[2109] Detailed profile (hobbies, lifestyle, values, etc.)

[2110] Estimating needs and challenges

[2111] Step 6:

[2112] The server stores the generated personas in a database.

[2113] Step 7:

[2114] Based on the persona generated by the server, a list of questions for user surveys is generated.

[2115] Selecting questions based on the persona's needs and challenges

[2116] Step 8:

[2117] The server starts the emotion engine and collects the user's emotion data through the user's input information and interface operations.

[2118] The emotion data includes the user's happiness, sadness, anger, etc.

[2119] Step 9:

[2120] The server analyzes the emotional data and further refines the generated persona profile.

[2121] For example, if a user is feeling stressed, add that emotion to the persona's characteristics.

[2122] Step 10:

[2123] The server adjusts the questions in the survey questionnaire based on the emotional data.

[2124] Add or change emotion-based questions.

[2125] Step 11:

[2126] The server provides a survey template along with a list of survey questions.

[2127] The terminal receives the question list and template provided by the server.

[2128] Step 12:

[2129] The user checks the list of questions provided by the terminal.

[2130] The user selects the survey format (online survey, interview, etc.).

[2131] Step 13:

[2132] The survey is conducted in a format selected by the user.

[2133] For online surveys, distribute the survey link to target users.

[2134] In the case of interviews, schedules will be arranged with the interviewees.

[2135] Step 14:

[2136] The device stores the survey results in a database.

[2137] The device aggregates the survey results using a data aggregation module.

[2138] Step 15:

[2139] The device generates graphs and charts to visualize the aggregated results.

[2140] Step 16:

[2141] The device analyzes the analysis results and extracts key insights.

[2142] Step 17:

[2143] The device provides the user with analysis results and insights in the form of a report.

[2144] The above are the specific processing steps of the system program.

[2145] Example 2

[2146] 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."

[2147] Conventional survey systems have had difficulty efficiently completing the process from inputting user profile information to generating personas, creating survey question lists, and compiling and analyzing survey results. In particular, there were no systems that could refine personas and survey questions by taking user emotional data into account, which led to problems with reduced survey accuracy and reliability.

[2148] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting desired user profile information, a means for automatically generating a persona based on the user profile information, a means for automatically generating a survey question list based on the generated persona, a means for collecting emotional data from user input information and operation data, a means for analyzing the emotional data and refining the profile of the generated persona, a means for aggregating and analyzing the survey results, and a means for visualizing the analysis results and presenting them to the user. This solves the conventional problems and enables highly accurate surveys that take user emotional data into consideration.

[2149] "User profile information" is information about personal attributes and characteristics such as the user's age, sex, occupation, nationality, hobbies, and lifestyle.

[2150] A "persona" is a fictional character created based on user profile information, including a detailed profile, values, needs, and challenges.

[2151] "Generative AI" refers to algorithms and systems that use artificial intelligence to automatically generate personas and survey question lists.

[2152] A "survey questionnaire" is a list of questions that are automatically generated to address the needs and challenges of a persona.

[2153] "Emotion data" refers to data relating to emotional states such as joy, sadness, and anger, which are collected through user input information and interface operations.

[2154] A "database" is a system for storing and managing data such as persona information, survey question lists, and survey results.

[2155] "Data analysis module" refers to the functions and software that aggregate and analyze survey results to gain useful insights.

[2156] A "visualization module" is a function or tool for visually displaying the results of data analysis, using graphs, charts, etc.

[2157] An "emotion engine" is an algorithm or system that analyzes user input information and behavioral data to detect and evaluate emotional states.

[2158] An "interface" is a screen, form, or other means by which a user inputs information into a system.

[2159] MODE FOR CARRYING OUT THE INVENTION

[2160] This system uses a generative AI to automatically generate a persona based on user profile information, and then combines it with an emotion engine that recognizes the user's emotions to provide a list of survey questions and analyze and visualize the survey results. A specific embodiment is described below.

[2161] System Configuration

[2162] 1. User enters persona information

[2163] Users log in to the system using devices such as PCs or smartphones.

[2164] Users enter profile information such as age, gender, occupation, nationality, hobbies, and lifestyle on a dedicated persona information input screen.

[2165] For example, enter information such as "Age: 30," "Gender: Female," "Occupation: Marketing Manager," "Nationality: Japanese," "Hobbies: Fitness," and "Lifestyle: Works in an urban area and spends weekends actively," and click the "Submit" button to send the information to the server.

[2166] 2. The server automatically generates personas

[2167] The server receives the profile information sent by the user.

[2168] A generative AI module installed on the server analyzes the input information and generates a detailed persona.

[2169] The generated persona will include the following elements:

[2170] Name (e.g. "Yamada Hanako")

[2171] Hobbies (fitness, cooking)

[2172] Lifestyle (working in the city and being active on the weekends)

[2173] Values ​​(health-conscious, efficiency-oriented)

[2174] Needs (efficient tools and services to balance work and personal life)

[2175] Challenges (busy work schedule and limited leisure time)

[2176] The server stores the generated persona information in a database.

[2177] 3. The server automatically generates a list of survey questions

[2178] The server automatically generates a list of survey questions based on the persona information stored in the database.

[2179] The prompt sent to the generative AI is, "Create effective survey questions relevant to this persona."

[2180] Specific questions include:

[2181] “How did you learn about new health tools and services?”

[2182] "How often do you devote time to fitness?"

[2183] "What applications and devices do you use to stay healthy efficiently?"

[2184] The server stores the automatically generated survey questionnaire in a database.

[2185] 4. The server utilizes the emotion engine

[2186] The server starts the emotion engine and collects user input information and data from interface operations.

[2187] The data collected includes the user's emotional state, such as happiness, sadness, or anger.

[2188] For example, emotional data is analyzed based on facial expressions, tone of voice, click behavior, etc. when a user answers a question.

[2189] 5. The server uses emotion data

[2190] The server analyzes the collected emotional data to further refine the persona profile.

[2191] The emotional data is reflected in the survey questions, and the questions in the survey list are adjusted accordingly. For example, questions that are likely to cause stress to users are softened.

[2192] 6. Users conduct surveys

[2193] The user reviews the list of survey questions provided by the server.

[2194] Users choose an appropriate format to conduct the survey, such as an online survey or an interview.

[2195] Conduct a survey in the format of your choice and set up the system to store each response in a database.

[2196] 7. The device aggregates and analyzes the results

[2197] The survey results are compiled into a database.

[2198] The terminal launches a data analysis module to analyze the aggregated results.

[2199] The analyzed data can be visualized as follows:

[2200] Use graphs, charts, and other visualization techniques.

[2201] Users gain new insights based on the visualized results.

[2202] Prompt Sentence Examples

[2203] "A 30-year-old female marketing manager is looking for a new health tool. Please generate a persona for her."

[2204] "Generate personas based on the user's age (30 years old), gender (female), occupation (marketing manager), and nationality (Japanese), and create a list of relevant survey questions."

[2205] In this way, the system combines a generative AI model with an emotion engine to enable efficient and in-depth user research.

[2206] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2207] System program processing flow

[2208] Step 1:

[2209] User enters persona information

[2210] Input: Users log in to the system using a device such as a PC or smartphone and enter profile information such as age, gender, occupation, nationality, hobbies, and lifestyle on the persona information input screen.

[2211] Data processing: The entered information is temporarily stored in the device's memory and any necessary validation (e.g., checking the input format and required fields) is performed before transmission.

[2212] Output: When the user clicks the "Submit" button, the input information is sent to the server. Examples include "Age: 30," "Gender: Female," "Occupation: Marketing Manager," "Nationality: Japanese," "Hobbies: Fitness," and "Lifestyle: Works in a city and spends weekends actively."

[2213] Step 2:

[2214] The server automatically generates a persona

[2215] Input: The server receives the profile information sent by the user and temporarily stores it in a database.

[2216] Data calculation: The generative AI module installed on the server analyzes the received information. During the analysis, statistical and machine learning models are used to extract user characteristics based on the input data, and a persona is generated.

[2217] Output: The generated persona information is saved in a database. The generated persona includes information such as name (e.g., Hanako Yamada), hobbies (fitness, cooking), lifestyle (works in an urban area, spends weekends actively), values ​​(health-conscious, emphasizes efficiency), needs (efficient tools and services for balancing work and personal life), and challenges (busy work, limited leisure time).

[2218] Step 3:

[2219] The server automatically generates a list of survey questions.

[2220] Input: The server retrieves the persona information stored in the database.

[2221] Data calculation: The server sends a prompt to the generation AI module, saying, "Please create effective survey questions related to this persona." The generation AI analyzes the persona information and generates a corresponding list of survey questions.

[2222] Output: The server stores the automatically generated survey questions in a database. Specific example questions include, "How do you learn about new health tools and services?", "How often do you devote time to fitness?", and "What applications and devices do you use to effectively maintain your health?"

[2223] Step 4:

[2224] The server utilizes the emotion engine

[2225] Input: Collects user input and interface interaction data, such as facial expressions, tone of voice, and click behavior when answering questions.

[2226] Data calculation: The emotion engine analyzes the collected data and detects the user's emotional state (happiness, sadness, anger, etc.).

[2227] Output: The detected emotion data is stored in a database for further analysis and profile refinement.

[2228] Step 5:

[2229] The server uses the emotion data

[2230] Input: Obtain emotional data and persona information stored in the database.

[2231] Data calculation: The persona profile is further refined based on emotional data. For example, questions that are likely to cause stress to the user may be softened, and the content and order of questions may be adjusted according to the user's emotions.

[2232] Output: Updated personas and survey question list saved to database.

[2233] Step 6:

[2234] A user conducts a survey

[2235] Input: Get the latest list of survey questions provided by the server.

[2236] Specific operation: The user selects an appropriate survey format (such as an online questionnaire or interview) and conducts the survey with target users. The survey questions are displayed on the user's device, and the user enters the answers.

[2237] Output: Each answer is saved in a database in real time.

[2238] Step 7:

[2239] The device aggregates and analyzes the results

[2240] Input: Retrieve survey results stored in the database.

[2241] Data calculation: The data analysis module installed on the device aggregates and analyzes the survey results. Analysis methods include statistical analysis and text mining.

[2242] Output: The analysis results are visualized through the visualization module. The results are displayed in graphs, charts, etc. and provided to the user, allowing the user to gain new insights.

[2243] As described above, this system realizes efficient and precise user research through each processing step.

[2244] (Application example 2)

[2245] 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."

[2246] Many modern commercial systems struggle to provide personalized services that accurately reflect customer emotions and behavioral characteristics. This results in low customer satisfaction and a lack of improvement in user experience. Furthermore, conventional systems are unable to accurately analyze survey results by analyzing user emotions and respond to diverse customer needs. A method to resolve these issues and provide more effective and personalized customer service is needed.

[2247] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting desired user profile information, means for automatically generating a persona based on the user profile information, means for automatically generating a survey question list based on the generated persona, means for providing the survey question list to the user, means for the user to conduct the survey, means for further refining the survey results using an emotion engine that analyzes the user's emotions, means for aggregating and analyzing the survey results, and means for visualizing the analysis results and presenting them to the user. This makes it possible to analyze user emotion data and refine the survey results. This makes it possible to provide more personalized and effective customer service and improve customer satisfaction.

[2248] "User profile information" refers to personal information such as a user's age, gender, occupation, hobbies, and lifestyle.

[2249] A "persona" is a virtual image of a user that is generated based on user profile information, and includes the user's name, hobbies, lifestyle, values, needs, challenges, etc.

[2250] "Survey Question List" refers to a list of questions that are automatically generated based on the generated personas.

[2251] An "emotion engine" refers to technology that analyzes a user's facial expressions, tone of voice, text input, etc., to recognize emotional states such as joy, sadness, and anger in real time.

[2252] "Aggregation" refers to the compilation of survey results obtained from users.

[2253] "Analysis" refers to the analysis of aggregated data.

[2254] "Visualization" refers to presenting analytical results in a visual format such as graphs or charts and providing them to users.

[2255] "Interface" refers to the means by which a user inputs information into or receives information from a system.

[2256] "Generative AI" refers to artificial intelligence technology that automatically generates content and information based on given data.

[2257] A "database" refers to a system for storing and managing information in an organized manner.

[2258] "Data Analysis Module" refers to a software component for analyzing collected data.

[2259] "Visualization Module" refers to a software component for visually representing the results of data analysis.

[2260] MODE FOR CARRYING OUT THE INVENTION

[2261] This invention is a system that automatically generates personas based on user profile information, incorporates an emotion engine that recognizes user emotions, provides a survey question list, and analyzes and visualizes survey results. The system of this invention operates based on information entered by the user.

[2262] Program Generation

[2263] The server executes the system program according to the following procedure.

[2264] A natural language description of what the program does

[2265] 1. Enter your user profile information:

[2266] Users log in from a device such as a smartphone or tablet and enter profile information such as their name, age, gender, occupation, hobbies, and lifestyle. This information is accepted through the interface and sent to the server.

[2267] 2. Automatic persona generation:

[2268] Based on the received user profile information, the server uses a generative AI model (e.g., GPT-3) to automatically generate a detailed persona, which includes detailed information about the user's hobbies, lifestyle, values, and needs.

[2269] 3. Automatic generation of survey questions:

[2270] The server automatically generates a list of survey questions based on the persona. For example, if the persona is a "30-year-old woman interested in fitness," a survey list containing fitness-related questions will be generated.

[2271] 4. Leveraging the Emotion Engine:

[2272] During the survey, the server activates an emotion engine to collect real-time emotional data from the user's facial expressions and tone of voice, allowing analysis of the emotional state in which the user provided their answers.

[2273] 5. Elaboration and analysis of findings:

[2274] The server uses the collected emotional data to further refine the generated persona information. It also reflects the emotional data in the questions in the survey questionnaire to make more appropriate adjustments. Once the survey is complete, the user's responses are stored in a database and analyzed by the data analysis module.

[2275] 6. Visualizing the results:

[2276] The analysis results are visualized in the form of graphs and charts through the visualization module and provided to users, allowing them to intuitively understand the survey results.

[2277] Specific examples

[2278] For example, a user enters the following profile information:

[2279] Age: 35

[2280] Gender: Male

[2281] Occupation: Software Engineer

[2282] Hobbies: Reading, games

[2283] Lifestyle: Urban areas, many work from home

[2284] An example of a prompt for a generative AI model is:

[2285] "35-year-old male, software engineer, hobbies include reading and gaming, lifestyle is urban, mostly working from home. Please generate a persona based on this information."

[2286] The persona generated in this way can depict a specific character, such as "an engineer who loves games and places importance on ways to relax while working from home." A list of fitness and relaxation-related survey questions is automatically generated based on this persona, and an emotion engine is used to collect and analyze emotional data in real time, allowing for a personalized experience tailored to the user.

[2287] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2288] Step 1:

[2289] A user logs in from a device such as a smartphone or tablet and enters profile information such as name, age, gender, occupation, hobbies, and lifestyle. The entered information is sent to the server through the interface, allowing the server to receive (input) the user profile information. Specifically, the user enters information into a form and presses the "Submit" button.

[2290] Step 2:

[2291] The server automatically generates a detailed persona using a generative AI model (e.g., GPT-3) based on the received user profile information. At this time, it analyzes the input profile information and provides a prompt to the generative AI model (data processing). For example, if the profile information is "35-year-old male, software engineer, hobbies are reading and gaming," the prompt would be "35-year-old male, software engineer, hobbies are reading and gaming. Please generate a persona based on this information." The generated persona is returned to the device (output).

[2292] Step 3:

[2293] The server automatically generates a list of survey questions based on the generated persona. At this time, it analyzes the information of the generated persona and generates questions that suit the persona's tastes and needs (data calculation). For example, if the persona is a "30-year-old woman who is interested in fitness," a list including fitness-related questions will be generated. The generated list of survey questions is provided to the terminal (output).

[2294] Step 4:

[2295] The user receives a list of survey questions provided by the terminal. The user conducts the survey based on the list and answers the questions. At this time, the user's answers are sent (input) to the server through the interface. Specifically, the user answers the questions in a questionnaire format and presses the "send" button.

[2296] Step 5:

[2297] The server activates the emotion engine and collects emotional data in real time from the user's facial expressions and tone of voice. The server recognizes the user's emotional state based on the user's input information and interface operations, and stores the analysis results in a database (data calculation). For example, the server collects and analyzes the user's facial expressions and tone of voice when answering questions using a camera or microphone.

[2298] Step 6:

[2299] The server uses the collected emotional data to further refine the generated persona information. Furthermore, it reflects the emotional data in the questions in the survey questionnaire to make more appropriate adjustments (data calculation). For example, if the user is feeling stressed, it changes the questions in the questionnaire to reflect that emotion more gently. The refined persona and the adjusted questionnaire are saved in the database (output).

[2300] Step 7:

[2301] Once the survey is complete, the server collects the responses from users and stores them in a database (input). The server then analyzes the responses using a data analysis module, analyzing the aggregated data and performing statistical processing (data calculations). For example, the response data may be aggregated and analyzed for trends and patterns.

[2302] Step 8:

[2303] The server uses a visualization module to visualize the analysis results. At this time, the analysis results are converted into visual formats such as graphs and charts and provided to the user (output). Specifically, the analysis results are converted into diagrams and charts, and dashboards and reports are generated. This allows the user to intuitively understand the investigation results.

[2304] 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.

[2305] 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.

[2306] 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.

[2307] 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.

[2308] 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.

[2309] 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.

[2310] 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).

[2311] 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.

[2312] 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."

[2313] 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.

[2314] 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).

[2315] 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.

[2316] 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.

[2317] 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.

[2318] 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.

[2319] 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.

[2320] 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.

[2321] 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.

[2322] 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.

[2323] 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.

[2324] 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.

[2325] The following is further disclosed regarding the above embodiment.

[2326] (Claim 1)

[2327] means for inputting desired user profile information;

[2328] means for automatically generating a persona based on the user profile information;

[2329] A means to automatically generate a list of survey questions based on the generated personas,

[2330] means for providing said list of survey questions to a user;

[2331] a means for said user to conduct a survey;

[2332] means for aggregating and analyzing said survey results;

[2333] means for visualizing the analysis results and presenting them to a user;

[2334] A system including:

[2335] (Claim 2)

[2336] The system according to claim 1, characterized in that it supports a variety of profile information such as nationality, culture, gender, and age.

[2337] (Claim 3)

[2338] an interface for accepting input from a user;

[2339] An algorithm that automatically generates using generative AI,

[2340] a database for managing the survey questionnaire;

[2341] a data analysis module that stores and analyzes the survey results;

[2342] a visualization module for visualizing the results of the data analysis;

[2343] 2. The system of claim 1, further comprising:

[2344] "Example 1"

[2345] (Claim 1)

[2346] means for inputting desired user profile information;

[2347] means for automatically generating a persona using a generation AI model based on the user profile information;

[2348] A means for automatically generating a list of survey questions based on the generated personas;

[2349] means for providing said list of survey questions to a user;

[2350] means for the user to conduct a survey in the form of an online questionnaire or interview;

[2351] means for aggregating and analyzing said survey results in a data analysis module;

[2352] means for visualizing the data analysis results using a visualization module and presenting the results to a user;

[2353] A system including:

[2354] (Claim 2)

[2355] The system according to claim 1, characterized in that it supports a variety of profile information such as nationality, culture, gender, and age.

[2356] (Claim 3)

[2357] an interface for accepting input from a user;

[2358] An algorithm that automatically generates personas using a generative AI model,

[2359] An algorithm that generates a list of survey questions based on the persona's needs and challenges.

[2360] a database for managing the survey questionnaire;

[2361] a means to store the survey results and analyze them in the data analysis module;

[2362] a visualization module for visualizing the results of the data analysis;

[2363] 2. The system of claim 1, further comprising:

[2364] "Application Example 1"

[2365] (Claim 1)

[2366] means for inputting desired user profile information;

[2367] means for automatically generating a persona based on the user profile information;

[2368] A means to automatically generate a list of survey questions based on the generated personas,

[2369] means for providing said list of survey questions to a user;

[2370] a means for said user to conduct a survey;

[2371] means for aggregating and analyzing said survey results;

[2372] means for visualizing the analysis results and presenting them to a user;

[2373] Based on the generated personas and survey question list, we will optimize the content and format of advertising.

[2374] A system including:

[2375] (Claim 2)

[2376] The system according to claim 1, characterized in that it supports a variety of profile information such as nationality, culture, gender, and age.

[2377] (Claim 3)

[2378] an interface for accepting input from a user;

[2379] An algorithm that automatically generates using generative AI,

[2380] a database for managing the survey questionnaire;

[2381] a data analysis module that stores and analyzes the survey results;

[2382] a visualization module for visualizing the results of the data analysis;

[2383] Algorithms, including generative AI models for ad optimization;

[2384] 2. The system of claim 1, further comprising:

[2385] "Example 2: Combining Emotion Engines"

[2386] (Claim 1)

[2387] means for inputting desired user profile information;

[2388] means for automatically generating a persona based on the user profile information;

[2389] A means to automatically generate a list of survey questions based on the generated personas,

[2390] means for providing said list of survey questions to a user;

[2391] A means for collecting emotion data through user input information and operation data;

[2392] means for analyzing the emotion data and refining the generated persona profile;

[2393] a means for said user to conduct a survey;

[2394] means for aggregating and analyzing said survey results;

[2395] means for visualizing the analysis results and presenting them to a user;

[2396] A system including:

[2397] (Claim 2)

[2398] The system according to claim 1, characterized in that it supports a variety of profile information such as nationality, culture, gender, and age.

[2399] (Claim 3)

[2400] an interface for accepting input from a user;

[2401] An algorithm that automatically generates using generative AI,

[2402] An engine that collects and analyzes emotion data,

[2403] a database for managing the survey questionnaire;

[2404] a data analysis module that stores and analyzes the survey results;

[2405] a visualization module for visualizing the results of the data analysis;

[2406] 2. The system of claim 1, further comprising:

[2407] "Application example 2 when combining emotion engines"

[2408] (Claim 1)

[2409] means for inputting desired user profile information;

[2410] means for automatically generating a persona based on the user profile information;

[2411] A means to automatically generate a list of survey questions based on the generated personas,

[2412] means for providing said list of survey questions to a user;

[2413] a means for said user to conduct a survey;

[2414] a means for further refining the survey results by utilizing an emotion engine that analyzes user emotions;

[2415] means for aggregating and analyzing said survey results;

[2416] means for visualizing the analysis results and presenting them to a user;

[2417] A system including:

[2418] (Claim 2)

[2419] The system according to claim 1, characterized in that it supports a variety of profile information such as nationality, culture, gender, and age, and performs analysis based on emotional data.

[2420] (Claim 3)

[2421] an interface for accepting input from a user;

[2422] An algorithm that automatically generates using generative AI,

[2423] A module that uses an emotion engine for analysis,

[2424] a database for managing the survey questionnaire;

[2425] a data analysis module that stores and analyzes the survey results;

[2426] a visualization module for visualizing the results of the data analysis;

[2427] 2. The system of claim 1, further comprising: [Explanation of symbols]

[2428] 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. means for inputting desired user profile information; means for automatically generating a persona based on the user profile information; A means to automatically generate a list of survey questions based on the generated personas, means for providing said list of survey questions to a user; a means for said user to conduct a survey; means for aggregating and analyzing said survey results; means for visualizing the analysis results and presenting them to a user; A system including:

2. 2. The system according to claim 1, wherein the system supports a variety of profile information such as nationality, culture, gender, and age.

3. an interface for accepting input from a user; An algorithm that automatically generates using generative AI, a database for managing the survey questionnaire; a data analysis module that stores and analyzes the survey results; a visualization module for visualizing the results of the data analysis; The system of claim 1, comprising:

Images