system

Abstract

We provide the system. [Solution] The information processing device includes means for obtaining attribute information from a registered user database and generating a virtual person based on said attribute information, A means of receiving questions directed at a generated virtual character and simulating the answers, A means of providing an interface for evaluating simulated answers and adjusting inappropriate questions, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] The main reasons for the decline in the accuracy of responses in market research are the lack of understanding of respondents and inappropriate questionnaire design. In conventional market research methods, problems such as respondents not fully understanding the questions or large variations in samples often occur. As a result, the survey results may lack reliability and may not be useful for decision-making, so means to solve such problems are required.

Means for Solving the Problems

[0005] This invention provides a system that uses an information processing device to acquire attribute information from a registered user database and generate a virtual persona based on that information. The generated virtual persona is given hypothetical questions using AI technology and simulates virtual answers. The system then includes means to improve the accuracy of the survey design by identifying ambiguities in the questions during the process of evaluating the answers and adjusting the question wording through an interface. This means makes it possible to improve the appropriateness of the questions and the quality of the answers, thereby realizing more reliable market research.

[0006] An "information processing device" is an electronic device that collects, analyzes, stores, and processes data. It has the function of acquiring information from a registered user database and generating virtual individuals and simulating questions.

[0007] The "registrant database" is a data storage system that stores attribute information and historical data of registered users, and serves as the fundamental source of information for generating virtual individuals.

[0008] "Attribute information" refers to basic personal information such as the user's gender, annual income, and areas of interest, and is data that is considered when generating a virtual character.

[0009] A "virtual character" is a fictional persona generated based on attribute information obtained from the registrant database, and is used as a persona for simulating questions.

[0010] "Question simulation" is a process that uses AI technology to generate hypothetical answers to questions input to a virtual character, and predicts how the actual answers will be given.

[0011] "AI technology" refers to technologies that use data analysis and machine learning to make decisions and predictions based on input information, and is used in the simulation of responses in this system.

[0012] An "interface" is a point of contact for a user to interact with a system, and is a means of user interaction that allows for adjustments to question wording and confirmation of evaluation results. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the 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.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0027] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention is a system designed to improve the accuracy of market research, and this system is implemented by a program that runs within an information processing device. Specifically, the invention can be carried out in the following forms.

[0035] System-wide configuration

[0036] The system is primarily composed of a server, user terminals, and communication interfaces between them. The server contains a registrant database and information processing devices, which generate and simulate virtual characters. User terminals exchange data with the server through the interface, inputting questions, confirming answers, and making adjustments.

[0037] Server operation

[0038] The server first retrieves user attribute information from the registrant database. Based on this information, it uses an AI model to analyze user trends and generate a virtual persona. This process takes into account the user's profile and past response history to create a persona aligned with the expected market segment.

[0039] Next, the server simulates the survey questions received from the user's device for a virtual persona. Using AI technology, it generates virtual answers and sends the results to the user's device. It then evaluates whether these answers match the reactions expected by the persona.

[0040] User terminal behavior

[0041] The user's device sends a question to the server through the interface, and then receives and displays the answer from the server. The user can review the simulated answer and, based on the results, adjust the question wording or modify the virtual character's settings.

[0042] Specific example

[0043] For example, if a company wants to investigate market reaction to a new insurance product, the server generates a profile of a potential customer from an existing customer database. When a user inputs a question from their terminal, such as "How likely are you to subscribe to this insurance product?", the server generates and presents a hypothetical response. Based on this response, the user can refine the question, adjust the direction of the investigation, and prepare for the actual market research.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server retrieves user attribute information from the registrant database. This includes data such as the user's gender, age, annual income, and areas of interest. The server then prepares an initial dataset based on this information.

[0047] Step 2:

[0048] The server uses an AI model to analyze the acquired attribute information. Taking into account past response history and other factors, it determines the characteristics of the persona and generates a virtual character. Each attribute of the generated virtual character is adjusted to fit the expected market segment.

[0049] Step 3:

[0050] The user's terminal sends a survey question to the server. The user enters the question text through the interface and sends the request to the server by pressing the submit button.

[0051] Step 4:

[0052] The server simulates the questions received from the user against generated virtual characters. Using AI technology, it virtually calculates how each virtual character would answer the questions.

[0053] Step 5:

[0054] The server presents a virtually generated response to the user's device. The user reviews the presented response and evaluates whether the virtual character's reaction matches their expectations.

[0055] Step 6:

[0056] The questions are reviewed based on the answers provided by the user. Users can adjust or modify questions that seem unclear or ineffective. They can also modify the characteristics of the virtual character as needed.

[0057] Step 7:

[0058] Finally, the question set that the user has finished adjusting is sent back to the server. The server re-evaluates the adjusted questions and accumulates data to help design the optimal survey.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] Traditional market research methods have struggled to generate highly accurate virtual individuals based on consumer attribute information and to quickly simulate their responses to survey questions. This has led to problems with the reliability of survey results and the consistency of responses. Furthermore, adjusting inappropriate questions often relies on manual methods, which is time-consuming and laborious.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes means for an information processing device to acquire attribute information from a storage device and generate a virtual person based on said attribute information, means for receiving survey items for the generated virtual person and simulating answers using AI technology, and means for providing an input device for evaluating the simulated answers and adjusting inappropriate survey items. This enables users to conduct highly accurate market research quickly and efficiently, and to flexibly adjust survey items and virtual person settings.

[0064] An "information processing device" is a mechanical or electronic device that takes data as input, performs specific processing on it, and then outputs the result.

[0065] A "storage device" is a device that stores digital data and supplies that data as needed.

[0066] "Attribute information" refers to information that indicates the characteristics and traits associated with a specific individual or object.

[0067] A "virtual character" is a fictional character created on a computer for simulation purposes, modeled after a real person and based on specific attribute information.

[0068] "Survey items" are elements such as questions and tasks set up for a specific purpose, used to provide or elicit information from the survey subjects.

[0069] "AI technology" refers to a set of technologies that enable intelligent behavior by machines, based on the theory and implementation of artificial intelligence.

[0070] A "simulated response" is a virtual reaction or reply generated using AI technology based on questions directed at a virtual character.

[0071] An "input device" is a physical or electronic device used to transmit data or instructions to an information processing device.

[0072] A "display device" is a device used to visually display information in digital or analog format.

[0073] This invention is a system that enables efficient market research and is built around an information processing device. The invention is implemented using a server, terminals, and a communication interface connecting them. The server is equipped with a storage device for holding attribute information and a generative AI model for performing data processing using AI. This AI model is used to generate virtual characters and simulate their reactions.

[0074] Specifically, the server retrieves user attribute information from storage and generates a virtual person based on that information using a generative AI model. After generating the virtual person, it receives survey items from the user terminal and simulates the answers using AI technology. The server evaluates the results and provides a system that allows for adjustment of the survey items via input and display devices for providing the results to the user.

[0075] For example, if a company wants to conduct market research for a new product, the server designs a virtual person with specific attributes based on existing customer records. The user inputs specific research questions via a terminal, such as, "How likely are you to be interested in this product?" Based on this prompt, the server uses a generative AI model to simulate the virtual person's responses. This result is sent to the user, who can then refine the research questions and the virtual person's settings.

[0076] An example of a prompt might be, "Simulate how men in their 30s would react to a new product category in terms of purchasing intent." Using such specific prompts makes it possible to conduct more precise and targeted market research.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The server retrieves user attribute information from its storage device. It receives the user's ID as input and searches the database for attribute information such as age, gender, occupation, and past response history based on that ID. This information serves as the basic data for generating a virtual character.

[0080] Step 2:

[0081] The server uses the acquired attribute information to run a generative AI model and generate a virtual person. By providing attribute information as input to the generative AI model, it outputs a virtual person based on the relevant market segment. Specifically, it forms a person profile that reflects the user's interests and behavioral patterns.

[0082] Step 3:

[0083] The user's terminal sends survey items to the server. The user enters prompt text for the survey items from the terminal, and this data is sent to the server. These survey items are the information necessary for simulating a virtual character.

[0084] Step 4:

[0085] The server performs simulations with a virtual person based on the received survey items. Using AI technology with the survey items as input, it generates responses from the virtual person. The output is the virtual person's reactions and responses. Here, response generation takes into account past reactions and trends.

[0086] Step 5:

[0087] The server evaluates the simulated answers and sends the results to the user's terminal. It receives the generated answers as input and determines their usefulness and validity based on evaluation criteria. The evaluation results are provided to the user as output.

[0088] Step 6:

[0089] The user's device receives the transmitted simulation results and displays them, allowing the user to confirm the results. Based on this display, the user can adjust the investigation items and virtual character settings. Specifically, the user can input the adjustments and prepare the next investigation accordingly.

[0090] (Application Example 1)

[0091] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0092] When conducting advertising campaigns in the market, it is extremely difficult to evaluate in advance how to effectively approach the target user group. Traditional methods make it difficult to accurately predict advertising content before obtaining actual consumer responses, potentially leading to wasted time and resources. There is a need for methods to improve this situation and develop advertising strategies more efficiently.

[0093] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0094] In this invention, the server includes means for an information processing device to acquire attribute information from a registered user database and generate a virtual person based on said attribute information; means for receiving advertising-related data for the generated virtual person and simulating virtual user responses; means for evaluating virtual user responses based on the virtual person and providing an interface for adjusting advertising content; and means for a marketing person to input advertising creatives and simulate responses from the target user group. This makes it possible to predict the responses of the target user group and build an efficient advertising strategy before implementing an advertising campaign.

[0095] An "information processing device" is a device that retrieves attribute information from a database, generates virtual characters, and processes advertising-related data.

[0096] A "registrant database" is a structured collection of information used to store attribute information about users.

[0097] "Attribute information" refers to information about a user's characteristics and behavior, and is data used to create virtual characters.

[0098] A "virtual character" is a fictional user model generated based on attribute information obtained from a registered user database, used to predict advertising effectiveness.

[0099] "Advertising-related data" refers to data entered by marketing personnel in relation to advertising campaigns, and is necessary information for simulating responses to virtual characters.

[0100] "Simulation" is the process of reproducing and analyzing expected reactions and results using a virtual model.

[0101] "User response" refers to data that shows the responses and actions of a virtual character to an advertising campaign.

[0102] An "interface" is a means of providing a user interface or user control elements for communication between the user and the system.

[0103] A "marketing professional" is a specialist responsible for planning, executing, and evaluating advertising campaigns.

[0104] "Advertising creative" refers to creative works, including visuals and messages, used in advertising campaigns.

[0105] The "target user group" refers to the consumer group that is primarily targeted by an advertising campaign.

[0106] To realize this invention, a system is implemented that allows for smooth data exchange between the server and the user's terminal. The server functions as an information processing device and retrieves user attribute information from the registrant database. Based on this attribute information, an AI model is used to generate a virtual person and receives advertising-related data for that virtual person. The generated virtual person is then used to simulate a virtual user response using advertising campaign data.

[0107] The user's device can input advertising creatives through the interface and view simulated user responses. Marketing personnel can then adjust and optimize the advertising content based on these responses. Specifically, users connect to a cloud-based server using a smartphone or tablet and operate through prompt messages.

[0108] The main hardware used includes database servers and cloud servers, while the software includes, for example, Flask implemented in Python and generative AI model APIs (such as OpenAI®'s GPT and Google®'s BERT).

[0109] As a concrete example, consider a food manufacturer testing the advertising effectiveness of a new beverage. The marketing team can simulate "how the advertising message for the new product will resonate with busy business people." An example of a prompt here would be: "New product name: Morning Energy Drink, Advertisement content: Refresh with 100% natural ingredients for an energetic morning, Target: Business people in their 20s and 30s." This allows them to prepare in advance for the product's potential market launch.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The server retrieves user attribute information from the registrant database. It receives a user ID as input, searches the database for attribute information associated with that ID, and retrieves the attribute information as output. Based on this attribute information, it prepares to generate a virtual character.

[0113] Step 2:

[0114] The server uses attribute information as input to generate a virtual character using a generative AI model. The AI ​​model analyzes the attribute information and outputs a virtual character that mimics consumer tendencies and characteristics. This virtual character is then used in subsequent simulation processes.

[0115] Step 3:

[0116] The user inputs the ad creative as a prompt message from their device. This input data includes information about the ad content and target audience. This prompt message is used to prepare a request for ad simulation from the server.

[0117] Step 4:

[0118] The server simulates a virtual user response to an advertisement based on the generated virtual character and the prompt text entered by the user. It takes the virtual character and prompt text as input and the virtual user response as output. AI technology is used to calculate how the advertisement will affect the virtual character.

[0119] Step 5:

[0120] The user's device receives simulated user responses sent from the server and displays the evaluation results as output. Based on these evaluation results, the user can adjust the ad content. Specifically, the evaluation results are visualized and presented in a way that is easy for the user to understand.

[0121] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0122] This invention is a system that combines an information processing device and an emotion engine, and aims to obtain more accurate and insightful results in market research. Specific embodiments of this system are described below.

[0123] System Configuration

[0124] The system primarily consists of a server, user terminals, and an emotion engine. The server houses a registrant database and information processing device, and is responsible for generating virtual characters and simulating questions. The user terminal includes an interface, allowing users to input questions and view answers and emotion information. The emotion engine recognizes the user's emotional responses and provides emotion data to the entire system.

[0125] Server operation

[0126] The server first retrieves attribute information from the registrant database and uses this information to generate a virtual persona using an AI model. At this stage, emotional data obtained from the emotion engine is combined with the attribute information to construct a more realistic persona.

[0127] Next, the server simulates an answer to a question received from the user's terminal. Using AI technology, it calculates how a virtual character would answer the question, taking emotional data into consideration. This process ensures that the answer reflects emotional nuances.

[0128] User terminal behavior

[0129] The user's device sends questions to the server via an interface and receives answers and sentiment-based analysis results. The user can review these results and re-evaluate and adjust the question wording. Based on the analysis results provided by the sentiment engine, more appropriate question design becomes possible.

[0130] How the emotion engine works

[0131] The emotion engine analyzes user input and responses to extract emotional data. This data is sent to a server and used to generate virtual characters and simulate questions.

[0132] Specific example

[0133] For example, when investigating market reactions to a new product, the server uses existing customer data to create a virtual person who is likely to be highly interested. When the user inputs the question, "How interested are you in the new product?" from their terminal, the emotion engine analyzes the user's potential emotional responses, and the server generates a response that takes these into account. This allows the user to obtain research results that consider emotional aspects, enabling them to consider a more appropriate market approach.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] The server retrieves user attribute information from the registrant database. This includes information such as the user's age, occupation, and hobbies, and is used as basic data to generate a virtual character.

[0137] Step 2:

[0138] The server retrieves emotional data provided by the emotion engine and combines it with attribute information. The emotional data reflects the emotional tendencies and preferences the user has shown in the past and is incorporated into the personality of the virtual character.

[0139] Step 3:

[0140] The server utilizes an AI model to generate a virtual persona based on acquired attribute information and emotional data. This virtual persona is designed to represent a specific market segment and is embodied as a persona with emotional elements.

[0141] Step 4:

[0142] The user sends questions related to a specific market research project from their device to the server. The user inputs the questions through an interface and then proceeds to ask them to a virtual person being researched.

[0143] Step 5:

[0144] The server simulates the received question for a virtual character. Using AI technology, it virtually generates how the virtual character would answer the question. This answer generation also incorporates emotional data, taking into account the user's emotional nuances.

[0145] Step 6:

[0146] The server sends a virtually generated answer to the user's terminal. The user reviews the answer and evaluates whether the question is appropriate.

[0147] Step 7:

[0148] If the user's response indicates that the question needs to be modified, the question will be revised. The results of sentiment data analysis by the sentiment engine can be used to create a more appropriate question structure.

[0149] Step 8:

[0150] The server resimulates the questions, modified as needed, and collects and analyzes the results. This enables the implementation of more appropriate and higher-quality market research.

[0151] (Example 2)

[0152] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0153] Modern market research demands a more detailed understanding of consumer emotions and reactions, but current methods struggle to adequately capture these emotional aspects. Furthermore, the generation of virtual individuals based on traditional registration information often yields unrealistic results. A new methodology is needed to address these problems and obtain more accurate and insightful market research findings.

[0154] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0155] In this invention, the server includes means for an information processing device to acquire attribute information and sentiment data from a registered user database and generate a virtual person based on said attribute information and sentiment data; means for receiving questions directed to the generated virtual person and simulating responses that take sentiment data into account using a generation AI model; means for evaluating the simulated responses and providing an interface for adjusting inappropriate questions; and means for extracting sentiment data from user input data and responses using a sentiment analysis module. This makes it possible to conduct market research using a realistic virtual person that takes emotional aspects into account and their responses.

[0156] An "information processing device" is a device that retrieves attribute information from a database and generates a virtual person based on that information.

[0157] "Attribute information" refers to information that indicates specific characteristics or profiles of registered users.

[0158] A "virtual character" is an imaginary person generated based on acquired attribute information and emotional data.

[0159] "Emotional data" refers to data that indicates a user's emotional state, obtained by analyzing their input and responses.

[0160] A "generative AI model" is a model that uses artificial intelligence to analyze data based on a specific purpose and generate results.

[0161] An "interface" is a function that provides a window or screen for interaction between the user and the system.

[0162] A "sentiment analysis module" is a part of a system that has the function of analyzing user input and responses to extract emotional data.

[0163] This invention is a market research support system that combines an information processing device and an emotion analysis module. The server retrieves user attribute information from a registered user database and generates a virtual persona using an AI model based on that information. At this time, emotion data extracted by the emotion analysis module is combined to construct a more realistic persona.

[0164] The server receives questions entered via an interface from the user's terminal. It then uses AI technology to simulate how a generated virtual character would respond. During this process, emotional data is considered, and emotional nuances are added to the answers, allowing the user to gain deeper insights.

[0165] The sentiment analysis module analyzes user input and responses, extracting sentiment data. The user's device can review the responses and analysis results sent from the server, allowing for re-evaluation and adjustment of questions, thus supporting the development of optimal market strategies. Generative AI models play a central role in this entire process, contributing to complex data analysis and response generation.

[0166] For example, if you want to check the market reaction to a new health food product, the server creates a health-conscious virtual person based on past consumption data. Then, the user inputs a question from their terminal: "How interested are you in this new health food product?" The sentiment analysis module analyzes sentiment data based on this question, and the server, taking that data into consideration, generates an emotional response from the virtual person.

[0167] An example of a prompt might be, "Simulate the emotional response of health-conscious consumers to a new food product." This allows users to obtain research results that reflect emotional aspects and select a more appropriate market approach.

[0168] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0169] Step 1:

[0170] The server retrieves user attribute information from the registrant database. Based on this attribute information as input, the server generates a virtual persona using a generative AI model. Here, emotional data provided by the emotion analysis module is also retrieved as input and added to the virtual persona's attributes to create a more realistic persona. The output of this step is a virtual persona incorporating the emotional data.

[0171] Step 2:

[0172] The user's device inputs a question to the server through an interface. This question becomes the input, and the server receives it. Based on the received question, the server uses a generative AI model to simulate a response from a virtual character. Here, it generates an answer to the input question that takes sentiment data into account. The output of this step is a simulated answer that includes emotional nuances.

[0173] Step 3:

[0174] The emotion analysis module analyzes user input data and extracts emotion data. Based on this input, it generates and outputs data indicating the user's emotional response. This outputted emotion data is used in steps 1 and 2 described above.

[0175] Step 4:

[0176] The server generates the final research findings based on simulated responses and user feedback. During this process, if a question is deemed inappropriate, the user's terminal is notified and an interface is provided allowing for question adjustment. The output of this step is the final market research findings and a set of adjustable questions.

[0177] Step 5:

[0178] Users review the results provided through their devices and formulate market approaches, adjusting questions as needed. The input for this step is the final research findings received from the server, which the user uses to develop concrete action plans. Through this process, users can build strategies that leverage sentiment-aware insights.

[0179] (Application Example 2)

[0180] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0181] In modern market research, accurately understanding consumer emotions and needs is a challenge. However, traditional methods struggle to fully grasp the emotional aspects of users, limiting their ability to understand true consumer needs. Furthermore, there is a lack of mechanisms to utilize emotional data to present consumers with more appropriate products in specific product recommendation scenarios.

[0182] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0183] In this invention, the server includes an information processing device that includes means for acquiring attribute information from a registered user database and generating a virtual person based on said attribute information, means for receiving questions directed at the generated virtual person and simulating answers, and means for analyzing the user's emotional response and making optimized product recommendations based on that. This enables market research and product recommendations that take into account the emotional aspects of consumers.

[0184] An "information processing device" is a general term for hardware or software used to acquire data from users and process and analyze various types of information based on that data.

[0185] A "registrant database" is a data structure or management system that stores user and target attribute information.

[0186] "Attribute information" refers to data that indicates characteristics and status associated with a specific subject or user.

[0187] A "virtual character" is a fictional person created using digital data or AI technology, and is the subject of simulation by users or systems.

[0188] "Emotional response" refers to the emotional reaction or feedback that a user shows to a particular object or situation.

[0189] "Optimized product recommendations" is a process that takes into account the user's needs and emotions, and provides individually tailored product suggestions.

[0190] An "interface" refers to the means of communication and display screens that allow a user to interact directly with a system.

[0191] A specific embodiment of this invention will now be described. The system mainly consists of a server, a user terminal, and an emotion engine. The server is an information processing device with big data processing capabilities, which retrieves attribute information from a registered user database and generates a virtual person using AI technology. Questions directed to the generated virtual person are sent from the user's terminal, which is equipped with an ergonomically designed user interface.

[0192] The user's device first acts as a facilitator, sending questions to the server for the generated virtual character. Upon receiving the questions, the server uses an AI model to simulate the virtual character's emotional responses. In this process, the emotion engine analyzes the user's emotional reactions and provides this data to the entire system. This enables the server to generate highly accurate answers and product recommendations that take emotional data into account.

[0193] For example, in an e-commerce application, when a user browses products through smart glasses, emotional data is analyzed in real time from their gaze and facial expressions. The emotion engine sends this data to a server, which then prioritizes recommending products that match the user's preferences. This process utilizes image processing libraries such as OpenCV and emotion analysis APIs to grasp the user's emotions in real time and provide appropriate product suggestions.

[0194] Specific examples of prompt messages include: "Detect products that make the user smile and recommend similar products from that product category. What other combinations of emotional responses and product categories should be considered?" This allows for proactively suggesting products that evoke joy and interest in the user, ultimately providing a more attractive purchasing experience for consumers.

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The user's device sends questions created by the user to the server via the user interface. The input is the user's question content, and the output is transferred to the server. On the interface, data is sent to the server when the send button is clicked.

[0198] Step 2:

[0199] The server retrieves attribute information from the registrant database and uses this data to generate a virtual character using a generative AI model. The input is attribute information from the database, and the output is digital data of the virtual character. Here, an AI-built algorithm operates to generate a realistic and appealing virtual character.

[0200] Step 3:

[0201] The emotion engine analyzes emotional data, such as facial expressions and gaze, transmitted in real time from the user's device. Input is video data from a visual sensor, and the extracted emotional parameters are sent to the server as output. An image processing library is used to quantify the user's emotions.

[0202] Step 4:

[0203] The server simulates generated questions for a virtual character and produces answers based on sentiment data. The input consists of the user's question and sentiment data, and the output is a response that reflects those sentiments. AI models are also utilized here to provide emotionally resonant answers.

[0204] Step 5:

[0205] The server recommends the most suitable products based on the user's emotional data and simulation results. Inputs include emotional data, answers to questions, and past purchase history data, and the output is an optimized product list. Based on this information, the server presents products that reflect the user's tastes and preferences.

[0206] Step 6:

[0207] The user's device receives an optimized product list sent from the server and displays it on the user interface. The input is the product list sent from the server, and the output is the display of those products on the device screen. Specific actions include screen refresh and information updates.

[0208] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0209] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0210] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0211] [Second Embodiment]

[0212] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0213] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0214] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0215] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0216] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0217] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0218] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0219] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0220] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0221] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0222] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0223] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0224] This invention is a system designed to improve the accuracy of market research, and this system is implemented by a program that runs within an information processing device. Specifically, the invention can be carried out in the following forms.

[0225] System-wide configuration

[0226] The system is primarily composed of a server, user terminals, and communication interfaces between them. The server contains a registrant database and information processing devices, which generate and simulate virtual characters. User terminals exchange data with the server through the interface, inputting questions, confirming answers, and making adjustments.

[0227] Server operation

[0228] The server first retrieves user attribute information from the registrant database. Based on this information, it uses an AI model to analyze user trends and generate a virtual persona. This process takes into account the user's profile and past response history to create a persona aligned with the expected market segment.

[0229] Next, the server simulates the survey questions received from the user's device for a virtual persona. Using AI technology, it generates virtual answers and sends the results to the user's device. It then evaluates whether these answers match the reactions expected by the persona.

[0230] User terminal behavior

[0231] The user's device sends a question to the server through the interface, and then receives and displays the answer from the server. The user can review the simulated answer and, based on the results, adjust the question wording or modify the virtual character's settings.

[0232] Specific example

[0233] For example, if a company wants to investigate market reaction to a new insurance product, the server generates a profile of a potential customer from an existing customer database. When a user inputs a question from their terminal, such as "How likely are you to subscribe to this insurance product?", the server generates and presents a hypothetical response. Based on this response, the user can refine the question, adjust the direction of the investigation, and prepare for the actual market research.

[0234] The following describes the processing flow.

[0235] Step 1:

[0236] The server retrieves user attribute information from the registrant database. This includes data such as the user's gender, age, annual income, and areas of interest. The server then prepares an initial dataset based on this information.

[0237] Step 2:

[0238] The server uses an AI model to analyze the acquired attribute information. Taking into account past response history and other factors, it determines the characteristics of the persona and generates a virtual character. Each attribute of the generated virtual character is adjusted to fit the expected market segment.

[0239] Step 3:

[0240] The user's terminal sends a survey question to the server. The user enters the question text through the interface and sends the request to the server by pressing the submit button.

[0241] Step 4:

[0242] The server simulates the questions received from the user against generated virtual characters. Using AI technology, it virtually calculates how each virtual character would answer the questions.

[0243] Step 5:

[0244] The server presents a virtually generated response to the user's device. The user reviews the presented response and evaluates whether the virtual character's reaction matches their expectations.

[0245] Step 6:

[0246] The questions are reviewed based on the answers provided by the user. Users can adjust or modify questions that seem unclear or ineffective. They can also modify the characteristics of the virtual character as needed.

[0247] Step 7:

[0248] Finally, the question set that the user has finished adjusting is sent back to the server. The server re-evaluates the adjusted questions and accumulates data to help design the optimal survey.

[0249] (Example 1)

[0250] Next, we will describe Example 1. 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."

[0251] Traditional market research methods have struggled to generate highly accurate virtual individuals based on consumer attribute information and to quickly simulate their responses to survey questions. This has led to problems with the reliability of survey results and the consistency of responses. Furthermore, adjusting inappropriate questions often relies on manual methods, which is time-consuming and laborious.

[0252] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0253] In this invention, the server includes means for an information processing device to acquire attribute information from a storage device and generate a virtual person based on said attribute information, means for receiving survey items for the generated virtual person and simulating answers using AI technology, and means for providing an input device for evaluating the simulated answers and adjusting inappropriate survey items. This enables users to conduct highly accurate market research quickly and efficiently, and to flexibly adjust survey items and virtual person settings.

[0254] An "information processing device" is a mechanical or electronic device that takes data as input, performs specific processing on it, and then outputs the result.

[0255] A "storage device" is a device that stores digital data and supplies that data as needed.

[0256] "Attribute information" refers to information that indicates the characteristics and traits associated with a specific individual or object.

[0257] A "virtual character" is a fictional character created on a computer for simulation purposes, modeled after a real person and based on specific attribute information.

[0258] "Survey items" are elements such as questions and tasks set up for a specific purpose, used to provide or elicit information from the survey subjects.

[0259] "AI technology" refers to a set of technologies that enable intelligent behavior by machines, based on the theory and implementation of artificial intelligence.

[0260] A "simulated response" is a virtual reaction or reply generated using AI technology based on questions directed at a virtual character.

[0261] An "input device" is a physical or electronic device used to transmit data or instructions to an information processing device.

[0262] A "display device" is a device used to visually display information in digital or analog format.

[0263] This invention is a system that enables efficient market research and is built around an information processing device. The invention is implemented using a server, terminals, and a communication interface connecting them. The server is equipped with a storage device for holding attribute information and a generative AI model for performing data processing using AI. This AI model is used to generate virtual characters and simulate their reactions.

[0264] Specifically, the server retrieves user attribute information from storage and generates a virtual person based on that information using a generative AI model. After generating the virtual person, it receives survey items from the user terminal and simulates the answers using AI technology. The server evaluates the results and provides a system that allows for adjustment of the survey items via input and display devices for providing the results to the user.

[0265] For example, if a company wants to conduct market research for a new product, the server designs a virtual person with specific attributes based on existing customer records. The user inputs specific research questions via a terminal, such as, "How likely are you to be interested in this product?" Based on this prompt, the server uses a generative AI model to simulate the virtual person's responses. This result is sent to the user, who can then refine the research questions and the virtual person's settings.

[0266] An example of a prompt might be, "Simulate how men in their 30s would react to a new product category in terms of purchasing intent." Using such specific prompts makes it possible to conduct more precise and targeted market research.

[0267] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0268] Step 1:

[0269] The server retrieves user attribute information from its storage device. It receives the user's ID as input and searches the database for attribute information such as age, gender, occupation, and past response history based on that ID. This information serves as the basic data for generating a virtual character.

[0270] Step 2:

[0271] The server uses the acquired attribute information to run a generative AI model and generate a virtual person. By providing attribute information as input to the generative AI model, it outputs a virtual person based on the relevant market segment. Specifically, it forms a person profile that reflects the user's interests and behavioral patterns.

[0272] Step 3:

[0273] The user's terminal sends survey items to the server. The user enters prompt text for the survey items from the terminal, and this data is sent to the server. These survey items are the information necessary for simulating a virtual character.

[0274] Step 4:

[0275] The server performs simulations with a virtual person based on the received survey items. Using AI technology with the survey items as input, it generates responses from the virtual person. The output is the virtual person's reactions and responses. Here, response generation takes into account past reactions and trends.

[0276] Step 5:

[0277] The server evaluates the simulated answers and sends the results to the user's terminal. It receives the generated answers as input and determines their usefulness and validity based on evaluation criteria. The evaluation results are provided to the user as output.

[0278] Step 6:

[0279] The user's terminal receives the transmitted simulation results and displays them, enabling the user to confirm the results. Based on this display, the user can adjust the survey items and virtual character settings. As a specific operation, the user can input the adjustment content and accordingly prepare for the next survey.

[0280] (Application Example 1)

[0281] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0282] When conducting an advertising campaign in the market, it is very difficult to pre-evaluate an effective approach to the target user group. With conventional methods, it is difficult to accurately predict the advertising content before obtaining the reaction of actual consumers, which may lead to a waste of time and resources. There is a need for means to improve such a situation and formulate a more efficient advertising strategy.

[0283] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0284] In this invention, the server includes means for an information processing device to obtain attribute information from a registrant database and generate a virtual character based on the attribute information, means for receiving advertising-related data for the generated virtual character and simulating a virtual user reaction, means for providing an interface for evaluating the virtual user reaction based on the virtual character and adjusting the advertising content, and means for a marketing staff to input an advertising creative and simulate the reaction to the target user group. Thereby, before conducting an advertising campaign, it becomes possible to predict the reaction of the target user group and construct an efficient advertising strategy.

[0285] The "information processing device" is a device that obtains attribute information from a database and generates a virtual character and processes advertising-related data.

[0286] The "registered user database" is a structured information set for accumulating attribute information about users.

[0287] "Attribute information" is information about the characteristics and behaviors of users, and is data used for generating virtual characters.

[0288] A "virtual character" is a fictional user model generated based on attribute information obtained from the registered user database and used for predicting advertising effects.

[0289] "Advertising-related data" is data input by marketing personnel in relation to advertising campaigns, and is information necessary for simulating the reactions of virtual characters.

[0290] "Simulate" is a process of reproducing and analyzing reactions and results predicted using a virtual model.

[0291] "User reaction" is data indicating the responses and behaviors of virtual characters to advertising campaigns.

[0292] An "interface" is a means of providing an operation screen and operation elements for the user and the system to communicate.

[0293] A "marketing personnel" is a professional who plans, executes, and evaluates advertising campaigns.

[0294] An "advertising creative" is a creative work including visuals and messages used in advertising campaigns.

[0295] A "target user group" is a consumer group set as the main target of an advertising campaign.

[0296] To realize this invention, a system is implemented that allows for smooth data exchange between the server and the user's terminal. The server functions as an information processing device and retrieves user attribute information from the registrant database. Based on this attribute information, an AI model is used to generate a virtual person and receives advertising-related data for that virtual person. The generated virtual person is then used to simulate a virtual user response using advertising campaign data.

[0297] The user's device can input advertising creatives through the interface and view simulated user responses. Marketing personnel can then adjust and optimize the advertising content based on these responses. Specifically, users connect to a cloud-based server using a smartphone or tablet and operate through prompt messages.

[0298] The main hardware used includes database servers and cloud servers, while the software includes, for example, Flask implemented in Python and APIs for generative AI models (such as OpenAI's GPT and Google's BERT).

[0299] As a concrete example, consider a food manufacturer testing the advertising effectiveness of a new beverage. The marketing team can simulate "how the advertising message for the new product will resonate with busy business people." An example of a prompt here would be: "New product name: Morning Energy Drink, Advertisement content: Refresh with 100% natural ingredients for an energetic morning, Target: Business people in their 20s and 30s." This allows them to prepare in advance for the product's potential market launch.

[0300] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0301] Step 1:

[0302] The server retrieves the user's attribute information from the registered database. It receives the user ID as input, searches the database for the attribute information related to that ID, and obtains the attribute information as output. Based on this attribute information, it prepares to generate a virtual character.

[0303] Step 2:

[0304] The server uses the generation AI model to generate a virtual character with the attribute information as input. The AI model analyzes the attribute information and outputs a virtual character that mimics the consumer's tendencies and characteristics. This virtual character is used in subsequent simulation processing.

[0305] Step 3:

[0306] The user inputs an advertising creative as a prompt sentence from the terminal. The input data includes information about the advertisement content and the target layer. Using this prompt sentence, the user prepares to request an advertisement simulation from the server.

[0307] Step 4:

[0308] The server simulates a virtual user reaction to the advertisement based on the generated virtual character and the prompt sentence input by the user. It takes the virtual character and the prompt sentence as input and obtains a virtual user reaction as output. It utilizes AI technology to calculate how the advertisement affects the virtual character.

[0309] Step 5:

[0310] The user's terminal receives the simulated user reaction sent from the server and displays the evaluation result as output. Based on this evaluation result, the user can adjust the advertisement content. As a specific operation, the evaluation result is visualized and presented in a form that is easy for the user to understand.

[0311] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0312] This invention is a system that combines an information processing device and an emotion engine, and aims to obtain more accurate and insightful results in market research. Specific embodiments of this system are described below.

[0313] System Configuration

[0314] The system primarily consists of a server, user terminals, and an emotion engine. The server houses a registrant database and information processing device, and is responsible for generating virtual characters and simulating questions. The user terminal includes an interface, allowing users to input questions and view answers and emotion information. The emotion engine recognizes the user's emotional responses and provides emotion data to the entire system.

[0315] Server operation

[0316] The server first retrieves attribute information from the registrant database and uses this information to generate a virtual persona using an AI model. At this stage, emotional data obtained from the emotion engine is combined with the attribute information to construct a more realistic persona.

[0317] Next, the server simulates an answer to a question received from the user's terminal. Using AI technology, it calculates how a virtual character would answer the question, taking emotional data into consideration. This process ensures that the answer reflects emotional nuances.

[0318] User terminal behavior

[0319] The user's device sends questions to the server via an interface and receives answers and sentiment-based analysis results. The user can review these results and re-evaluate and adjust the question wording. Based on the analysis results provided by the sentiment engine, more appropriate question design becomes possible.

[0320] How the emotion engine works

[0321] The emotion engine analyzes user input and responses to extract emotional data. This data is sent to a server and used to generate virtual characters and simulate questions.

[0322] Specific example

[0323] For example, when investigating market reactions to a new product, the server uses existing customer data to create a virtual person who is likely to be highly interested. When the user inputs the question, "How interested are you in the new product?" from their terminal, the emotion engine analyzes the user's potential emotional responses, and the server generates a response that takes these into account. This allows the user to obtain research results that consider emotional aspects, enabling them to consider a more appropriate market approach.

[0324] The following describes the processing flow.

[0325] Step 1:

[0326] The server retrieves user attribute information from the registrant database. This includes information such as the user's age, occupation, and hobbies, and is used as basic data to generate a virtual character.

[0327] Step 2:

[0328] The server retrieves emotional data provided by the emotion engine and combines it with attribute information. The emotional data reflects the emotional tendencies and preferences the user has shown in the past and is incorporated into the personality of the virtual character.

[0329] Step 3:

[0330] The server utilizes an AI model to generate a virtual persona based on acquired attribute information and emotional data. This virtual persona is designed to represent a specific market segment and is embodied as a persona with emotional elements.

[0331] Step 4:

[0332] The user sends questions related to a specific market research project from their device to the server. The user inputs the questions through an interface and then proceeds to ask them to a virtual person being researched.

[0333] Step 5:

[0334] The server simulates the received question for a virtual character. Using AI technology, it virtually generates how the virtual character would answer the question. This answer generation also incorporates emotional data, taking into account the user's emotional nuances.

[0335] Step 6:

[0336] The server sends a virtually generated answer to the user's terminal. The user reviews the answer and evaluates whether the question is appropriate.

[0337] Step 7:

[0338] If the user's response indicates that the question needs to be modified, the question will be revised. The results of sentiment data analysis by the sentiment engine can be used to create a more appropriate question structure.

[0339] Step 8:

[0340] The server resimulates the questions, modified as needed, and collects and analyzes the results. This enables the implementation of more appropriate and higher-quality market research.

[0341] (Example 2)

[0342] Next, we will describe Example 2. 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".

[0343] Modern market research demands a more detailed understanding of consumer emotions and reactions, but current methods struggle to adequately capture these emotional aspects. Furthermore, the generation of virtual individuals based on traditional registration information often yields unrealistic results. A new methodology is needed to address these problems and obtain more accurate and insightful market research findings.

[0344] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0345] In this invention, the server includes means for an information processing device to acquire attribute information and sentiment data from a registered user database and generate a virtual person based on said attribute information and sentiment data; means for receiving questions directed to the generated virtual person and simulating responses that take sentiment data into account using a generation AI model; means for evaluating the simulated responses and providing an interface for adjusting inappropriate questions; and means for extracting sentiment data from user input data and responses using a sentiment analysis module. This makes it possible to conduct market research using a realistic virtual person that takes emotional aspects into account and their responses.

[0346] An "information processing device" is a device that retrieves attribute information from a database and generates a virtual person based on that information.

[0347] "Attribute information" refers to information that indicates specific characteristics or profiles of registered users.

[0348] A "virtual character" is an imaginary person generated based on acquired attribute information and emotional data.

[0349] "Emotional data" refers to data that indicates a user's emotional state, obtained by analyzing their input and responses.

[0350] A "generative AI model" is a model that uses artificial intelligence to analyze data based on a specific purpose and generate results.

[0351] An "interface" is a function that provides a window or screen for interaction between the user and the system.

[0352] A "sentiment analysis module" is a part of a system that has the function of analyzing user input and responses to extract emotional data.

[0353] This invention is a market research support system that combines an information processing device and an emotion analysis module. The server retrieves user attribute information from a registered user database and generates a virtual persona using an AI model based on that information. At this time, emotion data extracted by the emotion analysis module is combined to construct a more realistic persona.

[0354] The server receives questions entered via an interface from the user's terminal. It then uses AI technology to simulate how a generated virtual character would respond. During this process, emotional data is considered, and emotional nuances are added to the answers, allowing the user to gain deeper insights.

[0355] The sentiment analysis module analyzes user input and responses, extracting sentiment data. The user's device can review the responses and analysis results sent from the server, allowing for re-evaluation and adjustment of questions, thus supporting the development of optimal market strategies. Generative AI models play a central role in this entire process, contributing to complex data analysis and response generation.

[0356] For example, if you want to check the market reaction to a new health food product, the server creates a health-conscious virtual person based on past consumption data. Then, the user inputs a question from their terminal: "How interested are you in this new health food product?" The sentiment analysis module analyzes sentiment data based on this question, and the server, taking that data into consideration, generates an emotional response from the virtual person.

[0357] An example of a prompt might be, "Simulate the emotional response of health-conscious consumers to a new food product." This allows users to obtain research results that reflect emotional aspects and select a more appropriate market approach.

[0358] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0359] Step 1:

[0360] The server retrieves user attribute information from the registrant database. Based on this attribute information as input, the server generates a virtual persona using a generative AI model. Here, emotional data provided by the emotion analysis module is also retrieved as input and added to the virtual persona's attributes to create a more realistic persona. The output of this step is a virtual persona incorporating the emotional data.

[0361] Step 2:

[0362] The user's device inputs a question to the server through an interface. This question becomes the input, and the server receives it. Based on the received question, the server uses a generative AI model to simulate a response from a virtual character. Here, it generates an answer to the input question that takes sentiment data into account. The output of this step is a simulated answer that includes emotional nuances.

[0363] Step 3:

[0364] The emotion analysis module analyzes user input data and extracts emotion data. Based on this input, it generates and outputs data indicating the user's emotional response. This outputted emotion data is used in steps 1 and 2 described above.

[0365] Step 4:

[0366] The server generates the final research findings based on simulated responses and user feedback. During this process, if a question is deemed inappropriate, the user's terminal is notified and an interface is provided allowing for question adjustment. The output of this step is the final market research findings and a set of adjustable questions.

[0367] Step 5:

[0368] Users review the results provided through their devices and formulate market approaches, adjusting questions as needed. The input for this step is the final research findings received from the server, which the user uses to develop concrete action plans. Through this process, users can build strategies that leverage sentiment-aware insights.

[0369] (Application Example 2)

[0370] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0371] In modern market research, accurately understanding consumer emotions and needs is a challenge. However, traditional methods struggle to fully grasp the emotional aspects of users, limiting their ability to understand true consumer needs. Furthermore, there is a lack of mechanisms to utilize emotional data to present consumers with more appropriate products in specific product recommendation scenarios.

[0372] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0373] In this invention, the server includes an information processing device that includes means for acquiring attribute information from a registered user database and generating a virtual person based on said attribute information, means for receiving questions directed at the generated virtual person and simulating answers, and means for analyzing the user's emotional response and making optimized product recommendations based on that. This enables market research and product recommendations that take into account the emotional aspects of consumers.

[0374] An "information processing device" is a general term for hardware or software used to acquire data from users and process and analyze various types of information based on that data.

[0375] A "registrant database" is a data structure or management system that stores user and target attribute information.

[0376] "Attribute information" refers to data that indicates characteristics and status associated with a specific subject or user.

[0377] A "virtual character" is a fictional person created using digital data or AI technology, and is the subject of simulation by users or systems.

[0378] "Emotional response" refers to the emotional reaction or feedback that a user shows to a particular object or situation.

[0379] "Optimized product recommendations" is a process that takes into account the user's needs and emotions, and provides individually tailored product suggestions.

[0380] An "interface" refers to the means of communication and display screens that allow a user to interact directly with a system.

[0381] A specific embodiment of this invention will now be described. The system mainly consists of a server, a user terminal, and an emotion engine. The server is an information processing device with big data processing capabilities, which retrieves attribute information from a registered user database and generates a virtual person using AI technology. Questions directed to the generated virtual person are sent from the user's terminal, which is equipped with an ergonomically designed user interface.

[0382] The user's device first acts as a facilitator, sending questions to the server for the generated virtual character. Upon receiving the questions, the server uses an AI model to simulate the virtual character's emotional responses. In this process, the emotion engine analyzes the user's emotional reactions and provides this data to the entire system. This enables the server to generate highly accurate answers and product recommendations that take emotional data into account.

[0383] For example, in an e-commerce application, when a user browses products through smart glasses, emotional data is analyzed in real time from their gaze and facial expressions. The emotion engine sends this data to a server, which then prioritizes recommending products that match the user's preferences. This process utilizes image processing libraries such as OpenCV and emotion analysis APIs to grasp the user's emotions in real time and provide appropriate product suggestions.

[0384] Specific examples of prompt messages include: "Detect products that make the user smile and recommend similar products from that product category. What other combinations of emotional responses and product categories should be considered?" This allows for proactively suggesting products that evoke joy and interest in the user, ultimately providing a more attractive purchasing experience for consumers.

[0385] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0386] Step 1:

[0387] The user's device sends questions created by the user to the server via the user interface. The input is the user's question content, and the output is transferred to the server. On the interface, data is sent to the server when the send button is clicked.

[0388] Step 2:

[0389] The server retrieves attribute information from the registrant database and uses this data to generate a virtual character using a generative AI model. The input is attribute information from the database, and the output is digital data of the virtual character. Here, an AI-built algorithm operates to generate a realistic and appealing virtual character.

[0390] Step 3:

[0391] The emotion engine analyzes emotional data, such as facial expressions and gaze, transmitted in real time from the user's device. Input is video data from a visual sensor, and the extracted emotional parameters are sent to the server as output. An image processing library is used to quantify the user's emotions.

[0392] Step 4:

[0393] The server simulates generated questions for a virtual character and produces answers based on sentiment data. The input consists of the user's question and sentiment data, and the output is a response that reflects those sentiments. AI models are also utilized here to provide emotionally resonant answers.

[0394] Step 5:

[0395] The server recommends the most suitable products based on the user's emotional data and simulation results. Inputs include emotional data, answers to questions, and past purchase history data, and the output is an optimized product list. Based on this information, the server presents products that reflect the user's tastes and preferences.

[0396] Step 6:

[0397] The user's device receives an optimized product list sent from the server and displays it on the user interface. The input is the product list sent from the server, and the output is the display of those products on the device screen. Specific actions include screen refresh and information updates.

[0398] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0399] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0400] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0401] [Third Embodiment]

[0402] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0403] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0404] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0405] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0406] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0407] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0408] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0409] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0410] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0411] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0412] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0413] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0414] This invention is a system designed to improve the accuracy of market research, and this system is implemented by a program that runs within an information processing device. Specifically, the invention can be carried out in the following forms.

[0415] System-wide configuration

[0416] The system is primarily composed of a server, user terminals, and communication interfaces between them. The server contains a registrant database and information processing devices, which generate and simulate virtual characters. User terminals exchange data with the server through the interface, inputting questions, confirming answers, and making adjustments.

[0417] Server operation

[0418] The server first retrieves user attribute information from the registrant database. Based on this information, it uses an AI model to analyze user trends and generate a virtual persona. This process takes into account the user's profile and past response history to create a persona aligned with the expected market segment.

[0419] Next, the server simulates the survey questions received from the user's device for a virtual persona. Using AI technology, it generates virtual answers and sends the results to the user's device. It then evaluates whether these answers match the reactions expected by the persona.

[0420] User terminal behavior

[0421] The user's device sends a question to the server through the interface, and then receives and displays the answer from the server. The user can review the simulated answer and, based on the results, adjust the question wording or modify the virtual character's settings.

[0422] Specific example

[0423] For example, if a company wants to investigate market reaction to a new insurance product, the server generates a profile of a potential customer from an existing customer database. When a user inputs a question from their terminal, such as "How likely are you to subscribe to this insurance product?", the server generates and presents a hypothetical response. Based on this response, the user can refine the question, adjust the direction of the investigation, and prepare for the actual market research.

[0424] The following describes the processing flow.

[0425] Step 1:

[0426] The server retrieves user attribute information from the registrant database. This includes data such as the user's gender, age, annual income, and areas of interest. The server then prepares an initial dataset based on this information.

[0427] Step 2:

[0428] The server uses an AI model to analyze the acquired attribute information. Taking into account past response history and other factors, it determines the characteristics of the persona and generates a virtual character. Each attribute of the generated virtual character is adjusted to fit the expected market segment.

[0429] Step 3:

[0430] The user's terminal sends a survey question to the server. The user enters the question text through the interface and sends the request to the server by pressing the submit button.

[0431] Step 4:

[0432] The server simulates the questions received from the user against generated virtual characters. Using AI technology, it virtually calculates how each virtual character would answer the questions.

[0433] Step 5:

[0434] The server presents a virtually generated response to the user's device. The user reviews the presented response and evaluates whether the virtual character's reaction matches their expectations.

[0435] Step 6:

[0436] The questions are reviewed based on the answers provided by the user. Users can adjust or modify questions that seem unclear or ineffective. They can also modify the characteristics of the virtual character as needed.

[0437] Step 7:

[0438] Finally, the question set that the user has finished adjusting is sent back to the server. The server re-evaluates the adjusted questions and accumulates data to help design the optimal survey.

[0439] (Example 1)

[0440] Next, we will describe Example 1. 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."

[0441] Traditional market research methods have struggled to generate highly accurate virtual individuals based on consumer attribute information and to quickly simulate their responses to survey questions. This has led to problems with the reliability of survey results and the consistency of responses. Furthermore, adjusting inappropriate questions often relies on manual methods, which is time-consuming and laborious.

[0442] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0443] In this invention, the server includes means for an information processing device to acquire attribute information from a storage device and generate a virtual person based on said attribute information, means for receiving survey items for the generated virtual person and simulating answers using AI technology, and means for providing an input device for evaluating the simulated answers and adjusting inappropriate survey items. This enables users to conduct highly accurate market research quickly and efficiently, and to flexibly adjust survey items and virtual person settings.

[0444] An "information processing device" is a mechanical or electronic device that takes data as input, performs specific processing on it, and then outputs the result.

[0445] A "storage device" is a device that stores digital data and supplies that data as needed.

[0446] "Attribute information" refers to information that indicates the characteristics and traits associated with a specific individual or object.

[0447] A "virtual character" is a fictional character created on a computer for simulation purposes, modeled after a real person and based on specific attribute information.

[0448] "Survey items" are elements such as questions and tasks set up for a specific purpose, used to provide or elicit information from the survey subjects.

[0449] "AI technology" refers to a set of technologies that enable intelligent behavior by machines, based on the theory and implementation of artificial intelligence.

[0450] A "simulated response" is a virtual reaction or reply generated using AI technology based on questions directed at a virtual character.

[0451] An "input device" is a physical or electronic device used to transmit data or instructions to an information processing device.

[0452] A "display device" is a device used to visually display information in digital or analog format.

[0453] This invention is a system that enables efficient market research and is built around an information processing device. The invention is implemented using a server, terminals, and a communication interface connecting them. The server is equipped with a storage device for holding attribute information and a generative AI model for performing data processing using AI. This AI model is used to generate virtual characters and simulate their reactions.

[0454] Specifically, the server retrieves user attribute information from storage and generates a virtual person based on that information using a generative AI model. After generating the virtual person, it receives survey items from the user terminal and simulates the answers using AI technology. The server evaluates the results and provides a system that allows for adjustment of the survey items via input and display devices for providing the results to the user.

[0455] For example, if a company wants to conduct market research for a new product, the server designs a virtual person with specific attributes based on existing customer records. The user inputs specific research questions via a terminal, such as, "How likely are you to be interested in this product?" Based on this prompt, the server uses a generative AI model to simulate the virtual person's responses. This result is sent to the user, who can then refine the research questions and the virtual person's settings.

[0456] An example of a prompt might be, "Simulate how men in their 30s would react to a new product category in terms of purchasing intent." Using such specific prompts makes it possible to conduct more precise and targeted market research.

[0457] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0458] Step 1:

[0459] The server retrieves user attribute information from its storage device. It receives the user's ID as input and searches the database for attribute information such as age, gender, occupation, and past response history based on that ID. This information serves as the basic data for generating a virtual character.

[0460] Step 2:

[0461] The server uses the acquired attribute information to run a generative AI model and generate a virtual person. By providing attribute information as input to the generative AI model, it outputs a virtual person based on the relevant market segment. Specifically, it forms a person profile that reflects the user's interests and behavioral patterns.

[0462] Step 3:

[0463] The user's terminal sends survey items to the server. The user enters prompt text for the survey items from the terminal, and this data is sent to the server. These survey items are the information necessary for simulating a virtual character.

[0464] Step 4:

[0465] The server performs simulations with a virtual person based on the received survey items. Using AI technology with the survey items as input, it generates responses from the virtual person. The output is the virtual person's reactions and responses. Here, response generation takes into account past reactions and trends.

[0466] Step 5:

[0467] The server evaluates the simulated answers and sends the results to the user's terminal. It receives the generated answers as input and determines their usefulness and validity based on evaluation criteria. The evaluation results are provided to the user as output.

[0468] Step 6:

[0469] The user's device receives the transmitted simulation results and displays them, allowing the user to confirm the results. Based on this display, the user can adjust the investigation items and virtual character settings. Specifically, the user can input the adjustments and prepare the next investigation accordingly.

[0470] (Application Example 1)

[0471] Next, we will explain Application Example 1. In the following explanation, 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."

[0472] When conducting advertising campaigns in the market, it is extremely difficult to evaluate in advance how to effectively approach the target user group. Traditional methods make it difficult to accurately predict advertising content before obtaining actual consumer responses, potentially leading to wasted time and resources. There is a need for methods to improve this situation and develop advertising strategies more efficiently.

[0473] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0474] In this invention, the server includes means for an information processing device to acquire attribute information from a registered user database and generate a virtual person based on said attribute information; means for receiving advertising-related data for the generated virtual person and simulating virtual user responses; means for evaluating virtual user responses based on the virtual person and providing an interface for adjusting advertising content; and means for a marketing person to input advertising creatives and simulate responses from the target user group. This makes it possible to predict the responses of the target user group and build an efficient advertising strategy before implementing an advertising campaign.

[0475] An "information processing device" is a device that retrieves attribute information from a database, generates virtual characters, and processes advertising-related data.

[0476] A "registrant database" is a structured collection of information used to store attribute information about users.

[0477] "Attribute information" refers to information about a user's characteristics and behavior, and is data used to create virtual characters.

[0478] A "virtual character" is a fictional user model generated based on attribute information obtained from a registered user database, used to predict advertising effectiveness.

[0479] "Advertising-related data" refers to data entered by marketing personnel in relation to advertising campaigns, and is necessary information for simulating responses to virtual characters.

[0480] "Simulation" is the process of reproducing and analyzing expected reactions and results using a virtual model.

[0481] "User response" refers to data that shows the responses and actions of a virtual character to an advertising campaign.

[0482] An "interface" is a means of providing a user interface or user control elements for communication between the user and the system.

[0483] A "marketing professional" is a specialist responsible for planning, executing, and evaluating advertising campaigns.

[0484] "Advertising creative" refers to creative works, including visuals and messages, used in advertising campaigns.

[0485] The "target user group" refers to the consumer group that is primarily targeted by an advertising campaign.

[0486] To realize this invention, a system is implemented that allows for smooth data exchange between the server and the user's terminal. The server functions as an information processing device and retrieves user attribute information from the registrant database. Based on this attribute information, an AI model is used to generate a virtual person and receives advertising-related data for that virtual person. The generated virtual person is then used to simulate a virtual user response using advertising campaign data.

[0487] The user's device can input advertising creatives through the interface and view simulated user responses. Marketing personnel can then adjust and optimize the advertising content based on these responses. Specifically, users connect to a cloud-based server using a smartphone or tablet and operate through prompt messages.

[0488] The main hardware used includes database servers and cloud servers, while the software includes, for example, Flask implemented in Python and APIs for generative AI models (such as OpenAI's GPT and Google's BERT).

[0489] As a concrete example, consider a food manufacturer testing the advertising effectiveness of a new beverage. The marketing team can simulate "how the advertising message for the new product will resonate with busy business people." An example of a prompt here would be: "New product name: Morning Energy Drink, Advertisement content: Refresh with 100% natural ingredients for an energetic morning, Target: Business people in their 20s and 30s." This allows them to prepare in advance for the product's potential market launch.

[0490] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0491] Step 1:

[0492] The server retrieves user attribute information from the registrant database. It receives a user ID as input, searches the database for attribute information associated with that ID, and retrieves the attribute information as output. Based on this attribute information, it prepares to generate a virtual character.

[0493] Step 2:

[0494] The server uses attribute information as input to generate a virtual character using a generative AI model. The AI ​​model analyzes the attribute information and outputs a virtual character that mimics consumer tendencies and characteristics. This virtual character is then used in subsequent simulation processes.

[0495] Step 3:

[0496] The user inputs the ad creative as a prompt message from their device. This input data includes information about the ad content and target audience. This prompt message is used to prepare a request for ad simulation from the server.

[0497] Step 4:

[0498] The server simulates a virtual user response to an advertisement based on the generated virtual character and the prompt text entered by the user. It takes the virtual character and prompt text as input and the virtual user response as output. AI technology is used to calculate how the advertisement will affect the virtual character.

[0499] Step 5:

[0500] The user's device receives simulated user responses sent from the server and displays the evaluation results as output. Based on these evaluation results, the user can adjust the ad content. Specifically, the evaluation results are visualized and presented in a way that is easy for the user to understand.

[0501] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0502] This invention is a system that combines an information processing device and an emotion engine, and aims to obtain more accurate and insightful results in market research. Specific embodiments of this system are described below.

[0503] System Configuration

[0504] The system primarily consists of a server, user terminals, and an emotion engine. The server houses a registrant database and information processing device, and is responsible for generating virtual characters and simulating questions. The user terminal includes an interface, allowing users to input questions and view answers and emotion information. The emotion engine recognizes the user's emotional responses and provides emotion data to the entire system.

[0505] Server operation

[0506] The server first retrieves attribute information from the registrant database and uses this information to generate a virtual persona using an AI model. At this stage, emotional data obtained from the emotion engine is combined with the attribute information to construct a more realistic persona.

[0507] Next, the server simulates an answer to a question received from the user's terminal. Using AI technology, it calculates how a virtual character would answer the question, taking emotional data into consideration. This process ensures that the answer reflects emotional nuances.

[0508] User terminal behavior

[0509] The user's device sends questions to the server via an interface and receives answers and sentiment-based analysis results. The user can review these results and re-evaluate and adjust the question wording. Based on the analysis results provided by the sentiment engine, more appropriate question design becomes possible.

[0510] How the emotion engine works

[0511] The emotion engine analyzes user input and responses to extract emotional data. This data is sent to a server and used to generate virtual characters and simulate questions.

[0512] Specific example

[0513] For example, when investigating market reactions to a new product, the server uses existing customer data to create a virtual person who is likely to be highly interested. When the user inputs the question, "How interested are you in the new product?" from their terminal, the emotion engine analyzes the user's potential emotional responses, and the server generates a response that takes these into account. This allows the user to obtain research results that consider emotional aspects, enabling them to consider a more appropriate market approach.

[0514] The following describes the processing flow.

[0515] Step 1:

[0516] The server retrieves user attribute information from the registrant database. This includes information such as the user's age, occupation, and hobbies, and is used as basic data to generate a virtual character.

[0517] Step 2:

[0518] The server retrieves emotional data provided by the emotion engine and combines it with attribute information. The emotional data reflects the emotional tendencies and preferences the user has shown in the past and is incorporated into the personality of the virtual character.

[0519] Step 3:

[0520] The server utilizes an AI model to generate a virtual persona based on acquired attribute information and emotional data. This virtual persona is designed to represent a specific market segment and is embodied as a persona with emotional elements.

[0521] Step 4:

[0522] The user sends questions related to a specific market research project from their device to the server. The user inputs the questions through an interface and then proceeds to ask them to a virtual person being researched.

[0523] Step 5:

[0524] The server simulates the received question for a virtual character. Using AI technology, it virtually generates how the virtual character would answer the question. This answer generation also incorporates emotional data, taking into account the user's emotional nuances.

[0525] Step 6:

[0526] The server sends a virtually generated answer to the user's terminal. The user reviews the answer and evaluates whether the question is appropriate.

[0527] Step 7:

[0528] If the user's response indicates that the question needs to be modified, the question will be revised. The results of sentiment data analysis by the sentiment engine can be used to create a more appropriate question structure.

[0529] Step 8:

[0530] The server resimulates the questions, modified as needed, and collects and analyzes the results. This enables the implementation of more appropriate and higher-quality market research.

[0531] (Example 2)

[0532] Next, we will describe Example 2. 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."

[0533] Modern market research demands a more detailed understanding of consumer emotions and reactions, but current methods struggle to adequately capture these emotional aspects. Furthermore, the generation of virtual individuals based on traditional registration information often yields unrealistic results. A new methodology is needed to address these problems and obtain more accurate and insightful market research findings.

[0534] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0535] In this invention, the server includes means for an information processing device to acquire attribute information and sentiment data from a registered user database and generate a virtual person based on said attribute information and sentiment data; means for receiving questions directed to the generated virtual person and simulating responses that take sentiment data into account using a generation AI model; means for evaluating the simulated responses and providing an interface for adjusting inappropriate questions; and means for extracting sentiment data from user input data and responses using a sentiment analysis module. This makes it possible to conduct market research using a realistic virtual person that takes emotional aspects into account and their responses.

[0536] An "information processing device" is a device that retrieves attribute information from a database and generates a virtual person based on that information.

[0537] "Attribute information" refers to information that indicates specific characteristics or profiles of registered users.

[0538] A "virtual character" is an imaginary person generated based on acquired attribute information and emotional data.

[0539] "Emotional data" refers to data that indicates a user's emotional state, obtained by analyzing their input and responses.

[0540] A "generative AI model" is a model that uses artificial intelligence to analyze data based on a specific purpose and generate results.

[0541] An "interface" is a function that provides a window or screen for interaction between the user and the system.

[0542] A "sentiment analysis module" is a part of a system that has the function of analyzing user input and responses to extract emotional data.

[0543] This invention is a market research support system that combines an information processing device and an emotion analysis module. The server retrieves user attribute information from a registered user database and generates a virtual persona using an AI model based on that information. At this time, emotion data extracted by the emotion analysis module is combined to construct a more realistic persona.

[0544] The server receives questions entered via an interface from the user's terminal. It then uses AI technology to simulate how a generated virtual character would respond. During this process, emotional data is considered, and emotional nuances are added to the answers, allowing the user to gain deeper insights.

[0545] The sentiment analysis module analyzes user input and responses, extracting sentiment data. The user's device can review the responses and analysis results sent from the server, allowing for re-evaluation and adjustment of questions, thus supporting the development of optimal market strategies. Generative AI models play a central role in this entire process, contributing to complex data analysis and response generation.

[0546] For example, if you want to check the market reaction to a new health food product, the server creates a health-conscious virtual person based on past consumption data. Then, the user inputs a question from their terminal: "How interested are you in this new health food product?" The sentiment analysis module analyzes sentiment data based on this question, and the server, taking that data into consideration, generates an emotional response from the virtual person.

[0547] An example of a prompt might be, "Simulate the emotional response of health-conscious consumers to a new food product." This allows users to obtain research results that reflect emotional aspects and select a more appropriate market approach.

[0548] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0549] Step 1:

[0550] The server retrieves user attribute information from the registrant database. Based on this attribute information as input, the server generates a virtual persona using a generative AI model. Here, emotional data provided by the emotion analysis module is also retrieved as input and added to the virtual persona's attributes to create a more realistic persona. The output of this step is a virtual persona incorporating the emotional data.

[0551] Step 2:

[0552] The user's device inputs a question to the server through an interface. This question becomes the input, and the server receives it. Based on the received question, the server uses a generative AI model to simulate a response from a virtual character. Here, it generates an answer to the input question that takes sentiment data into account. The output of this step is a simulated answer that includes emotional nuances.

[0553] Step 3:

[0554] The emotion analysis module analyzes user input data and extracts emotion data. Based on this input, it generates and outputs data indicating the user's emotional response. This outputted emotion data is used in steps 1 and 2 described above.

[0555] Step 4:

[0556] The server generates the final research findings based on simulated responses and user feedback. During this process, if a question is deemed inappropriate, the user's terminal is notified and an interface is provided allowing for question adjustment. The output of this step is the final market research findings and a set of adjustable questions.

[0557] Step 5:

[0558] Users review the results provided through their devices and formulate market approaches, adjusting questions as needed. The input for this step is the final research findings received from the server, which the user uses to develop concrete action plans. Through this process, users can build strategies that leverage sentiment-aware insights.

[0559] (Application Example 2)

[0560] Next, we will explain application example 2. In the following explanation, 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."

[0561] In modern market research, accurately understanding consumer emotions and needs is a challenge. However, traditional methods struggle to fully grasp the emotional aspects of users, limiting their ability to understand true consumer needs. Furthermore, there is a lack of mechanisms to utilize emotional data to present consumers with more appropriate products in specific product recommendation scenarios.

[0562] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0563] In this invention, the server includes an information processing device that includes means for acquiring attribute information from a registered user database and generating a virtual person based on said attribute information, means for receiving questions directed at the generated virtual person and simulating answers, and means for analyzing the user's emotional response and making optimized product recommendations based on that. This enables market research and product recommendations that take into account the emotional aspects of consumers.

[0564] An "information processing device" is a general term for hardware or software used to acquire data from users and process and analyze various types of information based on that data.

[0565] A "registrant database" is a data structure or management system that stores user and target attribute information.

[0566] "Attribute information" refers to data that indicates characteristics and status associated with a specific subject or user.

[0567] A "virtual character" is a fictional person created using digital data or AI technology, and is the subject of simulation by users or systems.

[0568] "Emotional response" refers to the emotional reaction or feedback that a user shows to a particular object or situation.

[0569] "Optimized product recommendations" is a process that takes into account the user's needs and emotions, and provides individually tailored product suggestions.

[0570] An "interface" refers to the means of communication and display screens that allow a user to interact directly with a system.

[0571] A specific embodiment of this invention will now be described. The system mainly consists of a server, a user terminal, and an emotion engine. The server is an information processing device with big data processing capabilities, which retrieves attribute information from a registered user database and generates a virtual person using AI technology. Questions directed to the generated virtual person are sent from the user's terminal, which is equipped with an ergonomically designed user interface.

[0572] The user's device first acts as a facilitator, sending questions to the server for the generated virtual character. Upon receiving the questions, the server uses an AI model to simulate the virtual character's emotional responses. In this process, the emotion engine analyzes the user's emotional reactions and provides this data to the entire system. This enables the server to generate highly accurate answers and product recommendations that take emotional data into account.

[0573] For example, in an e-commerce application, when a user browses products through smart glasses, emotional data is analyzed in real time from their gaze and facial expressions. The emotion engine sends this data to a server, which then prioritizes recommending products that match the user's preferences. This process utilizes image processing libraries such as OpenCV and emotion analysis APIs to grasp the user's emotions in real time and provide appropriate product suggestions.

[0574] Specific examples of prompt messages include: "Detect products that make the user smile and recommend similar products from that product category. What other combinations of emotional responses and product categories should be considered?" This allows for proactively suggesting products that evoke joy and interest in the user, ultimately providing a more attractive purchasing experience for consumers.

[0575] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0576] Step 1:

[0577] The user's device sends questions created by the user to the server via the user interface. The input is the user's question content, and the output is transferred to the server. On the interface, data is sent to the server when the send button is clicked.

[0578] Step 2:

[0579] The server retrieves attribute information from the registrant database and uses this data to generate a virtual character using a generative AI model. The input is attribute information from the database, and the output is digital data of the virtual character. Here, an AI-built algorithm operates to generate a realistic and appealing virtual character.

[0580] Step 3:

[0581] The emotion engine analyzes emotional data, such as facial expressions and gaze, transmitted in real time from the user's device. Input is video data from a visual sensor, and the extracted emotional parameters are sent to the server as output. An image processing library is used to quantify the user's emotions.

[0582] Step 4:

[0583] The server simulates generated questions for a virtual character and produces answers based on sentiment data. The input consists of the user's question and sentiment data, and the output is a response that reflects those sentiments. AI models are also utilized here to provide emotionally resonant answers.

[0584] Step 5:

[0585] The server recommends the most suitable products based on the user's emotional data and simulation results. Inputs include emotional data, answers to questions, and past purchase history data, and the output is an optimized product list. Based on this information, the server presents products that reflect the user's tastes and preferences.

[0586] Step 6:

[0587] The user's device receives an optimized product list sent from the server and displays it on the user interface. The input is the product list sent from the server, and the output is the display of those products on the device screen. Specific actions include screen refresh and information updates.

[0588] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0589] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0590] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0591] [Fourth Embodiment]

[0592] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0593] As shown in Figure 7, the 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.

[0594] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0595] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0596] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0597] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0598] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0599] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0600] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0601] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0602] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0603] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0604] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0605] This invention is a system designed to improve the accuracy of market research, and this system is implemented by a program that runs within an information processing device. Specifically, the invention can be carried out in the following forms.

[0606] System-wide configuration

[0607] The system is primarily composed of a server, user terminals, and communication interfaces between them. The server contains a registrant database and information processing devices, which generate and simulate virtual characters. User terminals exchange data with the server through the interface, inputting questions, confirming answers, and making adjustments.

[0608] Server operation

[0609] The server first retrieves user attribute information from the registrant database. Based on this information, it uses an AI model to analyze user trends and generate a virtual persona. This process takes into account the user's profile and past response history to create a persona aligned with the expected market segment.

[0610] Next, the server simulates the survey questions received from the user's device for a virtual persona. Using AI technology, it generates virtual answers and sends the results to the user's device. It then evaluates whether these answers match the reactions expected by the persona.

[0611] User terminal behavior

[0612] The user's device sends a question to the server through the interface, and then receives and displays the answer from the server. The user can review the simulated answer and, based on the results, adjust the question wording or modify the virtual character's settings.

[0613] Specific example

[0614] For example, if a company wants to investigate market reaction to a new insurance product, the server generates a profile of a potential customer from an existing customer database. When a user inputs a question from their terminal, such as "How likely are you to subscribe to this insurance product?", the server generates and presents a hypothetical response. Based on this response, the user can refine the question, adjust the direction of the investigation, and prepare for the actual market research.

[0615] The following describes the processing flow.

[0616] Step 1:

[0617] The server retrieves user attribute information from the registrant database. This includes data such as the user's gender, age, annual income, and areas of interest. The server then prepares an initial dataset based on this information.

[0618] Step 2:

[0619] The server uses an AI model to analyze the acquired attribute information. Taking into account past response history and other factors, it determines the characteristics of the persona and generates a virtual character. Each attribute of the generated virtual character is adjusted to fit the expected market segment.

[0620] Step 3:

[0621] The user's terminal sends a survey question to the server. The user enters the question text through the interface and sends the request to the server by pressing the submit button.

[0622] Step 4:

[0623] The server simulates the questions received from the user against generated virtual characters. Using AI technology, it virtually calculates how each virtual character would answer the questions.

[0624] Step 5:

[0625] The server presents a virtually generated response to the user's device. The user reviews the presented response and evaluates whether the virtual character's reaction matches their expectations.

[0626] Step 6:

[0627] The questions are reviewed based on the answers provided by the user. Users can adjust or modify questions that seem unclear or ineffective. They can also modify the characteristics of the virtual character as needed.

[0628] Step 7:

[0629] Finally, the question set that the user has finished adjusting is sent back to the server. The server re-evaluates the adjusted questions and accumulates data to help design the optimal survey.

[0630] (Example 1)

[0631] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0632] Traditional market research methods have struggled to generate highly accurate virtual individuals based on consumer attribute information and to quickly simulate their responses to survey questions. This has led to problems with the reliability of survey results and the consistency of responses. Furthermore, adjusting inappropriate questions often relies on manual methods, which is time-consuming and laborious.

[0633] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0634] In this invention, the server includes means for an information processing device to acquire attribute information from a storage device and generate a virtual person based on said attribute information, means for receiving survey items for the generated virtual person and simulating answers using AI technology, and means for providing an input device for evaluating the simulated answers and adjusting inappropriate survey items. This enables users to conduct highly accurate market research quickly and efficiently, and to flexibly adjust survey items and virtual person settings.

[0635] An "information processing device" is a mechanical or electronic device that takes data as input, performs specific processing on it, and then outputs the result.

[0636] A "storage device" is a device that stores digital data and supplies that data as needed.

[0637] "Attribute information" refers to information that indicates the characteristics and traits associated with a specific individual or object.

[0638] A "virtual character" is a fictional character created on a computer for simulation purposes, modeled after a real person and based on specific attribute information.

[0639] "Survey items" are elements such as questions and tasks set up for a specific purpose, used to provide or elicit information from the survey subjects.

[0640] "AI technology" refers to a set of technologies that enable intelligent behavior by machines, based on the theory and implementation of artificial intelligence.

[0641] A "simulated response" is a virtual reaction or reply generated using AI technology based on questions directed at a virtual character.

[0642] An "input device" is a physical or electronic device used to transmit data or instructions to an information processing device.

[0643] A "display device" is a device used to visually display information in digital or analog format.

[0644] This invention is a system that enables efficient market research and is built around an information processing device. The invention is implemented using a server, terminals, and a communication interface connecting them. The server is equipped with a storage device for holding attribute information and a generative AI model for performing data processing using AI. This AI model is used to generate virtual characters and simulate their reactions.

[0645] Specifically, the server retrieves user attribute information from storage and generates a virtual person based on that information using a generative AI model. After generating the virtual person, it receives survey items from the user terminal and simulates the answers using AI technology. The server evaluates the results and provides a system that allows for adjustment of the survey items via input and display devices for providing the results to the user.

[0646] For example, if a company wants to conduct market research for a new product, the server designs a virtual person with specific attributes based on existing customer records. The user inputs specific research questions via a terminal, such as, "How likely are you to be interested in this product?" Based on this prompt, the server uses a generative AI model to simulate the virtual person's responses. This result is sent to the user, who can then refine the research questions and the virtual person's settings.

[0647] An example of a prompt might be, "Simulate how men in their 30s would react to a new product category in terms of purchasing intent." Using such specific prompts makes it possible to conduct more precise and targeted market research.

[0648] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0649] Step 1:

[0650] The server retrieves user attribute information from its storage device. It receives the user's ID as input and searches the database for attribute information such as age, gender, occupation, and past response history based on that ID. This information serves as the basic data for generating a virtual character.

[0651] Step 2:

[0652] The server uses the acquired attribute information to run a generative AI model and generate a virtual person. By providing attribute information as input to the generative AI model, it outputs a virtual person based on the relevant market segment. Specifically, it forms a person profile that reflects the user's interests and behavioral patterns.

[0653] Step 3:

[0654] The user's terminal sends survey items to the server. The user enters prompt text for the survey items from the terminal, and this data is sent to the server. These survey items are the information necessary for simulating a virtual character.

[0655] Step 4:

[0656] The server performs simulations with a virtual person based on the received survey items. Using AI technology with the survey items as input, it generates responses from the virtual person. The output is the virtual person's reactions and responses. Here, response generation takes into account past reactions and trends.

[0657] Step 5:

[0658] The server evaluates the simulated answers and sends the results to the user's terminal. It receives the generated answers as input and determines their usefulness and validity based on evaluation criteria. The evaluation results are provided to the user as output.

[0659] Step 6:

[0660] The user's device receives the transmitted simulation results and displays them, allowing the user to confirm the results. Based on this display, the user can adjust the investigation items and virtual character settings. Specifically, the user can input the adjustments and prepare the next investigation accordingly.

[0661] (Application Example 1)

[0662] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0663] When conducting advertising campaigns in the market, it is extremely difficult to evaluate in advance how to effectively approach the target user group. Traditional methods make it difficult to accurately predict advertising content before obtaining actual consumer responses, potentially leading to wasted time and resources. There is a need for methods to improve this situation and develop advertising strategies more efficiently.

[0664] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0665] In this invention, the server includes means for an information processing device to acquire attribute information from a registered user database and generate a virtual person based on said attribute information; means for receiving advertising-related data for the generated virtual person and simulating virtual user responses; means for evaluating virtual user responses based on the virtual person and providing an interface for adjusting advertising content; and means for a marketing person to input advertising creatives and simulate responses from the target user group. This makes it possible to predict the responses of the target user group and build an efficient advertising strategy before implementing an advertising campaign.

[0666] An "information processing device" is a device that retrieves attribute information from a database, generates virtual characters, and processes advertising-related data.

[0667] A "registrant database" is a structured collection of information used to store attribute information about users.

[0668] "Attribute information" refers to information about a user's characteristics and behavior, and is data used to create virtual characters.

[0669] A "virtual character" is a fictional user model generated based on attribute information obtained from a registered user database, used to predict advertising effectiveness.

[0670] "Advertising-related data" refers to data entered by marketing personnel in relation to advertising campaigns, and is necessary information for simulating responses to virtual characters.

[0671] "Simulation" is the process of reproducing and analyzing expected reactions and results using a virtual model.

[0672] "User response" refers to data that shows the responses and actions of a virtual character to an advertising campaign.

[0673] An "interface" is a means of providing a user interface or user control elements for communication between the user and the system.

[0674] A "marketing professional" is a specialist responsible for planning, executing, and evaluating advertising campaigns.

[0675] "Advertising creative" refers to creative works, including visuals and messages, used in advertising campaigns.

[0676] The "target user group" refers to the consumer group that is primarily targeted by an advertising campaign.

[0677] To realize this invention, a system is implemented that allows for smooth data exchange between the server and the user's terminal. The server functions as an information processing device and retrieves user attribute information from the registrant database. Based on this attribute information, an AI model is used to generate a virtual person and receives advertising-related data for that virtual person. The generated virtual person is then used to simulate a virtual user response using advertising campaign data.

[0678] The user's device can input advertising creatives through the interface and view simulated user responses. Marketing personnel can then adjust and optimize the advertising content based on these responses. Specifically, users connect to a cloud-based server using a smartphone or tablet and operate through prompt messages.

[0679] The main hardware used includes database servers and cloud servers, while the software includes, for example, Flask implemented in Python and APIs for generative AI models (such as OpenAI's GPT and Google's BERT).

[0680] As a concrete example, consider a food manufacturer testing the advertising effectiveness of a new beverage. The marketing team can simulate "how the advertising message for the new product will resonate with busy business people." An example of a prompt here would be: "New product name: Morning Energy Drink, Advertisement content: Refresh with 100% natural ingredients for an energetic morning, Target: Business people in their 20s and 30s." This allows them to prepare in advance for the product's potential market launch.

[0681] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0682] Step 1:

[0683] The server retrieves user attribute information from the registrant database. It receives a user ID as input, searches the database for attribute information associated with that ID, and retrieves the attribute information as output. Based on this attribute information, it prepares to generate a virtual character.

[0684] Step 2:

[0685] The server uses attribute information as input to generate a virtual character using a generative AI model. The AI ​​model analyzes the attribute information and outputs a virtual character that mimics consumer tendencies and characteristics. This virtual character is then used in subsequent simulation processes.

[0686] Step 3:

[0687] The user inputs the ad creative as a prompt message from their device. This input data includes information about the ad content and target audience. This prompt message is used to prepare a request for ad simulation from the server.

[0688] Step 4:

[0689] The server simulates a virtual user response to an advertisement based on the generated virtual character and the prompt text entered by the user. It takes the virtual character and prompt text as input and the virtual user response as output. AI technology is used to calculate how the advertisement will affect the virtual character.

[0690] Step 5:

[0691] The user's device receives simulated user responses sent from the server and displays the evaluation results as output. Based on these evaluation results, the user can adjust the ad content. Specifically, the evaluation results are visualized and presented in a way that is easy for the user to understand.

[0692] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0693] This invention is a system that combines an information processing device and an emotion engine, and aims to obtain more accurate and insightful results in market research. Specific embodiments of this system are described below.

[0694] System Configuration

[0695] The system primarily consists of a server, user terminals, and an emotion engine. The server houses a registrant database and information processing device, and is responsible for generating virtual characters and simulating questions. The user terminal includes an interface, allowing users to input questions and view answers and emotion information. The emotion engine recognizes the user's emotional responses and provides emotion data to the entire system.

[0696] Server operation

[0697] The server first retrieves attribute information from the registrant database and uses this information to generate a virtual persona using an AI model. At this stage, emotional data obtained from the emotion engine is combined with the attribute information to construct a more realistic persona.

[0698] Next, the server simulates an answer to a question received from the user's terminal. Using AI technology, it calculates how a virtual character would answer the question, taking emotional data into consideration. This process ensures that the answer reflects emotional nuances.

[0699] User terminal behavior

[0700] The user's device sends questions to the server via an interface and receives answers and sentiment-based analysis results. The user can review these results and re-evaluate and adjust the question wording. Based on the analysis results provided by the sentiment engine, more appropriate question design becomes possible.

[0701] How the emotion engine works

[0702] The emotion engine analyzes user input and responses to extract emotional data. This data is sent to a server and used to generate virtual characters and simulate questions.

[0703] Specific example

[0704] For example, when investigating market reactions to a new product, the server uses existing customer data to create a virtual person who is likely to be highly interested. When the user inputs the question, "How interested are you in the new product?" from their terminal, the emotion engine analyzes the user's potential emotional responses, and the server generates a response that takes these into account. This allows the user to obtain research results that consider emotional aspects, enabling them to consider a more appropriate market approach.

[0705] The following describes the processing flow.

[0706] Step 1:

[0707] The server retrieves user attribute information from the registrant database. This includes information such as the user's age, occupation, and hobbies, and is used as basic data to generate a virtual character.

[0708] Step 2:

[0709] The server retrieves emotional data provided by the emotion engine and combines it with attribute information. The emotional data reflects the emotional tendencies and preferences the user has shown in the past and is incorporated into the personality of the virtual character.

[0710] Step 3:

[0711] The server utilizes an AI model to generate a virtual persona based on acquired attribute information and emotional data. This virtual persona is designed to represent a specific market segment and is embodied as a persona with emotional elements.

[0712] Step 4:

[0713] The user sends questions related to a specific market research project from their device to the server. The user inputs the questions through an interface and then proceeds to ask them to a virtual person being researched.

[0714] Step 5:

[0715] The server simulates the received question for a virtual character. Using AI technology, it virtually generates how the virtual character would answer the question. This answer generation also incorporates emotional data, taking into account the user's emotional nuances.

[0716] Step 6:

[0717] The server sends a virtually generated answer to the user's terminal. The user reviews the answer and evaluates whether the question is appropriate.

[0718] Step 7:

[0719] If the user's response indicates that the question needs to be modified, the question will be revised. The results of sentiment data analysis by the sentiment engine can be used to create a more appropriate question structure.

[0720] Step 8:

[0721] The server resimulates the questions, modified as needed, and collects and analyzes the results. This enables the implementation of more appropriate and higher-quality market research.

[0722] (Example 2)

[0723] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0724] Modern market research demands a more detailed understanding of consumer emotions and reactions, but current methods struggle to adequately capture these emotional aspects. Furthermore, the generation of virtual individuals based on traditional registration information often yields unrealistic results. A new methodology is needed to address these problems and obtain more accurate and insightful market research findings.

[0725] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0726] In this invention, the server includes means for an information processing device to acquire attribute information and sentiment data from a registered user database and generate a virtual person based on said attribute information and sentiment data; means for receiving questions directed to the generated virtual person and simulating responses that take sentiment data into account using a generation AI model; means for evaluating the simulated responses and providing an interface for adjusting inappropriate questions; and means for extracting sentiment data from user input data and responses using a sentiment analysis module. This makes it possible to conduct market research using a realistic virtual person that takes emotional aspects into account and their responses.

[0727] An "information processing device" is a device that retrieves attribute information from a database and generates a virtual person based on that information.

[0728] "Attribute information" refers to information that indicates specific characteristics or profiles of registered users.

[0729] A "virtual character" is an imaginary person generated based on acquired attribute information and emotional data.

[0730] "Emotional data" refers to data that indicates a user's emotional state, obtained by analyzing their input and responses.

[0731] A "generative AI model" is a model that uses artificial intelligence to analyze data based on a specific purpose and generate results.

[0732] An "interface" is a function that provides a window or screen for interaction between the user and the system.

[0733] A "sentiment analysis module" is a part of a system that has the function of analyzing user input and responses to extract emotional data.

[0734] This invention is a market research support system that combines an information processing device and an emotion analysis module. The server retrieves user attribute information from a registered user database and generates a virtual persona using an AI model based on that information. At this time, emotion data extracted by the emotion analysis module is combined to construct a more realistic persona.

[0735] The server receives questions entered via an interface from the user's terminal. It then uses AI technology to simulate how a generated virtual character would respond. During this process, emotional data is considered, and emotional nuances are added to the answers, allowing the user to gain deeper insights.

[0736] The sentiment analysis module analyzes user input and responses, extracting sentiment data. The user's device can review the responses and analysis results sent from the server, allowing for re-evaluation and adjustment of questions, thus supporting the development of optimal market strategies. Generative AI models play a central role in this entire process, contributing to complex data analysis and response generation.

[0737] For example, if you want to check the market reaction to a new health food product, the server creates a health-conscious virtual person based on past consumption data. Then, the user inputs a question from their terminal: "How interested are you in this new health food product?" The sentiment analysis module analyzes sentiment data based on this question, and the server, taking that data into consideration, generates an emotional response from the virtual person.

[0738] An example of a prompt might be, "Simulate the emotional response of health-conscious consumers to a new food product." This allows users to obtain research results that reflect emotional aspects and select a more appropriate market approach.

[0739] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0740] Step 1:

[0741] The server retrieves user attribute information from the registrant database. Based on this attribute information as input, the server generates a virtual persona using a generative AI model. Here, emotional data provided by the emotion analysis module is also retrieved as input and added to the virtual persona's attributes to create a more realistic persona. The output of this step is a virtual persona incorporating the emotional data.

[0742] Step 2:

[0743] The user's device inputs a question to the server through an interface. This question becomes the input, and the server receives it. Based on the received question, the server uses a generative AI model to simulate a response from a virtual character. Here, it generates an answer to the input question that takes sentiment data into account. The output of this step is a simulated answer that includes emotional nuances.

[0744] Step 3:

[0745] The emotion analysis module analyzes user input data and extracts emotion data. Based on this input, it generates and outputs data indicating the user's emotional response. This outputted emotion data is used in steps 1 and 2 described above.

[0746] Step 4:

[0747] The server generates the final research findings based on simulated responses and user feedback. During this process, if a question is deemed inappropriate, the user's terminal is notified and an interface is provided allowing for question adjustment. The output of this step is the final market research findings and a set of adjustable questions.

[0748] Step 5:

[0749] Users review the results provided through their devices and formulate market approaches, adjusting questions as needed. The input for this step is the final research findings received from the server, which the user uses to develop concrete action plans. Through this process, users can build strategies that leverage sentiment-aware insights.

[0750] (Application Example 2)

[0751] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0752] In modern market research, accurately understanding consumer emotions and needs is a challenge. However, traditional methods struggle to fully grasp the emotional aspects of users, limiting their ability to understand true consumer needs. Furthermore, there is a lack of mechanisms to utilize emotional data to present consumers with more appropriate products in specific product recommendation scenarios.

[0753] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0754] In this invention, the server includes an information processing device that includes means for acquiring attribute information from a registered user database and generating a virtual person based on said attribute information, means for receiving questions directed at the generated virtual person and simulating answers, and means for analyzing the user's emotional response and making optimized product recommendations based on that. This enables market research and product recommendations that take into account the emotional aspects of consumers.

[0755] An "information processing device" is a general term for hardware or software used to acquire data from users and process and analyze various types of information based on that data.

[0756] A "registrant database" is a data structure or management system that stores user and target attribute information.

[0757] "Attribute information" refers to data that indicates characteristics and status associated with a specific subject or user.

[0758] A "virtual character" is a fictional person created using digital data or AI technology, and is the subject of simulation by users or systems.

[0759] "Emotional response" refers to the emotional reaction or feedback that a user shows to a particular object or situation.

[0760] "Optimized product recommendations" is a process that takes into account the user's needs and emotions, and provides individually tailored product suggestions.

[0761] An "interface" refers to the means of communication and display screens that allow a user to interact directly with a system.

[0762] A specific embodiment of this invention will now be described. The system mainly consists of a server, a user terminal, and an emotion engine. The server is an information processing device with big data processing capabilities, which retrieves attribute information from a registered user database and generates a virtual person using AI technology. Questions directed to the generated virtual person are sent from the user's terminal, which is equipped with an ergonomically designed user interface.

[0763] The user's device first acts as a facilitator, sending questions to the server for the generated virtual character. Upon receiving the questions, the server uses an AI model to simulate the virtual character's emotional responses. In this process, the emotion engine analyzes the user's emotional reactions and provides this data to the entire system. This enables the server to generate highly accurate answers and product recommendations that take emotional data into account.

[0764] For example, in an e-commerce application, when a user browses products through smart glasses, emotional data is analyzed in real time from their gaze and facial expressions. The emotion engine sends this data to a server, which then prioritizes recommending products that match the user's preferences. This process utilizes image processing libraries such as OpenCV and emotion analysis APIs to grasp the user's emotions in real time and provide appropriate product suggestions.

[0765] Specific examples of prompt messages include: "Detect products that make the user smile and recommend similar products from that product category. What other combinations of emotional responses and product categories should be considered?" This allows for proactively suggesting products that evoke joy and interest in the user, ultimately providing a more attractive purchasing experience for consumers.

[0766] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0767] Step 1:

[0768] The user's device sends questions created by the user to the server via the user interface. The input is the user's question content, and the output is transferred to the server. On the interface, data is sent to the server when the send button is clicked.

[0769] Step 2:

[0770] The server retrieves attribute information from the registrant database and uses this data to generate a virtual character using a generative AI model. The input is attribute information from the database, and the output is digital data of the virtual character. Here, an AI-built algorithm operates to generate a realistic and appealing virtual character.

[0771] Step 3:

[0772] The emotion engine analyzes emotional data, such as facial expressions and gaze, transmitted in real time from the user's device. Input is video data from a visual sensor, and the extracted emotional parameters are sent to the server as output. An image processing library is used to quantify the user's emotions.

[0773] Step 4:

[0774] The server simulates generated questions for a virtual character and produces answers based on sentiment data. The input consists of the user's question and sentiment data, and the output is a response that reflects those sentiments. AI models are also utilized here to provide emotionally resonant answers.

[0775] Step 5:

[0776] The server recommends the most suitable products based on the user's emotional data and simulation results. Inputs include emotional data, answers to questions, and past purchase history data, and the output is an optimized product list. Based on this information, the server presents products that reflect the user's tastes and preferences.

[0777] Step 6:

[0778] The user's device receives an optimized product list sent from the server and displays it on the user interface. The input is the product list sent from the server, and the output is the display of those products on the device screen. Specific actions include screen refresh and information updates.

[0779] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0780] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0781] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0782] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0783] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0784] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0785] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0786] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0787] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0788] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0789] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0790] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0791] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0792] 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.

[0793] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0794] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0795] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0796] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0797] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0798] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0799] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0800] The following is further disclosed regarding the embodiments described above.

[0801] (Claim 1)

[0802] The information processing device includes means for obtaining attribute information from a registered user database and generating a virtual person based on said attribute information,

[0803] A means of receiving questions directed at a generated virtual character and simulating the answers,

[0804] A means of providing an interface for evaluating simulated answers and adjusting inappropriate questions,

[0805] A system that includes this.

[0806] (Claim 2)

[0807] The system according to claim 1, which determines the knowledge level using the results of attribute information analysis when setting up the aforementioned virtual person.

[0808] (Claim 3)

[0809] The system according to claim 1, wherein the information processing device re-evaluates the simulated answers and generates a final set of questions.

[0810] "Example 1"

[0811] (Claim 1)

[0812] An information processing device includes means for acquiring attribute information from a storage device and generating a virtual person based on said attribute information,

[0813] A means of receiving survey questions for a generated virtual person and simulating the answers using AI technology,

[0814] Means for providing an input device for evaluating simulated responses and adjusting inappropriate survey items,

[0815] A means for the user to operate a display device to adjust survey items and virtual character settings,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, wherein when setting up the aforementioned virtual person, the system determines the behavioral criteria of the virtual person using the results of attribute information analysis.

[0819] (Claim 3)

[0820] The system according to claim 1, wherein the information processing device re-evaluates the simulated answers and generates a set of questions for final evaluation.

[0821] "Application Example 1"

[0822] (Claim 1)

[0823] The information processing device includes means for obtaining attribute information from a registered user database and generating a virtual person based on said attribute information,

[0824] A means of receiving advertising-related data for a generated virtual character and simulating virtual user reactions,

[0825] A means of providing an interface for evaluating virtual user responses based on virtual characters and adjusting advertising content,

[0826] A means for marketing personnel to input advertising creatives and simulate responses from the target user group,

[0827] A system that includes this.

[0828] (Claim 2)

[0829] The system according to claim 1, which determines the knowledge level and advertising effectiveness prediction using the results of attribute information analysis when setting up the aforementioned virtual person.

[0830] (Claim 3)

[0831] The system according to claim 1, wherein the information processing device re-evaluates a virtual user response to a simulated advertisement and generates an optimal set of advertisement content.

[0832] "Example 2 of combining an emotion engine"

[0833] (Claim 1)

[0834] The information processing device includes means for obtaining attribute information from a registered user database and generating a virtual person based on said attribute information and sentiment data,

[0835] A means of receiving questions directed at a generated virtual character and simulating responses that take sentiment data into account using a generative AI model,

[0836] A means of providing an interface for evaluating simulated answers and adjusting inappropriate questions,

[0837] A means of extracting emotional data from user input data and responses using an emotion analysis module,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, which determines the knowledge level using the results of analysis of attribute information and emotional data when setting up the aforementioned virtual person.

[0841] (Claim 3)

[0842] The system according to claim 1, wherein the information processing device re-evaluates the simulated answers and generates a final set of questions that reflect sentiment data.

[0843] "Application example 2 when combining with an emotional engine"

[0844] (Claim 1)

[0845] The information processing device includes means for obtaining attribute information from a registered user database and generating a virtual person based on said attribute information,

[0846] A means of receiving questions directed at a generated virtual character and simulating the answers,

[0847] A means of analyzing the user's emotional response and providing optimized product recommendations based on that,

[0848] A means of providing an interface for evaluating simulated answers and adjusting inappropriate questions,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, which determines the knowledge level using the results of attribute information analysis when setting up the aforementioned virtual person.

[0852] (Claim 3)

[0853] The system according to claim 1, wherein the information processing device selects similar products when making product recommendations using the user's emotional data. [Explanation of symbols]

[0854] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. The information processing device includes means for obtaining attribute information from a registered user database and generating a virtual person based on said attribute information, A means of receiving questions directed at a generated virtual character and simulating the answers, A means of providing an interface for evaluating simulated answers and adjusting inappropriate questions, A system that includes this.

2. The system according to claim 1, wherein when setting up the aforementioned virtual person, the knowledge level is determined using the results of attribute information analysis.

3. The system according to claim 1, wherein the information processing device re-evaluates the simulated answers and generates a final set of questions.

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