system

Abstract

システムを提供する。【解決手段】生成型人工知能による質問リストの自動生成を行う手段と、前記質問リストに基づいて対象者にオンラインインタビューを実施する手段と、前記オンラインインタビューの結果をリアルタイムでデータベースに記録する手段と、収集したデータを分析してレポートを自動生成する手段と、前記レポートをユーザに提供する手段と、利用者の属性情報に基づき生成型人工知能を用いて関連する情報を提示する手段と、利用者が対象情報を参照している際に質問を挿入し、収集した回答を用いてデータを分析する手段と、分析結果を活動エンジンにフィードバックし、最適化された経験を提供する手段と、を含むシステム。

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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] In modern market research, in order to respond to the diverse needs and behaviors of consumers, a large number of rapid interviews are required. However, the conventional manual method has problems of taking a great deal of time and cost and unevenness in the quality of the survey. In particular, although efficiency is required in each process of interview preparation, implementation, and result analysis, a method for fully automating this has not yet been established.

Means for Solving the Problems

[0005] This invention solves the above problems by providing an automated system using generative artificial intelligence. This system automatically generates question lists using generative artificial intelligence, conducts online interviews with target individuals, records interview results in real time, analyzes collected data and automatically generates reports, and provides reports to users. Furthermore, by including automatic selection of target individuals and scheduling of appointments, and extraction of insights through text mining of voice or text data, it realizes efficient and high-quality market research.

[0006] "Generative artificial intelligence" is an applied technology of artificial intelligence that is capable of generating new information and content based on data.

[0007] "Automatic question list generation" refers to the process by which artificial intelligence automatically creates highly relevant questions according to the research objectives.

[0008] "Conducting online interviews" refers to a research method in which questions are asked to subjects in real time via the internet.

[0009] "Recording in a database" refers to the procedure of systematically saving the results of interviews to prepare for future reference and analysis.

[0010] "Automatic report generation" means that artificial intelligence automatically creates a report summarizing the research findings based on the collected data.

[0011] "Providing to users" means distributing the generated reports and analysis results so that users can easily access them.

[0012] "Selecting participants" refers to identifying and listing appropriate interviewees based on the research objectives.

[0013] "Automatically scheduling appointments" refers to the process of automatically determining and notifying selected participants of the interview date, time, and method.

[0014] "Text mining" refers to an analysis technique that automatically extracts useful information from a large amount of text data.

[0015] "Extract insights" refers to the process of finding useful knowledge and trends from the results of data analysis.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the language used in the following description will be explained.

[0019] In the following embodiments, the 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.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the 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, and the like.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] As an embodiment of the present invention, a market research system using generative artificial intelligence is provided. This system achieves efficient and high-quality market research through a highly automated process involving collaboration among a server, a terminal, and a user.

[0038] The server first receives a survey request from the user and records detailed information about the survey's purpose and target audience. Based on this information, it uses generative artificial intelligence to automatically generate a list of questions necessary for the survey. This list of questions is specialized for the survey's theme and target attributes, supporting highly accurate data collection.

[0039] Next, the server selects target individuals and automatically sets up appointments. This enables efficient interview preparation. The selected individuals conduct online interviews via a terminal. On the terminal, generative artificial intelligence presents questions in real time and records the individuals' responses.

[0040] Interview results are immediately sent to a server and stored in a database. The server then analyzes the collected data and performs text mining using artificial intelligence. This process generates automated reports based on the insights gained.

[0041] The generated reports are provided to users along with visualized data. This allows users to quickly grasp market trends and support strategic decision-making. For example, in a market awareness survey for a new product, multiple interview transcripts set by generative artificial intelligence can be instantly analyzed, providing immediate input for future marketing strategies.

[0042] Thus, the system of the present invention provides a concrete means of innovating market understanding through real-time and highly accurate interview surveys.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user submits a market research request to the server. The request includes details such as the purpose of the research, the target audience, and the research methodology.

[0046] Step 2:

[0047] The server reviews the investigation request received from the user and records its contents in the database. Simultaneously, it activates generative artificial intelligence to automatically generate a list of questions necessary for the investigation.

[0048] Step 3:

[0049] The server selects target individuals based on the generated list of questions. It then creates a candidate list using past history and specified target attributes.

[0050] Step 4:

[0051] The server automatically sends interview appointments to selected participants via email or notification. Participants receive the notification and confirm the interview date and time.

[0052] Step 5:

[0053] When the interview date and time arrives, the device conducts an online interview with the subject via generative artificial intelligence. The AI ​​agent presents questions in real time and collects responses from the subject.

[0054] Step 6:

[0055] The server records interview results from terminals into a database in real time, and the collected data is analyzed using artificial intelligence.

[0056] Step 7:

[0057] The server automatically generates a report based on the analyzed data. The report includes insights extracted through text mining.

[0058] Step 8:

[0059] The server provides the user with the generated report. The user then uses this to evaluate market trends and decide on their next strategic action.

[0060] (Example 1)

[0061] 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."

[0062] The market research process requires seamless integration of question creation, target audience selection, data collection, analysis, and report generation to improve efficiency and accuracy. In particular, there is a need to provide the ability to rapidly process large amounts of data and grasp market trends in real time.

[0063] 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.

[0064] In this invention, the server includes means for automatically generating a list of questions according to the research objective using generative artificial intelligence via an information processing device, means for conducting online interviews with subjects via an information processing terminal, and means for immediately storing the data on a recording medium via a communication line. This enables rapid and highly accurate collection and analysis of data.

[0065] An "information processing device" is a device equipped with the functions of inputting, processing, and outputting data, and in particular, one that uses generative artificial intelligence to generate and analyze data according to specific purposes.

[0066] "Generative artificial intelligence" refers to artificial intelligence technology that has the ability to generate natural language from provided input data, or to make judgments and predictions according to specific tasks.

[0067] An "information processing terminal" is a device that allows users to send and receive data through direct operation, and in online interviews, it is responsible for presenting questions and inputting answers.

[0068] A "communication line" is a physical or wireless path for transmitting data from a sender to a receiver, enabling the real-time transmission of interview results and other data.

[0069] A "recording medium" is a device or physical medium used to store collected data and make it available for retrieval as needed.

[0070] An "analysis processing device" is a computing device that analyzes collected data and extracts useful insights, and performs various information processing tasks, including text mining and report generation.

[0071] A "report" is a document or digital document that summarizes the results of data analysis and provides users with visualized information.

[0072] "Insight" refers to knowledge and understanding gained through data analysis, and is information that helps in grasping specific market trends and consumer behavior.

[0073] The following describes embodiments for carrying out this invention. This system combines an information processing device, an information processing terminal, and a communication line to perform efficient data collection and analysis during the market research process.

[0074] The server uses generative artificial intelligence as its information processing device. Specifically, it uses a text generation model to automatically generate a list of questions based on the research objectives provided by the user. In this process, a platform based on natural language processing technology can be used as an example of a generative AI model. The generated list of questions will be customized according to the characteristics of the target audience and the requirements of the research.

[0075] The user presents a list of questions obtained from the server to the interviewee via an information processing terminal. This terminal is equipped with communication software suitable for conducting interviews and allows for interaction with the interviewee. During the interview, the terminal transmits the interviewee's responses to the server in real time as text or audio. The transmitted data is securely and quickly stored on a recording medium.

[0076] The server analyzes the collected data using text mining techniques via an analytical processing unit. Specifically, Python's natural language processing libraries may be used for data analysis. This data processing allows users to gain important insights into the market.

[0077] Furthermore, the server generates a visualized report based on the analyzed data and provides it to the user. Through this report, the user can understand the awareness and market trends of the subject being investigated and make strategic decisions quickly.

[0078] As a concrete example, when conducting a market awareness survey for a new product, the user inputs a prompt into the server such as, "What measures would be effective in increasing awareness of this product?" Based on this prompt, the generative AI generates interview questions for target customers and provides a report showing the results of the analysis. This system automates the entire market research process, significantly reducing time and effort.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The user inputs the purpose of the market research and target demographic information into the server. This input includes details such as product name, target age group, and region. The server uses this input data to form prompts for a generative AI model, processing the data in the form of "Generate questions to understand awareness among the specified target group."

[0082] Step 2:

[0083] The server invokes a generative AI model to generate a list of questions based on the prompt text. The generation process utilizes natural language processing techniques to output specific and relevant questions optimized for the input conditions. The question list is then stored in a database.

[0084] Step 3:

[0085] The server selects survey participants from its database who meet the criteria specified by the user. This selection process takes into account past participation history and attribute information. Once participants are selected, the server uses an automated system to schedule interview appointments and sends notifications to them.

[0086] Step 4:

[0087] The device initiates an online interview with the research participant at a pre-set date and time. Using video call software, the device sequentially presents generated questions. The participant's responses are collected as text and audio data and transmitted to the server in real time.

[0088] Step 5:

[0089] The server stores the interview data transmitted from the terminals on a recording medium. This data is not sent directly; first, its format is standardized before it is sent to the analysis processing unit. The output from this unit is a dataset in a format suitable for analysis.

[0090] Step 6:

[0091] The analysis processing unit on the server analyzes the collected data using text mining techniques. It utilizes Python libraries to extract important keywords and trends. This process generates insightful information, providing data for report generation as the next output.

[0092] Step 7:

[0093] The server generates and provides users with visualized, interactive reports based on the analysis results. Users can access these reports through a web portal and develop market strategies based on the collected insights. As a result, users can quickly obtain real-time, highly accurate market information and make strategic decisions.

[0094] (Application Example 1)

[0095] 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."

[0096] In market research, efficiently understanding purchasing trends and consumer preferences in real time is difficult, and traditional methods are time-consuming and costly. Furthermore, random questions to users can impair the consumer experience, so there is a need to conduct interviews naturally while providing highly relevant information.

[0097] 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.

[0098] In this invention, the server includes means for automatically generating a list of questions using generative artificial intelligence, means for recording the results of online interviews in a database in real time, and means for presenting relevant information using generative artificial intelligence based on the user's attribute information. This makes it possible to grasp consumers' purchasing behavior and preferences in real time and provide an optimized consumer experience.

[0099] "Generative artificial intelligence" refers to artificial intelligence that possesses the technology to generate new information and content based on training data.

[0100] "Automatic question list generation" is a process that automatically creates interview or survey questions tailored to the specific purpose.

[0101] An "online interview" is a dialogue-based research activity conducted on a digital platform.

[0102] "Recording in real time to a database" refers to the process by which information is instantly saved to a digital recording medium.

[0103] "Presenting relevant information using generative artificial intelligence based on user attribute information" refers to the act of AI selecting and providing highly relevant information while considering the characteristics of each individual user.

[0104] "Understanding consumer purchasing behavior and preferences in real time" refers to the process of instantly understanding how customers choose and purchase products, as well as their preferences.

[0105] "Providing an optimized consumer experience" means improving customer satisfaction by offering the most suitable information and services to individual consumers.

[0106] The system for realizing this invention is built on cooperation between a server, a terminal, and a user.

[0107] The server uses generative artificial intelligence based on detailed attribute information to automatically generate a list of questions to present to the user. This list of questions is tailored to the user's preferences and behavior, and is prepared with the aim of collecting more accurate data.

[0108] The terminal plays a role in presenting users with appropriately generated questions when they use an e-commerce site application, and sending the answers to the server in real time. For example, while a user is searching for a specific product, it might ask, "Please tell us why you are interested in this product," and process the collected data immediately.

[0109] The server also performs text mining using a generative AI model to analyze the received response data. The analysis results extract insights that reveal customer purchasing behavior patterns and are automatically generated as a report. This report is visualized and used by users as a guide when making strategic decisions.

[0110] For example, if a user searches for information on fashion items and it is discovered that they are interested in specific colors or designs, that data can contribute to optimizing future product recommendations and promotional strategies.

[0111] An example of a prompt to input into a generating AI model would be, "Write a program that generates a list of questions for a customer survey based on a theme specified by the user."

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The server receives attribute information from the user. This information includes product categories the user is interested in and their past purchase history. Based on this, prompts are sent to generative artificial intelligence, which generates a list of relevant questions. Through this process, the collected user data is transformed into a concrete output in the form of a question list.

[0115] Step 2:

[0116] The server sends the generated list of questions to the user's device. The device receives this list of questions and integrates it seamlessly into the user's shopping experience, displaying the questions at the appropriate times. For example, it might present questions when the user is browsing products or adding items to their cart.

[0117] Step 3:

[0118] The user responds to questions displayed on the device. The user's answers are sent to the server in real time and recorded in the database. At this time, the user's input data is initially processed on the server side and saved in text format.

[0119] Step 4:

[0120] The server uses a generative AI model based on the collected response data to perform text mining. Specifically, it analyzes the dataset and extracts characteristic patterns and trends. The input response data is then output as analysis results of purchasing behavior and preferences.

[0121] Step 5:

[0122] The server automatically generates a report based on the analysis results and provides it to the user. The report includes visualized data, which the user uses to make future decisions. In this process, the server extracts specific insights from the analysis results and generates output that concludes with information useful for decision support.

[0123] 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.

[0124] As an embodiment of the present invention, a market research system using generative artificial intelligence and an emotion engine is provided. This system aims to obtain deeper consumer insights through automated interviews and emotion analysis, with cooperation among a server, terminal, and user.

[0125] First, the user sends a market research request to the server. This request includes information about the purpose of the research, the target audience, and the research methodology. Based on this information, the server automatically generates a list of research questions using generative artificial intelligence.

[0126] Simultaneously, the server activates the emotion engine and prepares for emotion recognition. Based on the generated list of questions, the server selects targets and automatically schedules interview appointments. Participants join the online interview via a terminal, during which a generative artificial intelligence asks questions in real time, while the emotion engine simultaneously evaluates the participant's emotions.

[0127] The emotion engine identifies the subject's emotions by analyzing audio and video data, and records the results along with the interview data. This data is sent to a server and stored in a database. The server immediately analyzes the collected interview results and emotion data using generative artificial intelligence and automatically generates a report. This report includes emotional responses in the target's responses, enabling the user to gain a deeper understanding.

[0128] To give a specific example, in market research for a new product, by using an emotion engine to analyze consumers' emotional responses to the product (joy, surprise, dissatisfaction, etc.), qualitative insights that cannot be obtained from conventional numerical data alone can be acquired, providing new suggestions for marketing strategies. In this way, the system of the present invention specifically constitutes an implementation means that combines generative artificial intelligence and emotion recognition technology to provide richer market research results.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] Users submit requests to the server to conduct market research. These requests include information such as the attributes of the target audience, the purpose of the research, and the necessary questions.

[0132] Step 2:

[0133] The server registers the survey information received from the user into a database and automatically generates a list of questions using generative artificial intelligence. This list is tailored to the purpose of the survey.

[0134] Step 3:

[0135] The server prepares the emotion engine and selects target individuals. This selection process uses filtering methods based on historical data and specified attributes.

[0136] Step 4:

[0137] The server automatically notifies the subject of the interview appointment via email or message. It then obtains confirmation of consent from the subject.

[0138] Step 5:

[0139] At the scheduled time, the device initiates an online interview with the subject. Generative artificial intelligence asks questions on the device and records the subject's responses in real time.

[0140] Step 6:

[0141] The terminal and emotion engine analyze the interviewee's voice and video during the interview and evaluate their emotions in real time. The emotion data is categorized into multiple emotion categories such as joy, surprise, and sadness.

[0142] Step 7:

[0143] The server collects response data and sentiment data from participants and stores it in a database. This data is analyzed immediately.

[0144] Step 8:

[0145] The server uses generative artificial intelligence to analyze collected data through text mining and gain insights. It integrates sentiment data and text data to automatically generate detailed reports.

[0146] Step 9:

[0147] The server provides the user with the generated report. Based on this, the user can identify areas for improvement in the product or service and plan their next actions.

[0148] (Example 2)

[0149] 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".

[0150] Traditional market research systems primarily relied on quantitative data analysis, making it difficult to capture consumers' emotional responses. Furthermore, the manual nature of the research process was time-consuming and labor-intensive. Moreover, there was no efficient method for automatically providing the deep insights users desired.

[0151] 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.

[0152] In this invention, the server includes means for automatically generating a list of survey questions using generative artificial intelligence, means for selecting relevant subjects and automatically scheduling interviews, and means for asking questions to subjects in real time in an online environment via a terminal. This makes it possible to quickly and efficiently obtain deep insights, including consumers' emotional responses.

[0153] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to automatically generate new information and content based on input data.

[0154] A "question list" is a set of questions generated for a specific purpose and posed to a target audience.

[0155] "Subjects" refer to individuals or groups selected for research or interviews.

[0156] "An interview appointment" refers to a pre-set time and date for conducting an online interview.

[0157] A "server" is a computer system used to process data and provide information over a network.

[0158] A "terminal" is a device used by users or target individuals to connect to a computer network and input or output data.

[0159] An "emotion engine" is a technology that analyzes audio and video data to evaluate and identify the emotional responses of a subject.

[0160] A "database" is an information system that systematically stores data in digital format and efficiently searches and manages it.

[0161] "Insight" refers to the insights and understanding gained through analysis and evaluation, and is particularly useful in marketing and research activities.

[0162] This invention is an information processing system specifically designed for market research. By combining generative artificial intelligence and an emotion recognition engine, it aims to provide deep customer insights that cannot be obtained through conventional research methods.

[0163] First, the user sends detailed information (e.g., target consumer group, survey items) to the server, tailored to the purpose of the market research. This information is sent from the user's device via a web form or a dedicated application. Based on the received information, the server automatically generates an appropriate list of questions using a generative AI model. This generated list of questions serves as the criterion for target selection.

[0164] Next, the server automatically selects target individuals based on profile information stored in the database beforehand. Then, it automatically sets up appointments, including the date and time of the interview, for the selected individuals.

[0165] During the interview, participants join the online interview via a device at a pre-specified date and time. At this time, a generative artificial intelligence presents questions to the participant in real time on the device. Meanwhile, an emotion engine analyzes audio and video data in real time, evaluating and recording the participant's emotional responses. For security reasons, this data is encrypted before being sent to the server and recorded in a database.

[0166] The server analyzes the collected data based on this information and automatically generates a report using a generative AI model. This report includes not only the interview responses but also emotional insights derived from tone of voice and facial expressions. Based on this detailed report, users can refine their marketing strategies and product development.

[0167] For example, when conducting market research for a new product, unexpected emotional responses from consumers, such as "surprise" or "joy," can be qualitatively analyzed, providing clues to explore new directions in product development.

[0168] An example of a prompt to a generative AI model is: "We would like to conduct a market survey on a new smartphone case targeting the following demographic: students aged 15 to 25. Please generate a list of questions that emphasize emotional responses."

[0169] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0170] Step 1:

[0171] The user sends information about the purpose of the market research and the target audience to the server. The input includes the research purpose, questions, and details about the target consumer group. The server receives this information and generates prompts for the AI ​​model. Based on these prompts, the AI ​​model automatically generates a list of questions. The output is the generated list of questions.

[0172] Step 2:

[0173] The server uses the generated list of questions to select suitable candidates from a database containing past survey data and schedules appointments. This process uses the questions and candidate attributes as input. Candidates are identified via queries from the database, and interview details are automatically sent via email or messaging systems. The output is a list of candidates with scheduled appointments.

[0174] Step 3:

[0175] The user prepares to connect online with the interviewee via a terminal to conduct the interview at the specified date and time. The terminal sets up the environment for the online interview and uses generative artificial intelligence to present questions to the interviewee in real time. The input includes a pre-set interview date and time and a list of questions. The questions are presented to the interviewee as appropriate via the interface on the terminal. The output is the interviewee's real-time responses.

[0176] Step 4:

[0177] The device acquires the subject's audio and video data in real time during the interview and performs sentiment analysis using an emotion engine. The input is the subject's audio and video data. The emotion engine calculates emotional indicators such as smiles, surprise, and confusion. The output is sentiment data recorded along with the interview content.

[0178] Step 5:

[0179] The terminal combines the acquired interview audio and emotion data and sends it to the server. The server receives this data and securely stores it in a database. The input is encrypted interview data. After the storage process, the output is a complete, securely stored dataset.

[0180] Step 6:

[0181] The server automatically generates insight reports using a generative AI model based on stored datasets. Inputs include interview and sentiment data. Data analysis techniques extract insights based on individual consumer responses. The output is an insight report, used in developing marketing strategies.

[0182] Step 7:

[0183] The server provides the user with the generated insights report. The report is sent to the user via email or a cloud-based dashboard. The input is the generated report data. The output is the detailed report provided to the user.

[0184] (Application Example 2)

[0185] 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."

[0186] In online market research and e-commerce, accurately and in real time understanding consumer sentiment has made it difficult to gain deeper insights. In particular, the emotions and feedback that consumers experience during e-commerce are difficult to quantify using existing survey methods, making it challenging to incorporate them into effective strategies.

[0187] 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.

[0188] In this invention, the server includes means for automatically generating a list of questions using generative artificial intelligence, means for conducting online interviews with subjects, means for recording the results of the online interviews in a database in real time, means for analyzing the collected data and automatically generating a report, means for emotion recognition to analyze the emotions of users, and means for generating and providing feedback based on the emotion analysis results. This makes it possible to quantify consumer emotions in real time, visualize the consumer experience, and gain strategic insights based on it.

[0189] "Generative artificial intelligence" is a type of artificial intelligence that generates new information from data and automates specific tasks.

[0190] "Automatic question list generation" is a process in which generative artificial intelligence automatically creates questions for a target audience based on given objectives and conditions.

[0191] "Methods for conducting online interviews" refer to methods for presenting generated questions to subjects via a network and collecting their responses.

[0192] "Means of recording in a database" refers to the technology for saving information obtained from online interviews to a database.

[0193] "Methods for automatically generating reports" refer to methods that analyze collected data and create reports in a predetermined format based on the results.

[0194] An "emotion recognition system" is a system for analyzing and evaluating emotions from a subject's voice and video data.

[0195] "Means of generating and providing feedback" refers to the process of communicating specific opinions and areas for improvement to consumers based on sentiment analysis results and interview results.

[0196] The system implementing this invention primarily consists of three components: a server, a terminal, and a user. The server first receives a market research request from the user and, based on that request, automatically generates a list of questions using generative artificial intelligence. This list of questions is used for interviews during electronic transactions.

[0197] The terminal is a device used by the user, and here we will use a smartphone as an example. This terminal conducts an online interview based on a generated list of questions. During the online interview, the user's audio and video data are transmitted to the server via the terminal's camera and microphone.

[0198] The server uses this audio and video data to activate emotion recognition mechanisms and analyze the user's emotions in real time. Specifically, it utilizes the image processing library OpenCV and the speech recognition API Google® Cloud Speech-to-Text. The analyzed emotion data is immediately stored in a database and analyzed together with the interview responses by generative artificial intelligence.

[0199] Based on this analysis, the server automatically generates and provides a report to the user. This report includes information that quantitatively expresses the user's emotional response. For example, it may include an example where a prompt such as "How do you feel about this new product?" is used to evaluate the feelings of consumers when purchasing a new product through online shopping.

[0200] This allows users to quickly obtain the qualitative insights necessary for market research. Furthermore, applying this technology to electronic trading services can be used to improve the consumer experience and enhance services.

[0201] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0202] Step 1:

[0203] The server receives market research requests from users. These requests include information about the research objectives, target audience, and research methodologies. Based on this input data, the server uses generative artificial intelligence to automatically generate a list of questions and outputs that list.

[0204] Step 2:

[0205] The device receives a list of questions sent from the server. When a user participates in an online interview through the device, the device activates its camera and microphone and collects audio and video data while sequentially presenting questions to the interviewee. This data is then sent to the server.

[0206] Step 3:

[0207] The server receives audio and video data transmitted from the terminal and activates the emotion recognition system. Specifically, it uses OpenCV to detect facial expressions in the video data, converts the audio data to text using Google Cloud Speech-to-Text, and then performs emotion analysis. Emotion data is generated and stored in a database.

[0208] Step 4:

[0209] The server performs analysis using interview responses and sentiment data stored in the database. During this analysis process, generative artificial intelligence integrates the data and extracts quantitative and qualitative insights. This automatically generates and outputs a report containing the insights.

[0210] Step 5:

[0211] The server provides the user with the generated report. The report includes details about consumer emotional responses, allowing users to gain deeper market insights. This enables effective feedback even in electronic trading services.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] [Second Embodiment]

[0216] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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".

[0228] As an embodiment of the present invention, a market research system using generative artificial intelligence is provided. This system achieves efficient and high-quality market research through a highly automated process involving collaboration among a server, a terminal, and a user.

[0229] The server first receives a survey request from the user and records detailed information about the survey's purpose and target audience. Based on this information, it uses generative artificial intelligence to automatically generate a list of questions necessary for the survey. This list of questions is specialized for the survey's theme and target attributes, supporting highly accurate data collection.

[0230] Next, the server selects target individuals and automatically sets up appointments. This enables efficient interview preparation. The selected individuals conduct online interviews via a terminal. On the terminal, generative artificial intelligence presents questions in real time and records the individuals' responses.

[0231] Interview results are immediately sent to a server and stored in a database. The server then analyzes the collected data and performs text mining using artificial intelligence. This process generates automated reports based on the insights gained.

[0232] The generated reports are provided to users along with visualized data. This allows users to quickly grasp market trends and support strategic decision-making. For example, in a market awareness survey for a new product, multiple interview transcripts set by generative artificial intelligence can be instantly analyzed, providing immediate input for future marketing strategies.

[0233] Thus, the system of the present invention provides a concrete means of innovating market understanding through real-time and highly accurate interview surveys.

[0234] The following describes the processing flow.

[0235] Step 1:

[0236] The user submits a market research request to the server. The request includes details such as the purpose of the research, the target audience, and the research methodology.

[0237] Step 2:

[0238] The server reviews the investigation request received from the user and records its contents in the database. Simultaneously, it activates generative artificial intelligence to automatically generate a list of questions necessary for the investigation.

[0239] Step 3:

[0240] The server selects target individuals based on the generated list of questions. It then creates a candidate list using past history and specified target attributes.

[0241] Step 4:

[0242] The server automatically sends interview appointments to selected participants via email or notification. Participants receive the notification and confirm the interview date and time.

[0243] Step 5:

[0244] When the interview date and time arrives, the device conducts an online interview with the subject via generative artificial intelligence. The AI ​​agent presents questions in real time and collects responses from the subject.

[0245] Step 6:

[0246] The server records interview results from terminals into a database in real time, and the collected data is analyzed using artificial intelligence.

[0247] Step 7:

[0248] The server automatically generates a report based on the analyzed data. The report includes insights extracted through text mining.

[0249] Step 8:

[0250] The server provides the user with the generated report. The user then uses this to evaluate market trends and decide on their next strategic action.

[0251] (Example 1)

[0252] 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."

[0253] The market research process requires seamless integration of question creation, target audience selection, data collection, analysis, and report generation to improve efficiency and accuracy. In particular, there is a need to provide the ability to rapidly process large amounts of data and grasp market trends in real time.

[0254] 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.

[0255] In this invention, the server includes means for automatically generating a list of questions according to the research objective using generative artificial intelligence via an information processing device, means for conducting online interviews with subjects via an information processing terminal, and means for immediately storing the data on a recording medium via a communication line. This enables rapid and highly accurate collection and analysis of data.

[0256] An "information processing device" is a device equipped with the functions of inputting, processing, and outputting data, and in particular, one that uses generative artificial intelligence to generate and analyze data according to specific purposes.

[0257] "Generative artificial intelligence" refers to artificial intelligence technology that has the ability to generate natural language from provided input data, or to make judgments and predictions according to specific tasks.

[0258] An "information processing terminal" is a device that allows users to send and receive data through direct operation, and in online interviews, it is responsible for presenting questions and inputting answers.

[0259] A "communication line" is a physical or wireless path for transmitting data from a sender to a receiver, enabling the real-time transmission of interview results and other data.

[0260] A "recording medium" is a device or physical medium used to store collected data and make it available for retrieval as needed.

[0261] An "analysis processing device" is a computing device that analyzes collected data and extracts useful insights, and performs various information processing tasks, including text mining and report generation.

[0262] A "report" is a document or digital document that summarizes the results of data analysis and provides users with visualized information.

[0263] "Insight" refers to knowledge and understanding gained through data analysis, and is information that helps in grasping specific market trends and consumer behavior.

[0264] The following describes embodiments for carrying out this invention. This system combines an information processing device, an information processing terminal, and a communication line to perform efficient data collection and analysis during the market research process.

[0265] The server uses generative artificial intelligence as its information processing device. Specifically, it uses a text generation model to automatically generate a list of questions based on the research objectives provided by the user. In this process, a platform based on natural language processing technology can be used as an example of a generative AI model. The generated list of questions will be customized according to the characteristics of the target audience and the requirements of the research.

[0266] The user presents a list of questions obtained from the server to the interviewee via an information processing terminal. This terminal is equipped with communication software suitable for conducting interviews and allows for interaction with the interviewee. During the interview, the terminal transmits the interviewee's responses to the server in real time as text or audio. The transmitted data is securely and quickly stored on a recording medium.

[0267] The server analyzes the collected data using text mining techniques via an analytical processing unit. Specifically, Python's natural language processing libraries may be used for data analysis. This data processing allows users to gain important insights into the market.

[0268] Furthermore, the server generates a visualized report based on the analyzed data and provides it to the user. Through this report, the user can understand the awareness and market trends of the subject being investigated and make strategic decisions quickly.

[0269] As a concrete example, when conducting a market awareness survey for a new product, the user inputs a prompt into the server such as, "What measures would be effective in increasing awareness of this product?" Based on this prompt, the generative AI generates interview questions for target customers and provides a report showing the results of the analysis. This system automates the entire market research process, significantly reducing time and effort.

[0270] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0271] Step 1:

[0272] The user inputs the purpose of the market research and target demographic information into the server. This input includes details such as product name, target age group, and region. The server uses this input data to form prompts for a generative AI model, processing the data in the form of "Generate questions to understand awareness among the specified target group."

[0273] Step 2:

[0274] The server invokes a generative AI model to generate a list of questions based on the prompt text. The generation process utilizes natural language processing techniques to output specific and relevant questions optimized for the input conditions. The question list is then stored in a database.

[0275] Step 3:

[0276] The server selects survey participants from its database who meet the criteria specified by the user. This selection process takes into account past participation history and attribute information. Once participants are selected, the server uses an automated system to schedule interview appointments and sends notifications to them.

[0277] Step 4:

[0278] The device initiates an online interview with the research participant at a pre-set date and time. Using video call software, the device sequentially presents generated questions. The participant's responses are collected as text and audio data and transmitted to the server in real time.

[0279] Step 5:

[0280] The server stores the interview data transmitted from the terminals on a recording medium. This data is not sent directly; first, its format is standardized before it is sent to the analysis processing unit. The output from this unit is a dataset in a format suitable for analysis.

[0281] Step 6:

[0282] The analysis processing unit on the server analyzes the collected data using text mining techniques. It utilizes Python libraries to extract important keywords and trends. This process generates insightful information, providing data for report generation as the next output.

[0283] Step 7:

[0284] The server generates a visualized interactive report based on the analysis results and provides it to the user. The user can access this report through the web portal and formulate a market strategy based on the insights collected. As a result, the user can quickly obtain real-time and highly accurate market information and make strategic decisions.

[0285] (Application Example 1)

[0286] 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".

[0287] In market research, it is difficult to efficiently grasp purchase trends and consumer preferences in real time, and there is a problem that the conventional methods require a great deal of time and cost. In addition, since random questions to users may damage the consumer experience, it is required to conduct interviews naturally while providing highly relevant information.

[0288] 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.

[0289] In this invention, the server includes means for automatically generating a question list by generative artificial intelligence, means for recording the results of an online interview in a database in real time, and means for presenting relevant information using generative artificial intelligence based on the attribute information of the user. Thereby, it becomes possible to grasp the purchase behavior and preferences of consumers in real time and provide an optimized consumer experience.

[0290] "Generative artificial intelligence" is artificial intelligence having a technology for generating new information and content based on learning data.

[0291] "Automatic generation of a question list" is a process of automatically creating questions for interviews and surveys according to the purpose.

[0292] An "online interview" is a dialogue-based research activity conducted on a digital platform.

[0293] "Recording in real time to a database" refers to the process by which information is instantly saved to a digital recording medium.

[0294] "Presenting relevant information using generative artificial intelligence based on user attribute information" refers to the act of AI selecting and providing highly relevant information while considering the characteristics of each individual user.

[0295] "Understanding consumer purchasing behavior and preferences in real time" refers to the process of instantly understanding how customers choose and purchase products, as well as their preferences.

[0296] "Providing an optimized consumer experience" means improving customer satisfaction by offering the most suitable information and services to individual consumers.

[0297] The system for realizing this invention is built on cooperation between a server, a terminal, and a user.

[0298] The server uses generative artificial intelligence based on detailed attribute information to automatically generate a list of questions to present to the user. This list of questions is tailored to the user's preferences and behavior, and is prepared with the aim of collecting more accurate data.

[0299] The terminal plays a role in presenting users with appropriately generated questions when they use an e-commerce site application, and sending the answers to the server in real time. For example, while a user is searching for a specific product, it might ask, "Please tell us why you are interested in this product," and process the collected data immediately.

[0300] The server also performs text mining using a generative AI model for analyzing the received response data. The analysis results extract insights that clarify the customer's purchase behavior patterns and are automatically generated as a report. This report is visualized and utilized as a guideline for users to make strategic decisions.

[0301] As a specific example, if it is found that a certain user is interested in a specific color or design while searching for fashion product information, that data contributes to future product recommendations and optimization of promotion strategies.

[0302] Examples of prompt sentences to input into the generative AI model include "Please write a program to generate a list of questions for customer surveys based on the theme specified by the user."

[0303] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0304] Step 1:

[0305] The server receives attribute information from the user. This information includes the product categories the user is interested in and past purchase histories. Based on this, a prompt sentence is sent to the generative artificial intelligence to generate a relevant list of questions. Through this process, the collected user data is converted into a specific output, namely the list of questions.

[0306] Step 2:

[0307] The server sends the generated list of questions to the terminal operated by the user. The terminal receives this list of questions, naturally integrates it into the user's shopping experience, and displays the questions at an appropriate timing. For example, when the user is browsing products or adding products to the cart, specific actions are taken to present the questions.

[0308] Step 3:

[0309] The user responds to questions displayed on the device. The user's answers are sent to the server in real time and recorded in the database. At this time, the user's input data is initially processed on the server side and saved in text format.

[0310] Step 4:

[0311] The server uses a generative AI model based on the collected response data to perform text mining. Specifically, it analyzes the dataset and extracts characteristic patterns and trends. The input response data is then output as analysis results of purchasing behavior and preferences.

[0312] Step 5:

[0313] The server automatically generates a report based on the analysis results and provides it to the user. The report includes visualized data, which the user uses to make future decisions. In this process, the server extracts specific insights from the analysis results and generates output that concludes with information useful for decision support.

[0314] 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.

[0315] As an embodiment of the present invention, a market research system using generative artificial intelligence and an emotion engine is provided. This system aims to obtain deeper consumer insights through automated interviews and emotion analysis, with cooperation among a server, terminal, and user.

[0316] First, the user sends a market research request to the server. This request includes information about the purpose of the research, the target audience, and the research methodology. Based on this information, the server automatically generates a list of research questions using generative artificial intelligence.

[0317] Simultaneously, the server activates the emotion engine and prepares for emotion recognition. Based on the generated list of questions, the server selects targets and automatically schedules interview appointments. Participants join the online interview via a terminal, during which a generative artificial intelligence asks questions in real time, while the emotion engine simultaneously evaluates the participant's emotions.

[0318] The emotion engine identifies the subject's emotions by analyzing audio and video data, and records the results along with the interview data. This data is sent to a server and stored in a database. The server immediately analyzes the collected interview results and emotion data using generative artificial intelligence and automatically generates a report. This report includes emotional responses in the target's responses, enabling the user to gain a deeper understanding.

[0319] To give a specific example, in market research for a new product, by using an emotion engine to analyze consumers' emotional responses to the product (joy, surprise, dissatisfaction, etc.), qualitative insights that cannot be obtained from conventional numerical data alone can be acquired, providing new suggestions for marketing strategies. In this way, the system of the present invention specifically constitutes an implementation means that combines generative artificial intelligence and emotion recognition technology to provide richer market research results.

[0320] The following describes the processing flow.

[0321] Step 1:

[0322] Users submit requests to the server to conduct market research. These requests include information such as the attributes of the target audience, the purpose of the research, and the necessary questions.

[0323] Step 2:

[0324] The server registers the survey information received from the user into a database and automatically generates a list of questions using generative artificial intelligence. This list is tailored to the purpose of the survey.

[0325] Step 3:

[0326] The server prepares the emotion engine and selects target individuals. This selection process uses filtering methods based on historical data and specified attributes.

[0327] Step 4:

[0328] The server automatically notifies the subject of the interview appointment via email or message. It then obtains confirmation of consent from the subject.

[0329] Step 5:

[0330] At the scheduled time, the device initiates an online interview with the subject. Generative artificial intelligence asks questions on the device and records the subject's responses in real time.

[0331] Step 6:

[0332] The terminal and emotion engine analyze the interviewee's voice and video during the interview and evaluate their emotions in real time. The emotion data is categorized into multiple emotion categories such as joy, surprise, and sadness.

[0333] Step 7:

[0334] The server collects response data and sentiment data from participants and stores it in a database. This data is analyzed immediately.

[0335] Step 8:

[0336] The server uses generative artificial intelligence to analyze collected data through text mining and gain insights. It integrates sentiment data and text data to automatically generate detailed reports.

[0337] Step 9:

[0338] The server provides the user with the generated report. Based on this, the user can identify areas for improvement in the product or service and plan their next actions.

[0339] (Example 2)

[0340] 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".

[0341] Traditional market research systems primarily relied on quantitative data analysis, making it difficult to capture consumers' emotional responses. Furthermore, the manual nature of the research process was time-consuming and labor-intensive. Moreover, there was no efficient method for automatically providing the deep insights users desired.

[0342] 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.

[0343] In this invention, the server includes means for automatically generating a list of survey questions using generative artificial intelligence, means for selecting relevant subjects and automatically scheduling interviews, and means for asking questions to subjects in real time in an online environment via a terminal. This makes it possible to quickly and efficiently obtain deep insights, including consumers' emotional responses.

[0344] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to automatically generate new information and content based on input data.

[0345] A "question list" is a set of questions generated for a specific purpose and posed to a target audience.

[0346] "Subjects" refer to individuals or groups selected for research or interviews.

[0347] "An interview appointment" refers to a pre-set time and date for conducting an online interview.

[0348] A "server" is a computer system used to process data and provide information over a network.

[0349] A "terminal" is a device used by users or target individuals to connect to a computer network and input or output data.

[0350] An "emotion engine" is a technology that analyzes audio and video data to evaluate and identify the emotional responses of a subject.

[0351] A "database" is an information system that systematically stores data in digital format and efficiently searches and manages it.

[0352] "Insight" refers to the insights and understanding gained through analysis and evaluation, and is particularly useful in marketing and research activities.

[0353] This invention is an information processing system specifically designed for market research. By combining generative artificial intelligence and an emotion recognition engine, it aims to provide deep customer insights that cannot be obtained through conventional research methods.

[0354] First, the user sends detailed information (e.g., target consumer group, survey items) to the server, tailored to the purpose of the market research. This information is sent from the user's device via a web form or a dedicated application. Based on the received information, the server automatically generates an appropriate list of questions using a generative AI model. This generated list of questions serves as the criterion for target selection.

[0355] Next, the server automatically selects target individuals based on profile information stored in the database beforehand. Then, it automatically sets up appointments, including the date and time of the interview, for the selected individuals.

[0356] During the interview, participants join the online interview via a device at a pre-specified date and time. At this time, a generative artificial intelligence presents questions to the participant in real time on the device. Meanwhile, an emotion engine analyzes audio and video data in real time, evaluating and recording the participant's emotional responses. For security reasons, this data is encrypted before being sent to the server and recorded in a database.

[0357] The server analyzes the collected data based on this information and automatically generates a report using a generative AI model. This report includes not only the interview responses but also emotional insights derived from tone of voice and facial expressions. Based on this detailed report, users can refine their marketing strategies and product development.

[0358] For example, when conducting market research for a new product, unexpected emotional responses from consumers, such as "surprise" or "joy," can be qualitatively analyzed, providing clues to explore new directions in product development.

[0359] An example of a prompt to a generative AI model is: "We would like to conduct a market survey on a new smartphone case targeting the following demographic: students aged 15 to 25. Please generate a list of questions that emphasize emotional responses."

[0360] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0361] Step 1:

[0362] The user sends information about the purpose of the market research and the target audience to the server. The input includes the research purpose, questions, and details about the target consumer group. The server receives this information and generates prompts for the AI ​​model. Based on these prompts, the AI ​​model automatically generates a list of questions. The output is the generated list of questions.

[0363] Step 2:

[0364] The server uses the generated list of questions to select suitable candidates from a database containing past survey data and schedules appointments. This process uses the questions and candidate attributes as input. Candidates are identified via queries from the database, and interview details are automatically sent via email or messaging systems. The output is a list of candidates with scheduled appointments.

[0365] Step 3:

[0366] The user prepares to connect online with the interviewee via a terminal to conduct the interview at the specified date and time. The terminal sets up the environment for the online interview and uses generative artificial intelligence to present questions to the interviewee in real time. The input includes a pre-set interview date and time and a list of questions. The questions are presented to the interviewee as appropriate via the interface on the terminal. The output is the interviewee's real-time responses.

[0367] Step 4:

[0368] The device acquires the subject's audio and video data in real time during the interview and performs sentiment analysis using an emotion engine. The input is the subject's audio and video data. The emotion engine calculates emotional indicators such as smiles, surprise, and confusion. The output is sentiment data recorded along with the interview content.

[0369] Step 5:

[0370] The terminal combines the acquired interview audio and emotion data and sends it to the server. The server receives this data and securely stores it in a database. The input is encrypted interview data. After the storage process, the output is a complete, securely stored dataset.

[0371] Step 6:

[0372] The server automatically generates insight reports using a generative AI model based on stored datasets. Inputs include interview and sentiment data. Data analysis techniques extract insights based on individual consumer responses. The output is an insight report, used in developing marketing strategies.

[0373] Step 7:

[0374] The server provides the user with the generated insights report. The report is sent to the user via email or a cloud-based dashboard. The input is the generated report data. The output is the detailed report provided to the user.

[0375] (Application Example 2)

[0376] 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."

[0377] In online market research and e-commerce, accurately and in real time understanding consumer sentiment has made it difficult to gain deeper insights. In particular, the emotions and feedback that consumers experience during e-commerce are difficult to quantify using existing survey methods, making it challenging to incorporate them into effective strategies.

[0378] 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.

[0379] In this invention, the server includes means for automatically generating a list of questions using generative artificial intelligence, means for conducting online interviews with subjects, means for recording the results of the online interviews in a database in real time, means for analyzing the collected data and automatically generating a report, means for emotion recognition to analyze the emotions of users, and means for generating and providing feedback based on the emotion analysis results. This makes it possible to quantify consumer emotions in real time, visualize the consumer experience, and gain strategic insights based on it.

[0380] "Generative artificial intelligence" is a type of artificial intelligence that generates new information from data and automates specific tasks.

[0381] "Automatic question list generation" is a process in which generative artificial intelligence automatically creates questions for a target audience based on given objectives and conditions.

[0382] "Methods for conducting online interviews" refer to methods for presenting generated questions to subjects via a network and collecting their responses.

[0383] "Means of recording in a database" refers to the technology for saving information obtained from online interviews to a database.

[0384] "Methods for automatically generating reports" refer to methods that analyze collected data and create reports in a predetermined format based on the results.

[0385] An "emotion recognition system" is a system for analyzing and evaluating emotions from a subject's voice and video data.

[0386] "Means of generating and providing feedback" refers to the process of communicating specific opinions and areas for improvement to consumers based on sentiment analysis results and interview results.

[0387] The system implementing this invention primarily consists of three components: a server, a terminal, and a user. The server first receives a market research request from the user and, based on that request, automatically generates a list of questions using generative artificial intelligence. This list of questions is used for interviews during electronic transactions.

[0388] The terminal is a device used by the user, and here we will use a smartphone as an example. This terminal conducts an online interview based on a generated list of questions. During the online interview, the user's audio and video data are transmitted to the server via the terminal's camera and microphone.

[0389] The server uses this audio and video data to activate emotion recognition mechanisms and analyze the user's emotions in real time. Specifically, it utilizes the image processing library OpenCV and the speech recognition API Google Cloud Speech-to-Text. The analyzed emotion data is immediately stored in a database and analyzed together with the interview responses by generative artificial intelligence.

[0390] Based on this analysis, the server automatically generates and provides a report to the user. This report includes information that quantitatively expresses the user's emotional response. For example, it may include an example where a prompt such as "How do you feel about this new product?" is used to evaluate the feelings of consumers when purchasing a new product through online shopping.

[0391] This allows users to quickly obtain the qualitative insights necessary for market research. Furthermore, applying this technology to electronic trading services can be used to improve the consumer experience and enhance services.

[0392] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0393] Step 1:

[0394] The server receives market research requests from users. These requests include information about the research objectives, target audience, and research methodologies. Based on this input data, the server uses generative artificial intelligence to automatically generate a list of questions and outputs that list.

[0395] Step 2:

[0396] The device receives a list of questions sent from the server. When a user participates in an online interview through the device, the device activates its camera and microphone and collects audio and video data while sequentially presenting questions to the interviewee. This data is then sent to the server.

[0397] Step 3:

[0398] The server receives audio and video data transmitted from the terminal and activates the emotion recognition system. Specifically, it uses OpenCV to detect facial expressions in the video data, converts the audio data to text using Google Cloud Speech-to-Text, and then performs emotion analysis. Emotion data is generated and stored in a database.

[0399] Step 4:

[0400] The server performs analysis using interview responses and sentiment data stored in the database. During this analysis process, generative artificial intelligence integrates the data and extracts quantitative and qualitative insights. This automatically generates and outputs a report containing the insights.

[0401] Step 5:

[0402] The server provides the user with the generated report. The report includes details about consumer emotional responses, allowing users to gain deeper market insights. This enables effective feedback even in electronic trading services.

[0403] 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.

[0404] 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.

[0405] 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.

[0406] [Third Embodiment]

[0407] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0408] 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.

[0409] 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).

[0410] 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.

[0411] 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.

[0412] 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).

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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".

[0419] As an embodiment of the present invention, a market research system using generative artificial intelligence is provided. This system achieves efficient and high-quality market research through a highly automated process involving collaboration among a server, a terminal, and a user.

[0420] The server first receives a survey request from the user and records detailed information about the survey's purpose and target audience. Based on this information, it uses generative artificial intelligence to automatically generate a list of questions necessary for the survey. This list of questions is specialized for the survey's theme and target attributes, supporting highly accurate data collection.

[0421] Next, the server selects target individuals and automatically sets up appointments. This enables efficient interview preparation. The selected individuals conduct online interviews via a terminal. On the terminal, generative artificial intelligence presents questions in real time and records the individuals' responses.

[0422] Interview results are immediately sent to a server and stored in a database. The server then analyzes the collected data and performs text mining using artificial intelligence. This process generates automated reports based on the insights gained.

[0423] The generated reports are provided to users along with visualized data. This allows users to quickly grasp market trends and support strategic decision-making. For example, in a market awareness survey for a new product, multiple interview transcripts set by generative artificial intelligence can be instantly analyzed, providing immediate input for future marketing strategies.

[0424] Thus, the system of the present invention provides a concrete means of innovating market understanding through real-time and highly accurate interview surveys.

[0425] The following describes the processing flow.

[0426] Step 1:

[0427] The user submits a market research request to the server. The request includes details such as the purpose of the research, the target audience, and the research methodology.

[0428] Step 2:

[0429] The server reviews the investigation request received from the user and records its contents in the database. Simultaneously, it activates generative artificial intelligence to automatically generate a list of questions necessary for the investigation.

[0430] Step 3:

[0431] The server selects target individuals based on the generated list of questions. It then creates a candidate list using past history and specified target attributes.

[0432] Step 4:

[0433] The server automatically sends interview appointments to selected participants via email or notification. Participants receive the notification and confirm the interview date and time.

[0434] Step 5:

[0435] When the interview date and time arrives, the device conducts an online interview with the subject via generative artificial intelligence. The AI ​​agent presents questions in real time and collects responses from the subject.

[0436] Step 6:

[0437] The server records interview results from terminals into a database in real time, and the collected data is analyzed using artificial intelligence.

[0438] Step 7:

[0439] The server automatically generates a report based on the analyzed data. The report includes insights extracted through text mining.

[0440] Step 8:

[0441] The server provides the user with the generated report. The user then uses this to evaluate market trends and decide on their next strategic action.

[0442] (Example 1)

[0443] 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."

[0444] The market research process requires seamless integration of question creation, target audience selection, data collection, analysis, and report generation to improve efficiency and accuracy. In particular, there is a need to provide the ability to rapidly process large amounts of data and grasp market trends in real time.

[0445] 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.

[0446] In this invention, the server includes means for automatically generating a list of questions according to the research objective using generative artificial intelligence via an information processing device, means for conducting online interviews with subjects via an information processing terminal, and means for immediately storing the data on a recording medium via a communication line. This enables rapid and highly accurate collection and analysis of data.

[0447] An "information processing device" is a device equipped with the functions of inputting, processing, and outputting data, and in particular, one that uses generative artificial intelligence to generate and analyze data according to specific purposes.

[0448] "Generative artificial intelligence" refers to artificial intelligence technology that has the ability to generate natural language from provided input data, or to make judgments and predictions according to specific tasks.

[0449] An "information processing terminal" is a device that allows users to send and receive data through direct operation, and in online interviews, it is responsible for presenting questions and inputting answers.

[0450] A "communication line" is a physical or wireless path for transmitting data from a sender to a receiver, enabling the real-time transmission of interview results and other data.

[0451] A "recording medium" is a device or physical medium used to store collected data and make it available for retrieval as needed.

[0452] An "analysis processing device" is a computing device that analyzes collected data and extracts useful insights, and performs various information processing tasks, including text mining and report generation.

[0453] A "report" is a document or digital document that summarizes the results of data analysis and provides users with visualized information.

[0454] "Insight" refers to knowledge and understanding gained through data analysis, and is information that helps in grasping specific market trends and consumer behavior.

[0455] The following describes embodiments for carrying out this invention. This system combines an information processing device, an information processing terminal, and a communication line to perform efficient data collection and analysis during the market research process.

[0456] The server uses generative artificial intelligence as its information processing device. Specifically, it uses a text generation model to automatically generate a list of questions based on the research objectives provided by the user. In this process, a platform based on natural language processing technology can be used as an example of a generative AI model. The generated list of questions will be customized according to the characteristics of the target audience and the requirements of the research.

[0457] The user presents a list of questions obtained from the server to the interviewee via an information processing terminal. This terminal is equipped with communication software suitable for conducting interviews and allows for interaction with the interviewee. During the interview, the terminal transmits the interviewee's responses to the server in real time as text or audio. The transmitted data is securely and quickly stored on a recording medium.

[0458] The server analyzes the collected data using text mining techniques via an analytical processing unit. Specifically, Python's natural language processing libraries may be used for data analysis. This data processing allows users to gain important insights into the market.

[0459] Furthermore, the server generates a visualized report based on the analyzed data and provides it to the user. Through this report, the user can understand the awareness and market trends of the subject being investigated and make strategic decisions quickly.

[0460] As a concrete example, when conducting a market awareness survey for a new product, the user inputs a prompt into the server such as, "What measures would be effective in increasing awareness of this product?" Based on this prompt, the generative AI generates interview questions for target customers and provides a report showing the results of the analysis. This system automates the entire market research process, significantly reducing time and effort.

[0461] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0462] Step 1:

[0463] The user inputs the purpose of the market research and target demographic information into the server. This input includes details such as product name, target age group, and region. The server uses this input data to form prompts for a generative AI model, processing the data in the form of "Generate questions to understand awareness among the specified target group."

[0464] Step 2:

[0465] The server invokes a generative AI model to generate a list of questions based on the prompt text. The generation process utilizes natural language processing techniques to output specific and relevant questions optimized for the input conditions. The question list is then stored in a database.

[0466] Step 3:

[0467] The server selects survey participants from its database who meet the criteria specified by the user. This selection process takes into account past participation history and attribute information. Once participants are selected, the server uses an automated system to schedule interview appointments and sends notifications to them.

[0468] Step 4:

[0469] The device initiates an online interview with the research participant at a pre-set date and time. Using video call software, the device sequentially presents generated questions. The participant's responses are collected as text and audio data and transmitted to the server in real time.

[0470] Step 5:

[0471] The server stores the interview data transmitted from the terminals on a recording medium. This data is not sent directly; first, its format is standardized before it is sent to the analysis processing unit. The output from this unit is a dataset in a format suitable for analysis.

[0472] Step 6:

[0473] The analysis processing unit on the server analyzes the collected data using text mining techniques. It utilizes Python libraries to extract important keywords and trends. This process generates insightful information, providing data for report generation as the next output.

[0474] Step 7:

[0475] The server generates and provides users with visualized, interactive reports based on the analysis results. Users can access these reports through a web portal and develop market strategies based on the collected insights. As a result, users can quickly obtain real-time, highly accurate market information and make strategic decisions.

[0476] (Application Example 1)

[0477] 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."

[0478] In market research, efficiently understanding purchasing trends and consumer preferences in real time is difficult, and traditional methods are time-consuming and costly. Furthermore, random questions to users can impair the consumer experience, so there is a need to conduct interviews naturally while providing highly relevant information.

[0479] 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.

[0480] In this invention, the server includes means for automatically generating a list of questions using generative artificial intelligence, means for recording the results of online interviews in a database in real time, and means for presenting relevant information using generative artificial intelligence based on the user's attribute information. This makes it possible to grasp consumers' purchasing behavior and preferences in real time and provide an optimized consumer experience.

[0481] "Generative artificial intelligence" refers to artificial intelligence that possesses the technology to generate new information and content based on training data.

[0482] "Automatic question list generation" is a process that automatically creates interview or survey questions tailored to the specific purpose.

[0483] An "online interview" is a dialogue-based research activity conducted on a digital platform.

[0484] "Recording in real time to a database" refers to the process by which information is instantly saved to a digital recording medium.

[0485] "Presenting relevant information using generative artificial intelligence based on user attribute information" refers to the act of AI selecting and providing highly relevant information while considering the characteristics of each individual user.

[0486] "Understanding consumer purchasing behavior and preferences in real time" refers to the process of instantly understanding how customers choose and purchase products, as well as their preferences.

[0487] "Providing an optimized consumer experience" means improving customer satisfaction by offering the most suitable information and services to individual consumers.

[0488] The system for realizing this invention is built on cooperation between a server, a terminal, and a user.

[0489] The server uses generative artificial intelligence based on detailed attribute information to automatically generate a list of questions to present to the user. This list of questions is tailored to the user's preferences and behavior, and is prepared with the aim of collecting more accurate data.

[0490] The terminal plays a role in presenting users with appropriately generated questions when they use an e-commerce site application, and sending the answers to the server in real time. For example, while a user is searching for a specific product, it might ask, "Please tell us why you are interested in this product," and process the collected data immediately.

[0491] The server also performs text mining using a generative AI model to analyze the received response data. The analysis results extract insights that reveal customer purchasing behavior patterns and are automatically generated as a report. This report is visualized and used by users as a guide when making strategic decisions.

[0492] For example, if a user searches for information on fashion items and it is discovered that they are interested in specific colors or designs, that data can contribute to optimizing future product recommendations and promotional strategies.

[0493] An example of a prompt to input into a generating AI model would be, "Write a program that generates a list of questions for a customer survey based on a theme specified by the user."

[0494] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0495] Step 1:

[0496] The server receives attribute information from the user. This information includes product categories the user is interested in and their past purchase history. Based on this, prompts are sent to generative artificial intelligence, which generates a list of relevant questions. Through this process, the collected user data is transformed into a concrete output in the form of a question list.

[0497] Step 2:

[0498] The server sends the generated list of questions to the user's device. The device receives this list of questions and integrates it seamlessly into the user's shopping experience, displaying the questions at the appropriate times. For example, it might present questions when the user is browsing products or adding items to their cart.

[0499] Step 3:

[0500] The user responds to questions displayed on the device. The user's answers are sent to the server in real time and recorded in the database. At this time, the user's input data is initially processed on the server side and saved in text format.

[0501] Step 4:

[0502] The server uses a generative AI model based on the collected response data to perform text mining. Specifically, it analyzes the dataset and extracts characteristic patterns and trends. The input response data is then output as analysis results of purchasing behavior and preferences.

[0503] Step 5:

[0504] The server automatically generates a report based on the analysis results and provides it to the user. The report includes visualized data, which the user uses to make future decisions. In this process, the server extracts specific insights from the analysis results and generates output that concludes with information useful for decision support.

[0505] 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.

[0506] As an embodiment of the present invention, a market research system using generative artificial intelligence and an emotion engine is provided. This system aims to obtain deeper consumer insights through automated interviews and emotion analysis, with cooperation among a server, terminal, and user.

[0507] First, the user sends a market research request to the server. This request includes information about the purpose of the research, the target audience, and the research methodology. Based on this information, the server automatically generates a list of research questions using generative artificial intelligence.

[0508] Simultaneously, the server activates the emotion engine and prepares for emotion recognition. Based on the generated list of questions, the server selects targets and automatically schedules interview appointments. Participants join the online interview via a terminal, during which a generative artificial intelligence asks questions in real time, while the emotion engine simultaneously evaluates the participant's emotions.

[0509] The emotion engine identifies the subject's emotions by analyzing audio and video data, and records the results along with the interview data. This data is sent to a server and stored in a database. The server immediately analyzes the collected interview results and emotion data using generative artificial intelligence and automatically generates a report. This report includes emotional responses in the target's responses, enabling the user to gain a deeper understanding.

[0510] To give a specific example, in market research for a new product, by using an emotion engine to analyze consumers' emotional responses to the product (joy, surprise, dissatisfaction, etc.), qualitative insights that cannot be obtained from conventional numerical data alone can be acquired, providing new suggestions for marketing strategies. In this way, the system of the present invention specifically constitutes an implementation means that combines generative artificial intelligence and emotion recognition technology to provide richer market research results.

[0511] The following describes the processing flow.

[0512] Step 1:

[0513] Users submit requests to the server to conduct market research. These requests include information such as the attributes of the target audience, the purpose of the research, and the necessary questions.

[0514] Step 2:

[0515] The server registers the survey information received from the user into a database and automatically generates a list of questions using generative artificial intelligence. This list is tailored to the purpose of the survey.

[0516] Step 3:

[0517] The server prepares the emotion engine and selects target individuals. This selection process uses filtering methods based on historical data and specified attributes.

[0518] Step 4:

[0519] The server automatically notifies the subject of the interview appointment via email or message. It then obtains confirmation of consent from the subject.

[0520] Step 5:

[0521] At the scheduled time, the device initiates an online interview with the subject. Generative artificial intelligence asks questions on the device and records the subject's responses in real time.

[0522] Step 6:

[0523] The terminal and emotion engine analyze the interviewee's voice and video during the interview and evaluate their emotions in real time. The emotion data is categorized into multiple emotion categories such as joy, surprise, and sadness.

[0524] Step 7:

[0525] The server collects response data and sentiment data from participants and stores it in a database. This data is analyzed immediately.

[0526] Step 8:

[0527] The server uses generative artificial intelligence to analyze collected data through text mining and gain insights. It integrates sentiment data and text data to automatically generate detailed reports.

[0528] Step 9:

[0529] The server provides the user with the generated report. Based on this, the user can identify areas for improvement in the product or service and plan their next actions.

[0530] (Example 2)

[0531] 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."

[0532] Traditional market research systems primarily relied on quantitative data analysis, making it difficult to capture consumers' emotional responses. Furthermore, the manual nature of the research process was time-consuming and labor-intensive. Moreover, there was no efficient method for automatically providing the deep insights users desired.

[0533] 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.

[0534] In this invention, the server includes means for automatically generating a list of survey questions using generative artificial intelligence, means for selecting relevant subjects and automatically scheduling interviews, and means for asking questions to subjects in real time in an online environment via a terminal. This makes it possible to quickly and efficiently obtain deep insights, including consumers' emotional responses.

[0535] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to automatically generate new information and content based on input data.

[0536] A "question list" is a set of questions generated for a specific purpose and posed to a target audience.

[0537] "Subjects" refer to individuals or groups selected for research or interviews.

[0538] "An interview appointment" refers to a pre-set time and date for conducting an online interview.

[0539] A "server" is a computer system used to process data and provide information over a network.

[0540] A "terminal" is a device used by users or target individuals to connect to a computer network and input or output data.

[0541] An "emotion engine" is a technology that analyzes audio and video data to evaluate and identify the emotional responses of a subject.

[0542] A "database" is an information system that systematically stores data in digital format and efficiently searches and manages it.

[0543] "Insight" refers to the insights and understanding gained through analysis and evaluation, and is particularly useful in marketing and research activities.

[0544] This invention is an information processing system specifically designed for market research. By combining generative artificial intelligence and an emotion recognition engine, it aims to provide deep customer insights that cannot be obtained through conventional research methods.

[0545] First, the user sends detailed information (e.g., target consumer group, survey items) to the server, tailored to the purpose of the market research. This information is sent from the user's device via a web form or a dedicated application. Based on the received information, the server automatically generates an appropriate list of questions using a generative AI model. This generated list of questions serves as the criterion for target selection.

[0546] Next, the server automatically selects target individuals based on profile information stored in the database beforehand. Then, it automatically sets up appointments, including the date and time of the interview, for the selected individuals.

[0547] During the interview, participants join the online interview via a device at a pre-specified date and time. At this time, a generative artificial intelligence presents questions to the participant in real time on the device. Meanwhile, an emotion engine analyzes audio and video data in real time, evaluating and recording the participant's emotional responses. For security reasons, this data is encrypted before being sent to the server and recorded in a database.

[0548] The server analyzes the collected data based on this information and automatically generates a report using a generative AI model. This report includes not only the interview responses but also emotional insights derived from tone of voice and facial expressions. Based on this detailed report, users can refine their marketing strategies and product development.

[0549] For example, when conducting market research for a new product, unexpected emotional responses from consumers, such as "surprise" or "joy," can be qualitatively analyzed, providing clues to explore new directions in product development.

[0550] An example of a prompt to a generative AI model is: "We would like to conduct a market survey on a new smartphone case targeting the following demographic: students aged 15 to 25. Please generate a list of questions that emphasize emotional responses."

[0551] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0552] Step 1:

[0553] The user sends information about the purpose of the market research and the target audience to the server. The input includes the research purpose, questions, and details about the target consumer group. The server receives this information and generates prompts for the AI ​​model. Based on these prompts, the AI ​​model automatically generates a list of questions. The output is the generated list of questions.

[0554] Step 2:

[0555] The server uses the generated list of questions to select suitable candidates from a database containing past survey data and schedules appointments. This process uses the questions and candidate attributes as input. Candidates are identified via queries from the database, and interview details are automatically sent via email or messaging systems. The output is a list of candidates with scheduled appointments.

[0556] Step 3:

[0557] The user prepares to connect online with the interviewee via a terminal to conduct the interview at the specified date and time. The terminal sets up the environment for the online interview and uses generative artificial intelligence to present questions to the interviewee in real time. The input includes a pre-set interview date and time and a list of questions. The questions are presented to the interviewee as appropriate via the interface on the terminal. The output is the interviewee's real-time responses.

[0558] Step 4:

[0559] The device acquires the subject's audio and video data in real time during the interview and performs sentiment analysis using an emotion engine. The input is the subject's audio and video data. The emotion engine calculates emotional indicators such as smiles, surprise, and confusion. The output is sentiment data recorded along with the interview content.

[0560] Step 5:

[0561] The terminal combines the acquired interview audio and emotion data and sends it to the server. The server receives this data and securely stores it in a database. The input is encrypted interview data. After the storage process, the output is a complete, securely stored dataset.

[0562] Step 6:

[0563] The server automatically generates insight reports using a generative AI model based on stored datasets. Inputs include interview and sentiment data. Data analysis techniques extract insights based on individual consumer responses. The output is an insight report, used in developing marketing strategies.

[0564] Step 7:

[0565] The server provides the user with the generated insights report. The report is sent to the user via email or a cloud-based dashboard. The input is the generated report data. The output is the detailed report provided to the user.

[0566] (Application Example 2)

[0567] 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."

[0568] In online market research and e-commerce, accurately and in real time understanding consumer sentiment has made it difficult to gain deeper insights. In particular, the emotions and feedback that consumers experience during e-commerce are difficult to quantify using existing survey methods, making it challenging to incorporate them into effective strategies.

[0569] 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.

[0570] In this invention, the server includes means for automatically generating a list of questions using generative artificial intelligence, means for conducting online interviews with subjects, means for recording the results of the online interviews in a database in real time, means for analyzing the collected data and automatically generating a report, means for emotion recognition to analyze the emotions of users, and means for generating and providing feedback based on the emotion analysis results. This makes it possible to quantify consumer emotions in real time, visualize the consumer experience, and gain strategic insights based on it.

[0571] "Generative artificial intelligence" is a type of artificial intelligence that generates new information from data and automates specific tasks.

[0572] "Automatic question list generation" is a process in which generative artificial intelligence automatically creates questions for a target audience based on given objectives and conditions.

[0573] "Methods for conducting online interviews" refer to methods for presenting generated questions to subjects via a network and collecting their responses.

[0574] "Means of recording in a database" refers to the technology for saving information obtained from online interviews to a database.

[0575] "Methods for automatically generating reports" refer to methods that analyze collected data and create reports in a predetermined format based on the results.

[0576] An "emotion recognition system" is a system for analyzing and evaluating emotions from a subject's voice and video data.

[0577] "Means of generating and providing feedback" refers to the process of communicating specific opinions and areas for improvement to consumers based on sentiment analysis results and interview results.

[0578] The system implementing this invention primarily consists of three components: a server, a terminal, and a user. The server first receives a market research request from the user and, based on that request, automatically generates a list of questions using generative artificial intelligence. This list of questions is used for interviews during electronic transactions.

[0579] The terminal is a device used by the user, and here we will use a smartphone as an example. This terminal conducts an online interview based on a generated list of questions. During the online interview, the user's audio and video data are transmitted to the server via the terminal's camera and microphone.

[0580] The server uses this audio and video data to activate emotion recognition mechanisms and analyze the user's emotions in real time. Specifically, it utilizes the image processing library OpenCV and the speech recognition API Google Cloud Speech-to-Text. The analyzed emotion data is immediately stored in a database and analyzed together with the interview responses by generative artificial intelligence.

[0581] Based on this analysis, the server automatically generates and provides a report to the user. This report includes information that quantitatively expresses the user's emotional response. For example, it may include an example where a prompt such as "How do you feel about this new product?" is used to evaluate the feelings of consumers when purchasing a new product through online shopping.

[0582] This allows users to quickly obtain the qualitative insights necessary for market research. Furthermore, applying this technology to electronic trading services can be used to improve the consumer experience and enhance services.

[0583] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0584] Step 1:

[0585] The server receives market research requests from users. These requests include information about the research objectives, target audience, and research methodologies. Based on this input data, the server uses generative artificial intelligence to automatically generate a list of questions and outputs that list.

[0586] Step 2:

[0587] The device receives a list of questions sent from the server. When a user participates in an online interview through the device, the device activates its camera and microphone and collects audio and video data while sequentially presenting questions to the interviewee. This data is then sent to the server.

[0588] Step 3:

[0589] The server receives audio and video data transmitted from the terminal and activates the emotion recognition system. Specifically, it uses OpenCV to detect facial expressions in the video data, converts the audio data to text using Google Cloud Speech-to-Text, and then performs emotion analysis. Emotion data is generated and stored in a database.

[0590] Step 4:

[0591] The server performs analysis using interview responses and sentiment data stored in the database. During this analysis process, generative artificial intelligence integrates the data and extracts quantitative and qualitative insights. This automatically generates and outputs a report containing the insights.

[0592] Step 5:

[0593] The server provides the user with the generated report. The report includes details about consumer emotional responses, allowing users to gain deeper market insights. This enables effective feedback even in electronic trading services.

[0594] 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.

[0595] 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.

[0596] 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.

[0597] [Fourth Embodiment]

[0598] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0599] 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.

[0600] 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).

[0601] 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.

[0602] 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.

[0603] 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).

[0604] 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.

[0605] 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.

[0606] 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.

[0607] 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.

[0608] 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.

[0609] 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.

[0610] 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".

[0611] As an embodiment of the present invention, a market research system using generative artificial intelligence is provided. This system achieves efficient and high-quality market research through a highly automated process involving collaboration among a server, a terminal, and a user.

[0612] The server first receives a survey request from the user and records detailed information about the survey's purpose and target audience. Based on this information, it uses generative artificial intelligence to automatically generate a list of questions necessary for the survey. This list of questions is specialized for the survey's theme and target attributes, supporting highly accurate data collection.

[0613] Next, the server selects target individuals and automatically sets up appointments. This enables efficient interview preparation. The selected individuals conduct online interviews via a terminal. On the terminal, generative artificial intelligence presents questions in real time and records the individuals' responses.

[0614] Interview results are immediately sent to a server and stored in a database. The server then analyzes the collected data and performs text mining using artificial intelligence. This process generates automated reports based on the insights gained.

[0615] The generated reports are provided to users along with visualized data. This allows users to quickly grasp market trends and support strategic decision-making. For example, in a market awareness survey for a new product, multiple interview transcripts set by generative artificial intelligence can be instantly analyzed, providing immediate input for future marketing strategies.

[0616] Thus, the system of the present invention provides a concrete means of innovating market understanding through real-time and highly accurate interview surveys.

[0617] The following describes the processing flow.

[0618] Step 1:

[0619] The user submits a market research request to the server. The request includes details such as the purpose of the research, the target audience, and the research methodology.

[0620] Step 2:

[0621] The server reviews the investigation request received from the user and records its contents in the database. Simultaneously, it activates generative artificial intelligence to automatically generate a list of questions necessary for the investigation.

[0622] Step 3:

[0623] The server selects target individuals based on the generated list of questions. It then creates a candidate list using past history and specified target attributes.

[0624] Step 4:

[0625] The server automatically sends interview appointments to selected participants via email or notification. Participants receive the notification and confirm the interview date and time.

[0626] Step 5:

[0627] When the interview date and time arrives, the device conducts an online interview with the subject via generative artificial intelligence. The AI ​​agent presents questions in real time and collects responses from the subject.

[0628] Step 6:

[0629] The server records interview results from terminals into a database in real time, and the collected data is analyzed using artificial intelligence.

[0630] Step 7:

[0631] The server automatically generates a report based on the analyzed data. The report includes insights extracted through text mining.

[0632] Step 8:

[0633] The server provides the user with the generated report. The user then uses this to evaluate market trends and decide on their next strategic action.

[0634] (Example 1)

[0635] 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".

[0636] The market research process requires seamless integration of question creation, target audience selection, data collection, analysis, and report generation to improve efficiency and accuracy. In particular, there is a need to provide the ability to rapidly process large amounts of data and grasp market trends in real time.

[0637] 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.

[0638] In this invention, the server includes means for automatically generating a list of questions according to the research objective using generative artificial intelligence via an information processing device, means for conducting online interviews with subjects via an information processing terminal, and means for immediately storing the data on a recording medium via a communication line. This enables rapid and highly accurate collection and analysis of data.

[0639] An "information processing device" is a device equipped with the functions of inputting, processing, and outputting data, and in particular, one that uses generative artificial intelligence to generate and analyze data according to specific purposes.

[0640] "Generative artificial intelligence" refers to artificial intelligence technology that has the ability to generate natural language from provided input data, or to make judgments and predictions according to specific tasks.

[0641] An "information processing terminal" is a device that allows users to send and receive data through direct operation, and in online interviews, it is responsible for presenting questions and inputting answers.

[0642] A "communication line" is a physical or wireless path for transmitting data from a sender to a receiver, enabling the real-time transmission of interview results and other data.

[0643] A "recording medium" is a device or physical medium used to store collected data and make it available for retrieval as needed.

[0644] An "analysis processing device" is a computing device that analyzes collected data and extracts useful insights, and performs various information processing tasks, including text mining and report generation.

[0645] A "report" is a document or digital document that summarizes the results of data analysis and provides users with visualized information.

[0646] "Insight" refers to knowledge and understanding gained through data analysis, and is information that helps in grasping specific market trends and consumer behavior.

[0647] The following describes embodiments for carrying out this invention. This system combines an information processing device, an information processing terminal, and a communication line to perform efficient data collection and analysis during the market research process.

[0648] The server uses generative artificial intelligence as its information processing device. Specifically, it uses a text generation model to automatically generate a list of questions based on the research objectives provided by the user. In this process, a platform based on natural language processing technology can be used as an example of a generative AI model. The generated list of questions will be customized according to the characteristics of the target audience and the requirements of the research.

[0649] The user presents a list of questions obtained from the server to the interviewee via an information processing terminal. This terminal is equipped with communication software suitable for conducting interviews and allows for interaction with the interviewee. During the interview, the terminal transmits the interviewee's responses to the server in real time as text or audio. The transmitted data is securely and quickly stored on a recording medium.

[0650] The server analyzes the collected data using text mining techniques via an analytical processing unit. Specifically, Python's natural language processing libraries may be used for data analysis. This data processing allows users to gain important insights into the market.

[0651] Furthermore, the server generates a visualized report based on the analyzed data and provides it to the user. Through this report, the user can understand the awareness and market trends of the subject being investigated and make strategic decisions quickly.

[0652] As a concrete example, when conducting a market awareness survey for a new product, the user inputs a prompt into the server such as, "What measures would be effective in increasing awareness of this product?" Based on this prompt, the generative AI generates interview questions for target customers and provides a report showing the results of the analysis. This system automates the entire market research process, significantly reducing time and effort.

[0653] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0654] Step 1:

[0655] The user inputs the purpose of the market research and target demographic information into the server. This input includes details such as product name, target age group, and region. The server uses this input data to form prompts for a generative AI model, processing the data in the form of "Generate questions to understand awareness among the specified target group."

[0656] Step 2:

[0657] The server invokes a generative AI model to generate a list of questions based on the prompt text. The generation process utilizes natural language processing techniques to output specific and relevant questions optimized for the input conditions. The question list is then stored in a database.

[0658] Step 3:

[0659] The server selects survey participants from its database who meet the criteria specified by the user. This selection process takes into account past participation history and attribute information. Once participants are selected, the server uses an automated system to schedule interview appointments and sends notifications to them.

[0660] Step 4:

[0661] The device initiates an online interview with the research participant at a pre-set date and time. Using video call software, the device sequentially presents generated questions. The participant's responses are collected as text and audio data and transmitted to the server in real time.

[0662] Step 5:

[0663] The server stores the interview data transmitted from the terminals on a recording medium. This data is not sent directly; first, its format is standardized before it is sent to the analysis processing unit. The output from this unit is a dataset in a format suitable for analysis.

[0664] Step 6:

[0665] The analysis processing unit on the server analyzes the collected data using text mining techniques. It utilizes Python libraries to extract important keywords and trends. This process generates insightful information, providing data for report generation as the next output.

[0666] Step 7:

[0667] The server generates and provides users with visualized, interactive reports based on the analysis results. Users can access these reports through a web portal and develop market strategies based on the collected insights. As a result, users can quickly obtain real-time, highly accurate market information and make strategic decisions.

[0668] (Application Example 1)

[0669] 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".

[0670] In market research, efficiently understanding purchasing trends and consumer preferences in real time is difficult, and traditional methods are time-consuming and costly. Furthermore, random questions to users can impair the consumer experience, so there is a need to conduct interviews naturally while providing highly relevant information.

[0671] 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.

[0672] In this invention, the server includes means for automatically generating a list of questions using generative artificial intelligence, means for recording the results of online interviews in a database in real time, and means for presenting relevant information using generative artificial intelligence based on the user's attribute information. This makes it possible to grasp consumers' purchasing behavior and preferences in real time and provide an optimized consumer experience.

[0673] "Generative artificial intelligence" refers to artificial intelligence that possesses the technology to generate new information and content based on training data.

[0674] "Automatic question list generation" is a process that automatically creates interview or survey questions tailored to the specific purpose.

[0675] An "online interview" is a dialogue-based research activity conducted on a digital platform.

[0676] "Recording in real time to a database" refers to the process by which information is instantly saved to a digital recording medium.

[0677] "Presenting relevant information using generative artificial intelligence based on user attribute information" refers to the act of AI selecting and providing highly relevant information while considering the characteristics of each individual user.

[0678] "Understanding consumer purchasing behavior and preferences in real time" refers to the process of instantly understanding how customers choose and purchase products, as well as their preferences.

[0679] "Providing an optimized consumer experience" means improving customer satisfaction by offering the most suitable information and services to individual consumers.

[0680] The system for realizing this invention is built on cooperation between a server, a terminal, and a user.

[0681] The server uses generative artificial intelligence based on detailed attribute information to automatically generate a list of questions to present to the user. This list of questions is tailored to the user's preferences and behavior, and is prepared with the aim of collecting more accurate data.

[0682] The terminal plays a role in presenting users with appropriately generated questions when they use an e-commerce site application, and sending the answers to the server in real time. For example, while a user is searching for a specific product, it might ask, "Please tell us why you are interested in this product," and process the collected data immediately.

[0683] The server also performs text mining using a generative AI model to analyze the received response data. The analysis results extract insights that reveal customer purchasing behavior patterns and are automatically generated as a report. This report is visualized and used by users as a guide when making strategic decisions.

[0684] For example, if a user searches for information on fashion items and it is discovered that they are interested in specific colors or designs, that data can contribute to optimizing future product recommendations and promotional strategies.

[0685] An example of a prompt to input into a generating AI model would be, "Write a program that generates a list of questions for a customer survey based on a theme specified by the user."

[0686] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0687] Step 1:

[0688] The server receives attribute information from the user. This information includes product categories the user is interested in and their past purchase history. Based on this, prompts are sent to generative artificial intelligence, which generates a list of relevant questions. Through this process, the collected user data is transformed into a concrete output in the form of a question list.

[0689] Step 2:

[0690] The server sends the generated list of questions to the user's device. The device receives this list of questions and integrates it seamlessly into the user's shopping experience, displaying the questions at the appropriate times. For example, it might present questions when the user is browsing products or adding items to their cart.

[0691] Step 3:

[0692] The user responds to questions displayed on the device. The user's answers are sent to the server in real time and recorded in the database. At this time, the user's input data is initially processed on the server side and saved in text format.

[0693] Step 4:

[0694] The server uses a generative AI model based on the collected response data to perform text mining. Specifically, it analyzes the dataset and extracts characteristic patterns and trends. The input response data is then output as analysis results of purchasing behavior and preferences.

[0695] Step 5:

[0696] The server automatically generates a report based on the analysis results and provides it to the user. The report includes visualized data, which the user uses to make future decisions. In this process, the server extracts specific insights from the analysis results and generates output that concludes with information useful for decision support.

[0697] 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.

[0698] As an embodiment of the present invention, a market research system using generative artificial intelligence and an emotion engine is provided. This system aims to obtain deeper consumer insights through automated interviews and emotion analysis, with cooperation among a server, terminal, and user.

[0699] First, the user sends a market research request to the server. This request includes information about the purpose of the research, the target audience, and the research methodology. Based on this information, the server automatically generates a list of research questions using generative artificial intelligence.

[0700] Simultaneously, the server activates the emotion engine and prepares for emotion recognition. Based on the generated list of questions, the server selects targets and automatically schedules interview appointments. Participants join the online interview via a terminal, during which a generative artificial intelligence asks questions in real time, while the emotion engine simultaneously evaluates the participant's emotions.

[0701] The emotion engine identifies the subject's emotions by analyzing audio and video data, and records the results along with the interview data. This data is sent to a server and stored in a database. The server immediately analyzes the collected interview results and emotion data using generative artificial intelligence and automatically generates a report. This report includes emotional responses in the target's responses, enabling the user to gain a deeper understanding.

[0702] To give a specific example, in market research for a new product, by using an emotion engine to analyze consumers' emotional responses to the product (joy, surprise, dissatisfaction, etc.), qualitative insights that cannot be obtained from conventional numerical data alone can be acquired, providing new suggestions for marketing strategies. In this way, the system of the present invention specifically constitutes an implementation means that combines generative artificial intelligence and emotion recognition technology to provide richer market research results.

[0703] The following describes the processing flow.

[0704] Step 1:

[0705] Users submit requests to the server to conduct market research. These requests include information such as the attributes of the target audience, the purpose of the research, and the necessary questions.

[0706] Step 2:

[0707] The server registers the survey information received from the user into a database and automatically generates a list of questions using generative artificial intelligence. This list is tailored to the purpose of the survey.

[0708] Step 3:

[0709] The server prepares the emotion engine and selects target individuals. This selection process uses filtering methods based on historical data and specified attributes.

[0710] Step 4:

[0711] The server automatically notifies the subject of the interview appointment via email or message. It then obtains confirmation of consent from the subject.

[0712] Step 5:

[0713] At the scheduled time, the device initiates an online interview with the subject. Generative artificial intelligence asks questions on the device and records the subject's responses in real time.

[0714] Step 6:

[0715] The terminal and emotion engine analyze the interviewee's voice and video during the interview and evaluate their emotions in real time. The emotion data is categorized into multiple emotion categories such as joy, surprise, and sadness.

[0716] Step 7:

[0717] The server collects response data and sentiment data from participants and stores it in a database. This data is analyzed immediately.

[0718] Step 8:

[0719] The server uses generative artificial intelligence to analyze collected data through text mining and gain insights. It integrates sentiment data and text data to automatically generate detailed reports.

[0720] Step 9:

[0721] The server provides the user with the generated report. Based on this, the user can identify areas for improvement in the product or service and plan their next actions.

[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] Traditional market research systems primarily relied on quantitative data analysis, making it difficult to capture consumers' emotional responses. Furthermore, the manual nature of the research process was time-consuming and labor-intensive. Moreover, there was no efficient method for automatically providing the deep insights users desired.

[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 automatically generating a list of survey questions using generative artificial intelligence, means for selecting relevant subjects and automatically scheduling interviews, and means for asking questions to subjects in real time in an online environment via a terminal. This makes it possible to quickly and efficiently obtain deep insights, including consumers' emotional responses.

[0727] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to automatically generate new information and content based on input data.

[0728] A "question list" is a set of questions generated for a specific purpose and posed to a target audience.

[0729] "Subjects" refer to individuals or groups selected for research or interviews.

[0730] "An interview appointment" refers to a pre-set time and date for conducting an online interview.

[0731] A "server" is a computer system used to process data and provide information over a network.

[0732] A "terminal" is a device used by users or target individuals to connect to a computer network and input or output data.

[0733] An "emotion engine" is a technology that analyzes audio and video data to evaluate and identify the emotional responses of a subject.

[0734] A "database" is an information system that systematically stores data in digital format and efficiently searches and manages it.

[0735] "Insight" refers to the insights and understanding gained through analysis and evaluation, and is particularly useful in marketing and research activities.

[0736] This invention is an information processing system specifically designed for market research. By combining generative artificial intelligence and an emotion recognition engine, it aims to provide deep customer insights that cannot be obtained through conventional research methods.

[0737] First, the user sends detailed information (e.g., target consumer group, survey items) to the server, tailored to the purpose of the market research. This information is sent from the user's device via a web form or a dedicated application. Based on the received information, the server automatically generates an appropriate list of questions using a generative AI model. This generated list of questions serves as the criterion for target selection.

[0738] Next, the server automatically selects target individuals based on profile information stored in the database beforehand. Then, it automatically sets up appointments, including the date and time of the interview, for the selected individuals.

[0739] During the interview, participants join the online interview via a device at a pre-specified date and time. At this time, a generative artificial intelligence presents questions to the participant in real time on the device. Meanwhile, an emotion engine analyzes audio and video data in real time, evaluating and recording the participant's emotional responses. For security reasons, this data is encrypted before being sent to the server and recorded in a database.

[0740] The server analyzes the collected data based on this information and automatically generates a report using a generative AI model. This report includes not only the interview responses but also emotional insights derived from tone of voice and facial expressions. Based on this detailed report, users can refine their marketing strategies and product development.

[0741] For example, when conducting market research for a new product, unexpected emotional responses from consumers, such as "surprise" or "joy," can be qualitatively analyzed, providing clues to explore new directions in product development.

[0742] An example of a prompt to a generative AI model is: "We would like to conduct a market survey on a new smartphone case targeting the following demographic: students aged 15 to 25. Please generate a list of questions that emphasize emotional responses."

[0743] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0744] Step 1:

[0745] The user sends information about the purpose of the market research and the target audience to the server. The input includes the research purpose, questions, and details about the target consumer group. The server receives this information and generates prompts for the AI ​​model. Based on these prompts, the AI ​​model automatically generates a list of questions. The output is the generated list of questions.

[0746] Step 2:

[0747] The server uses the generated list of questions to select suitable candidates from a database containing past survey data and schedules appointments. This process uses the questions and candidate attributes as input. Candidates are identified via queries from the database, and interview details are automatically sent via email or messaging systems. The output is a list of candidates with scheduled appointments.

[0748] Step 3:

[0749] The user prepares to connect online with the interviewee via a terminal to conduct the interview at the specified date and time. The terminal sets up the environment for the online interview and uses generative artificial intelligence to present questions to the interviewee in real time. The input includes a pre-set interview date and time and a list of questions. The questions are presented to the interviewee as appropriate via the interface on the terminal. The output is the interviewee's real-time responses.

[0750] Step 4:

[0751] The device acquires the subject's audio and video data in real time during the interview and performs sentiment analysis using an emotion engine. The input is the subject's audio and video data. The emotion engine calculates emotional indicators such as smiles, surprise, and confusion. The output is sentiment data recorded along with the interview content.

[0752] Step 5:

[0753] The terminal combines the acquired interview audio and emotion data and sends it to the server. The server receives this data and securely stores it in a database. The input is encrypted interview data. After the storage process, the output is a complete, securely stored dataset.

[0754] Step 6:

[0755] The server automatically generates insight reports using a generative AI model based on stored datasets. Inputs include interview and sentiment data. Data analysis techniques extract insights based on individual consumer responses. The output is an insight report, used in developing marketing strategies.

[0756] Step 7:

[0757] The server provides the user with the generated insights report. The report is sent to the user via email or a cloud-based dashboard. The input is the generated report data. The output is the detailed report provided to the user.

[0758] (Application Example 2)

[0759] 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".

[0760] In online market research and e-commerce, accurately and in real time understanding consumer sentiment has made it difficult to gain deeper insights. In particular, the emotions and feedback that consumers experience during e-commerce are difficult to quantify using existing survey methods, making it challenging to incorporate them into effective strategies.

[0761] 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.

[0762] In this invention, the server includes means for automatically generating a list of questions using generative artificial intelligence, means for conducting online interviews with subjects, means for recording the results of the online interviews in a database in real time, means for analyzing the collected data and automatically generating a report, means for emotion recognition to analyze the emotions of users, and means for generating and providing feedback based on the emotion analysis results. This makes it possible to quantify consumer emotions in real time, visualize the consumer experience, and gain strategic insights based on it.

[0763] "Generative artificial intelligence" is a type of artificial intelligence that generates new information from data and automates specific tasks.

[0764] "Automatic question list generation" is a process in which generative artificial intelligence automatically creates questions for a target audience based on given objectives and conditions.

[0765] "Methods for conducting online interviews" refer to methods for presenting generated questions to subjects via a network and collecting their responses.

[0766] "Means of recording in a database" refers to the technology for saving information obtained from online interviews to a database.

[0767] "Methods for automatically generating reports" refer to methods that analyze collected data and create reports in a predetermined format based on the results.

[0768] An "emotion recognition system" is a system for analyzing and evaluating emotions from a subject's voice and video data.

[0769] "Means of generating and providing feedback" refers to the process of communicating specific opinions and areas for improvement to consumers based on sentiment analysis results and interview results.

[0770] The system implementing this invention primarily consists of three components: a server, a terminal, and a user. The server first receives a market research request from the user and, based on that request, automatically generates a list of questions using generative artificial intelligence. This list of questions is used for interviews during electronic transactions.

[0771] The terminal is a device used by the user, and here we will use a smartphone as an example. This terminal conducts an online interview based on a generated list of questions. During the online interview, the user's audio and video data are transmitted to the server via the terminal's camera and microphone.

[0772] The server uses this audio and video data to activate emotion recognition mechanisms and analyze the user's emotions in real time. Specifically, it utilizes the image processing library OpenCV and the speech recognition API Google Cloud Speech-to-Text. The analyzed emotion data is immediately stored in a database and analyzed together with the interview responses by generative artificial intelligence.

[0773] Based on this analysis, the server automatically generates and provides a report to the user. This report includes information that quantitatively expresses the user's emotional response. For example, it may include an example where a prompt such as "How do you feel about this new product?" is used to evaluate the feelings of consumers when purchasing a new product through online shopping.

[0774] This allows users to quickly obtain the qualitative insights necessary for market research. Furthermore, applying this technology to electronic trading services can be used to improve the consumer experience and enhance services.

[0775] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0776] Step 1:

[0777] The server receives market research requests from users. These requests include information about the research objectives, target audience, and research methodologies. Based on this input data, the server uses generative artificial intelligence to automatically generate a list of questions and outputs that list.

[0778] Step 2:

[0779] The device receives a list of questions sent from the server. When a user participates in an online interview through the device, the device activates its camera and microphone and collects audio and video data while sequentially presenting questions to the interviewee. This data is then sent to the server.

[0780] Step 3:

[0781] The server receives audio and video data transmitted from the terminal and activates the emotion recognition system. Specifically, it uses OpenCV to detect facial expressions in the video data, converts the audio data to text using Google Cloud Speech-to-Text, and then performs emotion analysis. Emotion data is generated and stored in a database.

[0782] Step 4:

[0783] The server performs analysis using interview responses and sentiment data stored in the database. During this analysis process, generative artificial intelligence integrates the data and extracts quantitative and qualitative insights. This automatically generates and outputs a report containing the insights.

[0784] Step 5:

[0785] The server provides the user with the generated report. The report includes details about consumer emotional responses, allowing users to gain deeper market insights. This enables effective feedback even in electronic trading services.

[0786] 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.

[0787] 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.

[0788] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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."

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] The following is further disclosed regarding the embodiments described above.

[0808] (Claim 1)

[0809] A means of automatically generating a list of questions using generative artificial intelligence,

[0810] A means of conducting online interviews with the subjects based on the aforementioned list of questions,

[0811] A means for recording the results of the aforementioned online interview in a database in real time,

[0812] A method for automatically generating reports by analyzing collected data,

[0813] Means for providing the aforementioned report to the user,

[0814] A system that includes this.

[0815] (Claim 2)

[0816] The system according to claim 1, further comprising means for automatically selecting the subject and setting up interview appointments.

[0817] (Claim 3)

[0818] The system according to claim 1, further comprising means for performing text mining on the audio or text data of the aforementioned interview using generative artificial intelligence to extract relevant insights.

[0819] "Example 1"

[0820] (Claim 1)

[0821] A means for automatically generating a list of questions tailored to the purpose of a survey using generative artificial intelligence via an information processing device,

[0822] A means of conducting an online interview with the subject via an information processing terminal based on the aforementioned list of questions,

[0823] A means for immediately storing data obtained from the aforementioned online interview on a recording medium via a communication line,

[0824] A means for analyzing data stored on a recording medium using an analytical processing device and automatically generating a report,

[0825] A means of providing the aforementioned report to an information user using a display device,

[0826] A system that includes this.

[0827] (Claim 2)

[0828] The system according to claim 1, further comprising means for the information processing device to automatically identify a subject and set an interview schedule.

[0829] (Claim 3)

[0830] The system according to claim 1, further comprising means for performing text mining on speech and text information using generative artificial intelligence with the analysis processing device and extracting relevant insights.

[0831] "Application Example 1"

[0832] (Claim 1)

[0833] A means of automatically generating a list of questions using generative artificial intelligence,

[0834] A means of conducting online interviews with the subjects based on the aforementioned list of questions,

[0835] A means for recording the results of the aforementioned online interview in a database in real time,

[0836] A method for automatically generating reports by analyzing collected data,

[0837] Means for providing the aforementioned report to the user,

[0838] A means of presenting relevant information using generative artificial intelligence based on user attribute information,

[0839] A method for inserting questions while users are viewing target information and analyzing data using the collected responses,

[0840] A means of feeding analysis results back into the activity engine to provide an optimized experience,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, further comprising means for automatically selecting the subject and setting up interview appointments.

[0844] (Claim 3)

[0845] The system according to claim 1, further comprising means for performing text mining on the audio or text data of the aforementioned interview using generative artificial intelligence to extract relevant insights.

[0846] "Example 2 of combining an emotion engine"

[0847] (Claim 1)

[0848] A method for automatically generating a list of survey questions using generative artificial intelligence,

[0849] A means of selecting relevant subjects based on the aforementioned list of questions and automatically scheduling interviews,

[0850] A means of asking questions to a target in real time in an online environment via a terminal,

[0851] It is equipped with an emotion engine for analyzing the subject's emotions in real time, and means for identifying the subject's emotional response,

[0852] A means for recording the results of the aforementioned online interview in a database along with audio and video data,

[0853] A means to integrate and analyze collected interview and emotional data and automatically generate reports,

[0854] Means for providing the aforementioned report to the user,

[0855] A system that includes this.

[0856] (Claim 2)

[0857] The system according to claim 1, further comprising means for analyzing the subject's voice and video data using an emotion engine and recording their emotional responses during an online interview.

[0858] (Claim 3)

[0859] The system according to claim 1, further comprising means for analyzing audio data or image data obtained from the aforementioned interview and automatically extracting relevant insights using generative artificial intelligence.

[0860] "Application example 2 when combining with an emotional engine"

[0861] (Claim 1)

[0862] A means of automatically generating a list of questions using generative artificial intelligence,

[0863] A means of conducting online interviews with the subjects based on the aforementioned list of questions,

[0864] A means for recording the results of the aforementioned online interview in a database in real time,

[0865] A method for automatically generating reports by analyzing collected data,

[0866] Means for providing the aforementioned report to the user,

[0867] An emotion recognition method for analyzing the emotions of users during electronic transactions,

[0868] A means of generating and providing feedback based on the above emotion analysis results,

[0869] A system that includes this.

[0870] (Claim 2)

[0871] The system according to claim 1, further comprising means for automatically selecting the subject and setting up interview appointments.

[0872] (Claim 3)

[0873] The system according to claim 1, further comprising means for performing text mining on the audio or text data of the aforementioned interview using generative artificial intelligence to extract relevant insights. [Explanation of Symbols]

[0874] 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. A means of automatically generating a list of questions using generative artificial intelligence, A means of conducting online interviews with the subjects based on the aforementioned list of questions, A means for recording the results of the aforementioned online interview in a database in real time, A method for automatically generating reports by analyzing collected data, Means for providing the aforementioned report to the user, A means of presenting relevant information using generative artificial intelligence based on user attribute information, A method for inserting questions while users are viewing target information and analyzing data using the collected responses, A means of feeding analysis results back into the activity engine to provide an optimized experience, A system that includes this.

2. The system according to claim 1, further comprising means for automatically selecting the subject and setting up interview appointments.

3. The system according to claim 1, further comprising means for performing text mining on the audio or text data of the aforementioned interview using generative artificial intelligence to extract relevant insights.

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