system

Abstract

We provide the system. [Solution] A behavioral analysis method for selecting the target group for investigation, A scheduling method that automatically sets the date and time of the selected research subjects and interviews, Real-time communication methods for conducting interviews via online chat or video call, An analytical and evaluation method for analyzing interview results and generating reports, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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, it is necessary to quickly and efficiently grasp consumer trends that are so complex and diverse that they cannot be fully addressed by conventional manual methods. Also, there is a problem that conducting interviews with a large number of subjects and analyzing the results require a great deal of time and cost. Therefore, there is a need for a new research method that can obtain effective insights from more subjects while reducing the cost and time of the research.

Means for Solving the Problems

[0005] This invention provides a system that uses a generative AI agent to streamline the entire research process. Specifically, the system selects the optimal research target group using behavioral analysis means and automatically sets interview dates and times using scheduling means. Then, interviews are conducted using online chat or video calls via real-time communication means, and the interview results are quickly analyzed by analysis and evaluation means to generate reports. This system enables the efficient collection and analysis of a large amount of consumer insights, realizing advanced market research.

[0006] A "target group" refers to the group of consumers from whom data is collected in market research.

[0007] "Behavioral analysis methods" are techniques used to identify and select target groups for research by analyzing past consumer behavior data and market trends.

[0008] The "scheduling method" is a function that automatically sets the date and time for interviews with selected research participants.

[0009] "Real-time communication methods" refer to communication technologies used to conduct interviews via online chat or video calls.

[0010] "Natural language processing" is an artificial intelligence technology that understands and processes human language, and is used for analyzing the flow and responses of interviews.

[0011] "Interview management means" refers to a technology that uses natural language processing to control the content of an interview and the consumer's responses in order to facilitate the interview process.

[0012] "Analysis and evaluation tools" refer to technologies for analyzing collected interview data, extracting insights, and generating reports. [Brief explanation of the drawing]

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

[0014] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

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

[0016] 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), etc.

[0017] 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.

[0018] 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, etc.

[0019] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

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

[0021] [First Embodiment]

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

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

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

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

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

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

[0034] This invention relates to a system that streamlines the entire market research process using a generative AI agent. This system can handle everything from selecting research subjects to conducting interviews and analyzing the results.

[0035] First, the user inputs information about the research objectives and target market into the system. This information includes product categories, target age groups, and regions. Based on the input information, the server uses behavioral analysis tools to select appropriate research groups. This selection is based on historical behavioral data and real-time market trend data.

[0036] Next, the server automatically sends interview invitations to the selected participants via a scheduling mechanism. Notifications are sent via email or SMS, and the system also includes a function to manage consumer responses.

[0037] When the scheduled interview time arrives, the device initiates the interview session. The interview is then conducted via online chat or video call using real-time communication methods. A generative AI agent utilizes natural language processing to interact with the consumer and manage the progress of the interview.

[0038] The data collected during interviews is analyzed on a server using analytical and evaluation tools. The analysis extracts key phrases and topics from the response data to capture trends in consumer sentiment and opinions. Based on the analysis results, a detailed research report is generated. This report is provided to the user in PDF file or online dashboard format.

[0039] As a concrete example, in market research for a new fashion product targeting women in their 20s, the system automatically selects the appropriate target group and conducts online interviews with 500 consumers. This allows for rapid acquisition of highly accurate insights, which are then used in product development and marketing strategies.

[0040] This invention makes it possible to significantly reduce the cost and time of market research and obtain highly accurate research results.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user enters the objectives of the market research and the target market information into the system. This stage includes information on the target product category and consumer attributes (age, gender, region, etc.).

[0044] Step 2:

[0045] The server uses behavioral analysis tools based on the input market information to select the target group for the survey. This includes matching consumer databases with real-time market trend data.

[0046] Step 3:

[0047] The server automatically sends interview invitations to selected research participants using a scheduling mechanism. Notifications are sent via email or SMS, and responses and participation confirmations from consumers are managed.

[0048] Step 4:

[0049] The device initiates the interview session according to the set date and time. The interview begins via online chat or video call using a real-time communication method.

[0050] Step 5:

[0051] A generative AI agent interacts with consumers through natural language processing and manages the progress of the interview. It appropriately adjusts pre-set questions as needed to efficiently collect consumer responses.

[0052] Step 6:

[0053] The device records the interview responses in a database. The acquired data is saved in audio or text format for later analysis.

[0054] Step 7:

[0055] The server analyzes the collected data using analytical and evaluation tools. This involves extracting key topics and performing sentiment analysis based on consumer responses.

[0056] Step 8:

[0057] The server automatically generates a research report based on the analysis results. This report includes consumer insights and recommendations obtained.

[0058] Step 9:

[0059] The server provides the generated report to the user. The report can be viewed as a PDF file or in online dashboard format.

[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 significant time and resources, from selecting research groups and preparing and conducting interviews to analyzing the results. Traditional methods are inefficient, leading to increased costs and challenges in the accuracy of the results.

[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 data processing means, planning means, and communication control means. This enables efficient selection of the target group for investigation, automated interview execution, and rapid data analysis.

[0065] "Data processing means" refers to a device or system that collects and analyzes data necessary for appropriately selecting the target group for investigation.

[0066] A "plan setting means" is a device or process for automatically setting the selected survey target and the date and time of the survey.

[0067] "Communication control means" refers to a device or software for conducting interviews via digital communication.

[0068] "Information analysis means" refers to a device or function for analyzing interview results and generating a detailed report.

[0069] A "notification management system" is a device or process that notifies selected survey subjects about the survey and manages their responses.

[0070] A "dialogue management device" is a device or function that manages the progress of an interview conducted via digital communication using language analysis technology.

[0071] This invention relates to a system that streamlines the market research process using a generative AI agent. This system can handle everything from selecting the target group to conducting interviews and analyzing the results.

[0072] First, the user enters their market research objectives and information into the system. This information includes product category, age group, and region. The user enters and submits the information through an input form via a web browser.

[0073] Next, the server uses data processing tools to analyze the information sent by the user. Here, it utilizes Google Cloud's database service to search for and analyze past consumer behavior data, using data analysis libraries such as Pandas. This data processing makes it possible to identify the target group for the survey.

[0074] Subsequently, the server uses SMS and email services to send the interview date, time, and participation link to the selected participants as a scheduling method. Specifically, it uses the Twilio API and SendGrid service. The message sent will include a confirmation link for participation in the interview, and participants will confirm their participation by clicking the key.

[0075] When the scheduled interview time arrives, the device automatically initiates the interview via a digital communication platform such as Zoom or Microsoft Teams® using communication control means. The interview is then conducted using natural language processing with a generative AI agent (e.g., a large-scale language model).

[0076] The data collected during interviews is stored on a server and analyzed using data analysis tools. Text data is analyzed using Python natural language processing libraries (such as spaCy and VADER), and responses are summarized and sentiment analyzed. Based on the results, a survey report is automatically generated. This report is created in HTML format using a Jinja template and exported as a PDF file. It is also provided as an online dashboard via Power BI, which users can view through a web browser.

[0077] As a concrete example, in a market research project for a new fashion product targeting women in their 20s, the system conducts online interviews with 500 consumers to provide rapid and highly accurate insights. An example of a prompt message could be, "Generate an interview script to investigate consumer opinions in the market for a new fashion accessory targeting women in their 20s."

[0078] This system allows users to significantly reduce the cost and time of market research and obtain more accurate research results.

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

[0080] Step 1:

[0081] Users input market research objectives and information into the system. This input includes information such as product category, target age group, and region. This input data is sent to the server via a web browser form. The server receives this data and stores it in its database.

[0082] Step 2:

[0083] The server uses data processing tools to analyze information received from users. Here, historical consumer behavior data and real-time market data are aggregated and analyzed. Specifically, queries are executed using a database service, and the data is stored in a data frame using Pandas. This generates profiles of the surveyed group.

[0084] Step 3:

[0085] The server sends the interview date and time, along with a participation link, to the selected research participants via the planning mechanism. The input includes a list of selected research participants and their contact information, which is used to send notifications via the Twilio API or email service. The output is a log of the notification transmissions.

[0086] Step 4:

[0087] When the interview date and time arrives, the device establishes digital communication using communication control means. It sets up a video conference using a platform such as Zoom or Microsoft Teams and starts the interview session via a generative AI model. The device verifies participants, monitors connection status, and troubleshoots as needed.

[0088] Step 5:

[0089] The server analyzes data collected through interviews. The input is interview data expressed in natural language, and sentiment analysis and key phrase extraction are performed using Python's NLP library. The analysis results are output as statistical data and used for report generation.

[0090] Step 6:

[0091] The server uses data analysis tools to generate a report based on the analysis results. This involves creating an HTML summary using a Jinja template and exporting it as a PDF file. It is also provided as an online dashboard via Power BI. Finally, users can view and download these results from a web browser.

[0092] (Application Example 1)

[0093] 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."

[0094] Traditional market research processes have been plagued by significant time and cost involved in selecting research subjects, conducting interviews, and analyzing results. Furthermore, it was difficult to appropriately select interview participants and collect feedback quickly and effectively.

[0095] 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.

[0096] In this invention, the server includes processing means for selecting research subjects and analyzing behavioral data; system means for automatically scheduling and inviting interviews with selected research subjects; digital dialogue means for conducting interviews and communicating in real time; data processing means for analyzing consumer opinions and generating reports; and response processing means for providing research-related feedback in real time via an online platform. This enables increased efficiency and rapid data collection and analysis throughout the entire market research process.

[0097] "Survey subjects" refer to individual participants selected as information providers whose information aligns with the objectives of the market research.

[0098] "Behavioral data" refers to data that includes consumers' past purchase history and online behavior history, and is used to analyze consumer behavior patterns.

[0099] "Processing means" refers to computational and algorithmic methods used to analyze digital information and perform specific tasks.

[0100] "Scheduling" is the process of systematically setting the date and time for specific events or tasks.

[0101] A "system means" refers to an integrated technological system, such as a combination of software and hardware, built to achieve a specific purpose.

[0102] "Digital dialogue means" refers to technological means used to conduct real-time voice or text-based dialogues via the internet.

[0103] "Data processing means" refers to technical means that support the process of analyzing and evaluating collected information and generating insights and reports based on that analysis.

[0104] A "response processing means" is a function that immediately processes feedback information obtained through an online platform and provides it in a format useful for research.

[0105] The system for implementing this invention is realized through the coordinated operation of a server and a terminal. The server first receives the market research objectives and target information entered by the user, and then analyzes the data based on this information. The analysis uses behavioral data, including past purchase history and consumer online behavior data. Based on this, the system uses machine learning frameworks such as Python and scikit-learn to select the most suitable research targets.

[0106] The server schedules interviews with selected research participants. AWS Lambda is used for scheduling, automatically sending out research invitations via email and SMS. This allows for efficient scheduling of participants.

[0107] Next, the device takes on the role of conducting the interview at the designated date and time. The digital dialogue is conducted using real-time communication platforms such as Zoom and Google Meet®, and the interview proceeds smoothly using natural language processing powered by generative AI models. The generative AI model automatically generates phrases and questions in accordance with the progress of the conversation, supporting the interviewer in conducting question and answer smoothly.

[0108] After the interview concludes, the server analyzes the collected data and generates a detailed report using ReportLab. The report is provided in PDF format, allowing users to gain in-depth insights tailored to their specific needs. Users can also receive real-time feedback via an online dashboard.

[0109] As a concrete example, when conducting market research on a new health food product targeting women in their 20s, the following prompt would be used:

[0110] "Target age group: 20s, Category: Health foods, Region: Tokyo. Please select survey participants and schedule interviews based on their past purchase history."

[0111] This format enables rapid and accurate market research, allowing companies to develop effective marketing strategies.

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

[0113] Step 1:

[0114] The server receives data from the user regarding the purpose and target of the market research. This input data includes details such as the target age group, product category, and region. Based on this information, the server collects relevant behavioral history and market trend data from the database to form an analysis platform.

[0115] Step 2:

[0116] The server uses the collected user behavior data to process it for selecting research subjects. This process employs machine learning algorithms such as scikit-learn to select the appropriate target group. As a result, a list of selected research subjects is output.

[0117] Step 3:

[0118] The server automatically schedules interviews with selected participants and sends invitations via email or SMS. Inputs include a list of selected participants and available time slots. AWS Lambda is used to automate this task, and the output provides confirmed interview dates and times.

[0119] Step 4:

[0120] When the interview date and time arrive, the device initiates the interview via a real-time communication platform such as Zoom. Here, a generative AI model is used to perform natural language processing and conduct the dialogue. The input is the content of the chat or call with the interviewee, and the output is a real-time generated interview script and new questions.

[0121] Step 5:

[0122] After the interview is complete, the server analyzes the collected data. The input data consists of responses and observations gathered during the interview. Based on this, an analysis model built in Python and ReportLab are used to generate a report showing consumer opinions and sentiment trends. The output is a detailed report in PDF format.

[0123] Step 6:

[0124] Users view reports generated through an online dashboard and develop marketing strategies based on the insights gained. The input is the generated report data, and the output is an action plan for strategy development.

[0125] 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.

[0126] This invention combines a market research system utilizing a generative AI agent with an emotion engine that recognizes user emotions. This system streamlines all processes from consumer interview selection and execution to analysis and report generation, and further provides emotion-based insights.

[0127] First, the user inputs information about the purpose of the market research and the target market into the system. Based on this, the server uses behavioral analysis tools to select an appropriate group of research participants. For the selected participants, the server automatically sets the date and time for interview invitations using scheduling tools, sends notifications, and manages responses.

[0128] During the interview phase, the device initiates a session via online chat or video call. A generative AI agent uses natural language processing to conduct the interview and collect responses from consumers. Additionally, an emotion engine identifies the user's emotions in real time, which is used to dynamically adjust the interview process and questions.

[0129] Emotional data collected during interviews is sent to a server along with the interview results and analyzed by analytical evaluation tools. Based on the emotional trend analysis provided by the emotion engine, a deeper understanding of the nuances of consumer feedback is achieved, leading to more accurate insights. Based on these analysis results, a detailed research report is automatically generated. This report includes key topics, emotional trends, and recommended actions, and is provided to the user in PDF or online dashboard format.

[0130] As a concrete example, in market research for a new healthcare product, the system automatically selects consumers and conducts interviews with approximately 100 people. Each interview is conducted by a generative AI agent, and an emotion engine analyzes consumer responses to uncover potential dissatisfactions and expectations regarding the product. This information is useful for strategically improving the product.

[0131] This invention enables sophisticated market research that takes consumer sentiment into account, improving the reliability and accuracy of research results.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The user inputs information about the research objectives and target market into the system. This information includes the target product, consumer attributes, and the main areas of interest in the research.

[0135] Step 2:

[0136] The server uses behavioral analysis tools based on the input information, referencing historical data and real-time market trends to select an appropriate target group for the survey.

[0137] Step 3:

[0138] The server uses a scheduling mechanism to set interview dates and times for selected participants. Interview invitations are automatically sent via email or SMS, and responses are tracked and managed.

[0139] Step 4:

[0140] The device initiates an online chat or video call interview session at the scheduled date and time. A generative AI agent participates in the interview and leads the conversation.

[0141] Step 5:

[0142] A generative AI agent conducts the interview while collecting consumer responses in real time using natural language processing. Pre-set questions are used, but they are flexibly adjusted based on consumer responses.

[0143] Step 6:

[0144] The emotion engine identifies emotions from consumers' statements, facial expressions, and tone of voice during interviews. This allows the system to understand consumers' emotional responses and dynamically adjust the order and content of interview questions as needed.

[0145] Step 7:

[0146] The device transmits collected response data and sentiment data to the server in real time. This allows all data to be centrally managed in a central database.

[0147] Step 8:

[0148] The server analyzes the collected data using analytical and evaluation tools. This includes identifying key topics and consumer trends, including sentiment analysis using an emotion engine.

[0149] Step 9:

[0150] The server automatically generates a detailed research report based on the analysis results. The report includes the discovered insights and sentiment-based recommendations, and is provided to the user in PDF or online dashboard format.

[0151] Step 10:

[0152] Users review the provided reports and use the findings to improve products and develop marketing strategies.

[0153] (Example 2)

[0154] 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".

[0155] In the field of market research, traditional methods suffer from the significant time and effort required for selecting research subjects, conducting interviews, and analyzing results. Furthermore, obtaining detailed insights that take consumer sentiment into account is difficult, leaving challenges to the reliability and accuracy of market research.

[0156] 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.

[0157] In this invention, the server includes data processing means, time management means, dialogue means, information evaluation means, and emotion recognition means. This enables efficient selection of subjects for investigation, automatic date and time setting, real-time dialogue, and detailed data analysis based on emotions.

[0158] "Data processing means" refers to a device or program that performs processing to select subjects for investigation based on collected information.

[0159] A "time management tool" is a device or program that automatically sets the date and time of interviews for selected research subjects and manages their schedules.

[0160] "Dialogue means" refers to a device or program that uses a communication line to conduct dialogue and communicates with the subject of the investigation via online chat or video call.

[0161] An "information evaluation tool" is a device or program for analyzing dialogue results and generating output in the form of a report.

[0162] An "emotion recognition tool" is a device or program used to identify and analyze the emotions of a research subject in real time.

[0163] "Notification control means" refers to a device or program for notifying selected survey subjects and controlling and managing their replies and response status.

[0164] A "dialogue management device" is a device or program that uses natural language processing to manage dialogue over a communication line and enables natural communication with the research subject.

[0165] This invention is a system for efficiently conducting market research, automating the entire process from selecting research subjects and conducting interviews to analyzing results and generating reports. The system functions using a server, terminals, a generative AI model, and an emotion recognition engine.

[0166] The user first uses a device such as a PC or tablet to input information about the purpose of the market research and the target market into the system. This information is sent to a server, where data processing is performed. The data processing consists of software for analyzing past research data and consumer attribute data.

[0167] Next, the server automatically sets the interview date and time through a time management system and sends a notification to the subject using a communication system. At this time, a notification control system manages replies and automatically adjusts the schedule.

[0168] Once the interview begins, the device uses real-time dialogue methods to conduct online chat or video calls. The generative AI model conducts the interview through natural language processing, effectively collecting the interviewee's responses. Furthermore, emotion recognition methods identify emotions in real time from the interviewee's facial expressions and voice, and incorporate the obtained emotion data into the analysis.

[0169] Ultimately, the server comprehensively analyzes interview results and sentiment data using information evaluation tools, and automatically generates a detailed research report based on this analysis. This report is then converted into an easy-to-understand format and provided to the user as a PDF or online dashboard.

[0170] As a concrete example, in market research for a new healthcare product, the system conducts interviews with approximately 100 pre-selected consumers. Each interview is conducted by a generative AI model, and an emotion recognition engine analyzes the consumers' responses to uncover potential dissatisfactions and expectations regarding the product.

[0171] Examples of prompts for generative AI models:

[0172] "We are conducting market research for a new health supplement. Please set up interviews that take into account the health-conscious demographic of people aged 50 and over, and analyze their potential dissatisfactions and expectations."

[0173] In this way, the system streamlines all processes of market research and provides detailed, emotion-based insights, significantly improving the reliability and accuracy of research results.

[0174] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0175] Step 1:

[0176] The user enters information about their objectives and the target market into the terminal. This becomes the input data. The information includes the purpose of the research, details of the target market, and the insights they seek. The server organizes the received information and stores it in a database. This is the output data that forms the basis for subsequent processes.

[0177] Step 2:

[0178] The server uses data processing tools to analyze the input information and select the most suitable subjects by referring to past data and the attributes of the survey subjects. The server lists the selection results and outputs them as a subject list. This list includes identification information of the candidates who will be surveyed.

[0179] Step 3:

[0180] The server uses a time management system to automatically set the schedules of the selected participants. The input data includes a list of participants and date / time setting conditions. The server adjusts the dates and times to finalize the interview schedule for each participant. This schedule information becomes the output data and forms the basis for notifications.

[0181] Step 4:

[0182] The server uses a communication method to send interview invitation notifications to the target individuals. The notification control system manages the responses from the targets and readjusts the schedule if necessary. The input is the confirmed interview schedule, and the output is the notification sending status.

[0183] Step 5:

[0184] During the interview, the device initiates an online chat or video call via a dialogue method. The generative AI model uses natural language processing to collect responses and manage the conversation. The input data is the consumer's real-time responses, and the output is a record of the interview content.

[0185] Step 6:

[0186] The terminal transmits received voice and text data to an emotion recognition system, which then uses this data to perform real-time emotion analysis. The emotion recognition engine identifies the emotion information and structures the data for analysis. The output is a set of emotion data.

[0187] Step 7:

[0188] The server uses information evaluation tools to comprehensively analyze the collected interview content and sentiment data. The input data consists of interview records and sentiment data, and the server outputs consumer insights and recommendations based on the analysis. This forms the basis of the automatically generated research report.

[0189] Step 8:

[0190] The server generates the final research report and provides it to the user as a PDF or online dashboard. The input data is the analysis results, and the output is in the form of a research report for the user.

[0191] (Application Example 2)

[0192] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0193] In today's advertising market, accurately understanding consumer emotions and dynamically optimizing advertising strategies based on them is essential. However, conventional technologies struggle to analyze emotions in real time and adjust advertising based on that information. To solve this problem, an efficient system is needed that recognizes emotions and reflects them in advertising.

[0194] 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.

[0195] In this invention, the server includes behavioral analysis means for selecting a group of subjects to be surveyed, time management means for automatically setting the date and time of interviews with the selected subjects, real-time communication means for conducting interviews via online chat or video communication, and emotion analysis means for recognizing emotions and adjusting advertising content. This makes it possible to optimize advertising content in real time based on consumers' emotions.

[0196] "Behavioral analysis methods" refer to technical methods for analyzing consumer behavior in order to select a target group for a study.

[0197] "Time management means" refers to a technical method for automatically setting interview dates and times for selected research subjects.

[0198] "Real-time communication means" refers to communication technology used to conduct interviews in real time via online chat or video communication.

[0199] "Analysis and evaluation means" refers to a technical method for analyzing interview results and generating documents.

[0200] "Emotional analysis techniques" are technologies that recognize consumers' emotions and dynamically adjust advertising content based on that information.

[0201] To realize this invention, a server, terminal, and user cooperate to run the system. The server analyzes consumer behavior using behavioral analysis means and selects the target group for the study. A high-performance computer is used as the hardware, and behavioral analysis algorithms are installed as the software. After the selection is complete, the date and time of the interviews are automatically set by a time management means, and a schedule is created.

[0202] The terminal conducts interviews via online chat or video communication using real-time communication methods. A real-time communication platform via the internet is used for communication. This utilizes terminals equipped with high-resolution cameras and microphones.

[0203] During the interview, the built-in emotion analysis system recognizes the consumer's emotions in real time and immediately sends the results to the server. This data is analyzed by an analysis and evaluation system to generate insights for adjusting advertising content. The emotion analysis technology used here includes, for example, Azure® Emotion API and similar emotion recognition software.

[0204] Finally, the generative AI model dynamically generates ad content based on the analysis results, delivering ads optimized for the user. For example, if a consumer is excited after seeing an ad for a new car, the generative AI can use this emotional information to suggest additional details about the car's performance and design. An example of a prompt might be, "If a user frowns while watching a car ad, what additional information would be most appropriate?"

[0205] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0206] Step 1:

[0207] The server receives market information provided by users. Based on this, it selects research groups using behavioral analysis tools. The input is market information, and the output is a list of selected research groups. As part of data processing, consumer behavior patterns are analyzed and appropriate targets are selected.

[0208] Step 2:

[0209] The server automatically schedules interviews for selected research subjects and creates a schedule using a time management system. The input is a list of subjects, and the output is schedule information. The scheduling algorithm takes into account consumers' availability and suggests the optimal date and time.

[0210] Step 3:

[0211] The terminal initiates an online chat or video communication via an instant communication method to conduct an interview. Inputs are the interview question list and consumer responses, while output is the completed interview data. The terminal transmits and receives video and audio in real time and records consumer responses.

[0212] Step 4:

[0213] The emotion analysis system installed in the terminal collects emotion data from the consumer's facial expressions and voice during the interview and analyzes it in real time. The input is voice and video data, and the output is emotion data. Emotion recognition software performs the specific actions of classifying the consumer's emotional state.

[0214] Step 5:

[0215] The server integrates emotional data and interview results using analytical evaluation tools to perform analysis aimed at extracting deep insights. The input is emotional data and interview results, and the output is an analytical report. Using a generative AI model, the system meticulously evaluates consumer responses based on the obtained data and identifies areas for improvement in advertising content.

[0216] Step 6:

[0217] The server utilizes a generative AI model to generate ad content based on analysis results, optimize it for the user, and deliver it to them. The input is the analysis report, and the output is the optimized ad content. Using prompts, the AI ​​automatically generates effective ad copy and visuals.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] [Second Embodiment]

[0222] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0223] 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.

[0224] 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).

[0225] 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.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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".

[0234] This invention relates to a system that streamlines the entire market research process using a generative AI agent. This system can handle everything from selecting research subjects to conducting interviews and analyzing the results.

[0235] First, the user inputs information about the research objectives and target market into the system. This information includes product categories, target age groups, and regions. Based on the input information, the server uses behavioral analysis tools to select appropriate research groups. This selection is based on historical behavioral data and real-time market trend data.

[0236] Next, the server automatically sends interview invitations to the selected participants via a scheduling mechanism. Notifications are sent via email or SMS, and the system also includes a function to manage consumer responses.

[0237] When the scheduled interview time arrives, the device initiates the interview session. The interview is then conducted via online chat or video call using real-time communication methods. A generative AI agent utilizes natural language processing to interact with the consumer and manage the progress of the interview.

[0238] The data collected during interviews is analyzed on a server using analytical and evaluation tools. The analysis extracts key phrases and topics from the response data to capture trends in consumer sentiment and opinions. Based on the analysis results, a detailed research report is generated. This report is provided to the user in PDF file or online dashboard format.

[0239] As a concrete example, in market research for a new fashion product targeting women in their 20s, the system automatically selects the appropriate target group and conducts online interviews with 500 consumers. This allows for rapid acquisition of highly accurate insights, which are then used in product development and marketing strategies.

[0240] This invention makes it possible to significantly reduce the cost and time of market research and obtain highly accurate research results.

[0241] The following describes the processing flow.

[0242] Step 1:

[0243] The user enters the objectives of the market research and the target market information into the system. This stage includes information on the target product category and consumer attributes (age, gender, region, etc.).

[0244] Step 2:

[0245] The server uses behavioral analysis tools based on the input market information to select the target group for the survey. This includes matching consumer databases with real-time market trend data.

[0246] Step 3:

[0247] The server automatically sends interview invitations to selected research participants using a scheduling mechanism. Notifications are sent via email or SMS, and responses and participation confirmations from consumers are managed.

[0248] Step 4:

[0249] The device initiates the interview session according to the set date and time. The interview begins via online chat or video call using a real-time communication method.

[0250] Step 5:

[0251] A generative AI agent interacts with consumers through natural language processing and manages the progress of the interview. It appropriately adjusts pre-set questions as needed to efficiently collect consumer responses.

[0252] Step 6:

[0253] The device records the interview responses in a database. The acquired data is saved in audio or text format for later analysis.

[0254] Step 7:

[0255] The server analyzes the collected data using analytical and evaluation tools. This involves extracting key topics and performing sentiment analysis based on consumer responses.

[0256] Step 8:

[0257] The server automatically generates a research report based on the analysis results. This report includes consumer insights and recommendations obtained.

[0258] Step 9:

[0259] The server provides the generated report to the user. The report can be viewed as a PDF file or in online dashboard format.

[0260] (Example 1)

[0261] 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."

[0262] The market research process requires significant time and resources, from selecting research groups and preparing and conducting interviews to analyzing the results. Traditional methods are inefficient, leading to increased costs and challenges in the accuracy of the results.

[0263] 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.

[0264] In this invention, the server includes data processing means, planning means, and communication control means. This enables efficient selection of the target group for investigation, automated interview execution, and rapid data analysis.

[0265] "Data processing means" refers to a device or system that collects and analyzes data necessary for appropriately selecting the target group for investigation.

[0266] A "plan setting means" is a device or process for automatically setting the selected survey target and the date and time of the survey.

[0267] "Communication control means" refers to a device or software for conducting interviews via digital communication.

[0268] "Information analysis means" refers to a device or function for analyzing interview results and generating a detailed report.

[0269] A "notification management system" is a device or process that notifies selected survey subjects about the survey and manages their responses.

[0270] A "dialogue management device" is a device or function that manages the progress of an interview conducted via digital communication using language analysis technology.

[0271] This invention relates to a system that streamlines the market research process using a generative AI agent. This system can handle everything from selecting the target group to conducting interviews and analyzing the results.

[0272] First, the user enters their market research objectives and information into the system. This information includes product category, age group, and region. The user enters and submits the information through an input form via a web browser.

[0273] Next, the server uses data processing tools to analyze the information sent by the user. Here, it leverages Google Cloud's database service to search for and analyze past consumer behavior data, using data analysis libraries such as Pandas. This data processing makes it possible to identify the target group for the survey.

[0274] Subsequently, the server uses SMS and email services to send the interview date, time, and participation link to the selected participants as a scheduling method. Specifically, it uses the Twilio API and SendGrid service. The message sent will include a confirmation link for participation in the interview, and participants will confirm their participation by clicking the key.

[0275] When the scheduled interview time arrives, the device uses communication control to automatically initiate the interview via a digital communication platform such as Zoom or Microsoft Teams. The interview is then conducted using natural language processing with a generative AI agent (e.g., a large-scale language model).

[0276] The data collected during interviews is stored on a server and analyzed using data analysis tools. Text data is analyzed using Python natural language processing libraries (such as spaCy and VADER), and responses are summarized and sentiment analyzed. Based on the results, a survey report is automatically generated. This report is created in HTML format using a Jinja template and exported as a PDF file. It is also provided as an online dashboard via Power BI, which users can view through a web browser.

[0277] As a concrete example, in a market research project for a new fashion product targeting women in their 20s, the system conducts online interviews with 500 consumers to provide rapid and highly accurate insights. An example of a prompt message could be, "Generate an interview script to investigate consumer opinions in the market for a new fashion accessory targeting women in their 20s."

[0278] This system allows users to significantly reduce the cost and time of market research and obtain more accurate research results.

[0279] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0280] Step 1:

[0281] The user inputs the purpose and information of the market research into the system. The inputs include information such as product category, target age group, and region. These input data are sent to the server through the form of the web browser. The server receives this and stores it in the database.

[0282] Step 2:

[0283] The server uses data processing means to analyze the information received from the user. Here, past consumer behavior data and real-time market data are aggregated and analyzed. Specifically, a query is executed using the database service and stored in a data frame using Pandas. Thereby, the profile of the survey target group is generated.

[0284] Step 3:

[0285] The server sends the interview date and time and the participation link to the selected survey respondents through the planning setting means. The inputs include the list of selected survey respondents and contact information, and notifications are made using the Twilio API or email service. As an output, a transmission log of the notification is generated.

[0286] Step 4:

[0287] When the interview date and time arrives, the terminal establishes digital communication using the communication control means. A video conference is set up using a platform such as Zoom or Microsoft Teams, and the interview session is started through the generated AI model. The terminal monitors the confirmation and connection status of the participants and performs troubleshooting as necessary.

[0288] Step 5:

[0289] The server analyzes data collected through interviews. The input is interview data expressed in natural language, and sentiment analysis and key phrase extraction are performed using Python's NLP library. The analysis results are output as statistical data and used for report generation.

[0290] Step 6:

[0291] The server uses data analysis tools to generate a report based on the analysis results. This involves creating an HTML summary using a Jinja template and exporting it as a PDF file. It is also provided as an online dashboard via Power BI. Finally, users can view and download these results from a web browser.

[0292] (Application Example 1)

[0293] 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 glasses 214 will be referred to as the "terminal."

[0294] Traditional market research processes have been plagued by significant time and cost involved in selecting research subjects, conducting interviews, and analyzing results. Furthermore, it was difficult to appropriately select interview participants and collect feedback quickly and effectively.

[0295] 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.

[0296] In this invention, the server includes processing means for selecting research subjects and analyzing behavioral data; system means for automatically scheduling and inviting interviews with selected research subjects; digital dialogue means for conducting interviews and communicating in real time; data processing means for analyzing consumer opinions and generating reports; and response processing means for providing research-related feedback in real time via an online platform. This enables increased efficiency and rapid data collection and analysis throughout the entire market research process.

[0297] "Survey subjects" refer to individual participants selected as information providers whose information aligns with the objectives of the market research.

[0298] "Behavioral data" refers to data that includes consumers' past purchase history and online behavior history, and is used to analyze consumer behavior patterns.

[0299] "Processing means" refers to computational and algorithmic methods used to analyze digital information and perform specific tasks.

[0300] "Scheduling" is the process of systematically setting the date and time for specific events or tasks.

[0301] A "system means" refers to an integrated technological system, such as a combination of software and hardware, built to achieve a specific purpose.

[0302] "Digital dialogue means" refers to technological means used to conduct real-time voice or text-based dialogues via the internet.

[0303] "Data processing means" refers to technical means that support the process of analyzing and evaluating collected information and generating insights and reports based on that analysis.

[0304] The "response processing means" refers to the function of immediately processing the feedback information obtained through the online platform and providing it in a form useful for the survey.

[0305] The system for implementing the present invention is realized by the cooperation of a server and a terminal. First, the server receives the purpose and target information of the market survey input by the user, and analyzes the data based on this. For the analysis, behavioral data including past purchase history and consumer online behavior data is used. Based on this, a machine learning framework such as Python and scikit-learn is utilized to perform the process of selecting the optimal survey targets.

[0306] The server schedules interviews for the selected survey targets. AWS Lambda is utilized for the scheduling to automatically send survey invitations via email or SMS. As a result, the schedules of the participants can be efficiently secured.

[0307] Next, the terminal is responsible for conducting the interview at the specified date and time. The digital conversation is carried out using a real-time communication platform such as Zoom or Google Meet, and by using natural language processing utilizing a generative AI model, the interview proceeds smoothly. The generative AI model automatically generates phrases and questions according to the progress of the conversation, assisting the interviewer to smoothly conduct question-and-answer sessions.

[0308] After the interview ends, the server analyzes the collected data and generates a detailed report using ReportLab. The report is provided in PDF format, and the user can obtain in-depth insights according to their own purposes. Also, the user can receive feedback in real time via an online dashboard.

[0309] As a specific example, when conducting a market survey targeting women in their 20s on a new health food, the following prompt sentences are used:

[0310] "Target age group: 20s, Category: Health foods, Region: Tokyo. Please select survey participants and schedule interviews based on their past purchase history."

[0311] This format enables rapid and accurate market research, allowing companies to develop effective marketing strategies.

[0312] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0313] Step 1:

[0314] The server receives data from the user regarding the purpose and target of the market research. This input data includes details such as the target age group, product category, and region. Based on this information, the server collects relevant behavioral history and market trend data from the database to form an analysis platform.

[0315] Step 2:

[0316] The server uses the collected user behavior data to process it for selecting research subjects. This process employs machine learning algorithms such as scikit-learn to select the appropriate target group. As a result, a list of selected research subjects is output.

[0317] Step 3:

[0318] The server automatically schedules interviews with selected participants and sends invitations via email or SMS. Inputs include a list of selected participants and available time slots. AWS Lambda is used to automate this task, and the output provides confirmed interview dates and times.

[0319] Step 4:

[0320] When the interview date and time arrive, the device initiates the interview via a real-time communication platform such as Zoom. Here, a generative AI model is used to perform natural language processing and conduct the dialogue. The input is the content of the chat or call with the interviewee, and the output is a real-time generated interview script and new questions.

[0321] Step 5:

[0322] After the interview is complete, the server analyzes the collected data. The input data consists of responses and observations gathered during the interview. Based on this, an analysis model built in Python and ReportLab are used to generate a report showing consumer opinions and sentiment trends. The output is a detailed report in PDF format.

[0323] Step 6:

[0324] Users view reports generated through an online dashboard and develop marketing strategies based on the insights gained. The input is the generated report data, and the output is an action plan for strategy development.

[0325] 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.

[0326] This invention combines a market research system utilizing a generative AI agent with an emotion engine that recognizes user emotions. This system streamlines all processes from consumer interview selection and execution to analysis and report generation, and further provides emotion-based insights.

[0327] First, the user inputs information about the purpose of the market research and the target market into the system. Based on this, the server uses behavioral analysis tools to select an appropriate group of research participants. For the selected participants, the server automatically sets the date and time for interview invitations using scheduling tools, sends notifications, and manages responses.

[0328] During the interview phase, the device initiates a session via online chat or video call. A generative AI agent uses natural language processing to conduct the interview and collect responses from consumers. Additionally, an emotion engine identifies the user's emotions in real time, which is used to dynamically adjust the interview process and questions.

[0329] Emotional data collected during interviews is sent to a server along with the interview results and analyzed by analytical evaluation tools. Based on the emotional trend analysis provided by the emotion engine, a deeper understanding of the nuances of consumer feedback is achieved, leading to more accurate insights. Based on these analysis results, a detailed research report is automatically generated. This report includes key topics, emotional trends, and recommended actions, and is provided to the user in PDF or online dashboard format.

[0330] As a concrete example, in market research for a new healthcare product, the system automatically selects consumers and conducts interviews with approximately 100 people. Each interview is conducted by a generative AI agent, and an emotion engine analyzes consumer responses to uncover potential dissatisfactions and expectations regarding the product. This information is useful for strategically improving the product.

[0331] This invention enables sophisticated market research that takes consumer sentiment into account, improving the reliability and accuracy of research results.

[0332] The following describes the processing flow.

[0333] Step 1:

[0334] The user inputs information about the research objectives and target market into the system. This information includes the target product, consumer attributes, and the main areas of interest in the research.

[0335] Step 2:

[0336] The server uses behavioral analysis tools based on the input information, referencing historical data and real-time market trends to select an appropriate target group for the survey.

[0337] Step 3:

[0338] The server uses a scheduling mechanism to set interview dates and times for selected participants. Interview invitations are automatically sent via email or SMS, and responses are tracked and managed.

[0339] Step 4:

[0340] The device initiates an online chat or video call interview session at the scheduled date and time. A generative AI agent participates in the interview and leads the conversation.

[0341] Step 5:

[0342] A generative AI agent conducts the interview while collecting consumer responses in real time using natural language processing. Pre-set questions are used, but they are flexibly adjusted based on consumer responses.

[0343] Step 6:

[0344] The emotion engine identifies emotions from consumers' statements, facial expressions, and tone of voice during interviews. This allows the system to understand consumers' emotional responses and dynamically adjust the order and content of interview questions as needed.

[0345] Step 7:

[0346] The device transmits collected response data and sentiment data to the server in real time. This allows all data to be centrally managed in a central database.

[0347] Step 8:

[0348] The server analyzes the collected data using analytical and evaluation tools. This includes identifying key topics and consumer trends, including sentiment analysis using an emotion engine.

[0349] Step 9:

[0350] The server automatically generates a detailed research report based on the analysis results. The report includes the discovered insights and sentiment-based recommendations, and is provided to the user in PDF or online dashboard format.

[0351] Step 10:

[0352] Users review the provided reports and use the findings to improve products and develop marketing strategies.

[0353] (Example 2)

[0354] 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".

[0355] In the field of market research, traditional methods suffer from the significant time and effort required for selecting research subjects, conducting interviews, and analyzing results. Furthermore, obtaining detailed insights that take consumer sentiment into account is difficult, leaving challenges to the reliability and accuracy of market research.

[0356] 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.

[0357] In this invention, the server includes data processing means, time management means, dialogue means, information evaluation means, and emotion recognition means. This enables efficient selection of subjects for investigation, automatic date and time setting, real-time dialogue, and detailed data analysis based on emotions.

[0358] "Data processing means" refers to a device or program that performs processing to select subjects for investigation based on collected information.

[0359] A "time management tool" is a device or program that automatically sets the date and time of interviews for selected research subjects and manages their schedules.

[0360] "Dialogue means" refers to a device or program that uses a communication line to conduct dialogue and communicates with the subject of the investigation via online chat or video call.

[0361] An "information evaluation tool" is a device or program for analyzing dialogue results and generating output in the form of a report.

[0362] An "emotion recognition tool" is a device or program used to identify and analyze the emotions of a research subject in real time.

[0363] "Notification control means" refers to a device or program for notifying selected survey subjects and controlling and managing their replies and response status.

[0364] A "dialogue management device" is a device or program that uses natural language processing to manage dialogue over a communication line and enables natural communication with the research subject.

[0365] This invention is a system for efficiently conducting market research, automating the entire process from selecting research subjects and conducting interviews to analyzing results and generating reports. The system functions using a server, terminals, a generative AI model, and an emotion recognition engine.

[0366] The user first uses a device such as a PC or tablet to input information about the purpose of the market research and the target market into the system. This information is sent to a server, where data processing is performed. The data processing consists of software for analyzing past research data and consumer attribute data.

[0367] Next, the server automatically sets the interview date and time through a time management system and sends a notification to the subject using a communication system. At this time, a notification control system manages replies and automatically adjusts the schedule.

[0368] Once the interview begins, the device uses real-time dialogue methods to conduct online chat or video calls. The generative AI model conducts the interview through natural language processing, effectively collecting the interviewee's responses. Furthermore, emotion recognition methods identify emotions in real time from the interviewee's facial expressions and voice, and incorporate the obtained emotion data into the analysis.

[0369] Ultimately, the server comprehensively analyzes interview results and sentiment data using information evaluation tools, and automatically generates a detailed research report based on this analysis. This report is then converted into an easy-to-understand format and provided to the user as a PDF or online dashboard.

[0370] As a concrete example, in market research for a new healthcare product, the system conducts interviews with approximately 100 pre-selected consumers. Each interview is conducted by a generative AI model, and an emotion recognition engine analyzes the consumers' responses to uncover potential dissatisfactions and expectations regarding the product.

[0371] Examples of prompts for generative AI models:

[0372] "We are conducting market research for a new health supplement. Please set up interviews that take into account the health-conscious demographic of people aged 50 and over, and analyze their potential dissatisfactions and expectations."

[0373] In this way, the system streamlines all processes of market research and provides detailed, emotion-based insights, significantly improving the reliability and accuracy of research results.

[0374] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0375] Step 1:

[0376] The user enters information about their objectives and the target market into the terminal. This becomes the input data. The information includes the purpose of the research, details of the target market, and the insights they seek. The server organizes the received information and stores it in a database. This is the output data that forms the basis for subsequent processes.

[0377] Step 2:

[0378] The server uses data processing tools to analyze the input information and select the most suitable subjects by referring to past data and the attributes of the survey subjects. The server lists the selection results and outputs them as a subject list. This list includes identification information of the candidates who will be surveyed.

[0379] Step 3:

[0380] The server uses a time management system to automatically set the schedules of the selected participants. The input data includes a list of participants and date / time setting conditions. The server adjusts the dates and times to finalize the interview schedule for each participant. This schedule information becomes the output data and forms the basis for notifications.

[0381] Step 4:

[0382] The server uses a communication method to send interview invitation notifications to the target individuals. The notification control system manages the responses from the targets and readjusts the schedule if necessary. The input is the confirmed interview schedule, and the output is the notification sending status.

[0383] Step 5:

[0384] During the interview, the device initiates an online chat or video call via a dialogue method. The generative AI model uses natural language processing to collect responses and manage the conversation. The input data is the consumer's real-time responses, and the output is a record of the interview content.

[0385] Step 6:

[0386] The terminal transmits received voice and text data to an emotion recognition system, which then uses this data to perform real-time emotion analysis. The emotion recognition engine identifies the emotion information and structures the data for analysis. The output is a set of emotion data.

[0387] Step 7:

[0388] The server uses information evaluation tools to comprehensively analyze the collected interview content and sentiment data. The input data consists of interview records and sentiment data, and the server outputs consumer insights and recommendations based on the analysis. This forms the basis of the automatically generated research report.

[0389] Step 8:

[0390] The server generates the final research report and provides it to the user as a PDF or online dashboard. The input data is the analysis results, and the output is in the form of a research report for the user.

[0391] (Application Example 2)

[0392] 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."

[0393] In today's advertising market, accurately understanding consumer emotions and dynamically optimizing advertising strategies based on them is essential. However, conventional technologies struggle to analyze emotions in real time and adjust advertising based on that information. To solve this problem, an efficient system is needed that recognizes emotions and reflects them in advertising.

[0394] 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.

[0395] In this invention, the server includes behavioral analysis means for selecting a group of subjects to be surveyed, time management means for automatically setting the date and time of interviews with the selected subjects, real-time communication means for conducting interviews via online chat or video communication, and emotion analysis means for recognizing emotions and adjusting advertising content. This makes it possible to optimize advertising content in real time based on consumers' emotions.

[0396] "Behavioral analysis methods" refer to technical methods for analyzing consumer behavior in order to select a target group for a study.

[0397] "Time management means" refers to a technical method for automatically setting interview dates and times for selected research subjects.

[0398] "Real-time communication means" refers to communication technology used to conduct interviews in real time via online chat or video communication.

[0399] "Analysis and evaluation means" refers to a technical method for analyzing interview results and generating documents.

[0400] "Emotional analysis techniques" are technologies that recognize consumers' emotions and dynamically adjust advertising content based on that information.

[0401] To realize this invention, a server, terminal, and user cooperate to run the system. The server analyzes consumer behavior using behavioral analysis means and selects the target group for the study. A high-performance computer is used as the hardware, and behavioral analysis algorithms are installed as the software. After the selection is complete, the date and time of the interviews are automatically set by a time management means, and a schedule is created.

[0402] The terminal conducts interviews via online chat or video communication using real-time communication methods. A real-time communication platform via the internet is used for communication. This utilizes terminals equipped with high-resolution cameras and microphones.

[0403] During the interview, the built-in emotion analysis system recognizes the consumer's emotions in real time and immediately sends the results to the server. This data is analyzed by an analysis and evaluation system to generate insights for adjusting advertising content. The emotion analysis technology used here includes, for example, the Azure Emotion API and similar emotion recognition software.

[0404] Finally, the generative AI model dynamically generates ad content based on the analysis results, delivering ads optimized for the user. For example, if a consumer is excited after seeing an ad for a new car, the generative AI can use this emotional information to suggest additional details about the car's performance and design. An example of a prompt might be, "If a user frowns while watching a car ad, what additional information would be most appropriate?"

[0405] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0406] Step 1:

[0407] The server receives market information provided by users. Based on this, it selects research groups using behavioral analysis tools. The input is market information, and the output is a list of selected research groups. As part of data processing, consumer behavior patterns are analyzed and appropriate targets are selected.

[0408] Step 2:

[0409] The server automatically schedules interviews for selected research subjects and creates a schedule using a time management system. The input is a list of subjects, and the output is schedule information. The scheduling algorithm takes into account consumers' availability and suggests the optimal date and time.

[0410] Step 3:

[0411] The terminal initiates an online chat or video communication via an instant communication method to conduct an interview. Inputs are the interview question list and consumer responses, while output is the completed interview data. The terminal transmits and receives video and audio in real time and records consumer responses.

[0412] Step 4:

[0413] The emotion analysis system installed in the terminal collects emotion data from the consumer's facial expressions and voice during the interview and analyzes it in real time. The input is voice and video data, and the output is emotion data. Emotion recognition software performs the specific actions of classifying the consumer's emotional state.

[0414] Step 5:

[0415] The server integrates emotional data and interview results using analytical evaluation tools to perform analysis aimed at extracting deep insights. The input is emotional data and interview results, and the output is an analytical report. Using a generative AI model, the system meticulously evaluates consumer responses based on the obtained data and identifies areas for improvement in advertising content.

[0416] Step 6:

[0417] The server utilizes a generative AI model to generate ad content based on analysis results, optimize it for the user, and deliver it to them. The input is the analysis report, and the output is the optimized ad content. Using prompts, the AI ​​automatically generates effective ad copy and visuals.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] [Third Embodiment]

[0422] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0423] 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.

[0424] 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).

[0425] 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.

[0426] 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.

[0427] 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).

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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".

[0434] This invention relates to a system that streamlines the entire market research process using a generative AI agent. This system can handle everything from selecting research subjects to conducting interviews and analyzing the results.

[0435] First, the user inputs information about the research objectives and target market into the system. This information includes product categories, target age groups, and regions. Based on the input information, the server uses behavioral analysis tools to select appropriate research groups. This selection is based on historical behavioral data and real-time market trend data.

[0436] Next, the server automatically sends interview invitations to the selected participants via a scheduling mechanism. Notifications are sent via email or SMS, and the system also includes a function to manage consumer responses.

[0437] When the scheduled interview time arrives, the device initiates the interview session. The interview is then conducted via online chat or video call using real-time communication methods. A generative AI agent utilizes natural language processing to interact with the consumer and manage the progress of the interview.

[0438] The data collected during interviews is analyzed on a server using analytical and evaluation tools. The analysis extracts key phrases and topics from the response data to capture trends in consumer sentiment and opinions. Based on the analysis results, a detailed research report is generated. This report is provided to the user in PDF file or online dashboard format.

[0439] As a concrete example, in market research for a new fashion product targeting women in their 20s, the system automatically selects the appropriate target group and conducts online interviews with 500 consumers. This allows for rapid acquisition of highly accurate insights, which are then used in product development and marketing strategies.

[0440] This invention makes it possible to significantly reduce the cost and time of market research and obtain highly accurate research results.

[0441] The following describes the processing flow.

[0442] Step 1:

[0443] The user enters the objectives of the market research and the target market information into the system. This stage includes information on the target product category and consumer attributes (age, gender, region, etc.).

[0444] Step 2:

[0445] The server uses behavioral analysis tools based on the input market information to select the target group for the survey. This includes matching consumer databases with real-time market trend data.

[0446] Step 3:

[0447] The server automatically sends interview invitations to selected research participants using a scheduling mechanism. Notifications are sent via email or SMS, and responses and participation confirmations from consumers are managed.

[0448] Step 4:

[0449] The device initiates the interview session according to the set date and time. The interview begins via online chat or video call using a real-time communication method.

[0450] Step 5:

[0451] A generative AI agent interacts with consumers through natural language processing and manages the progress of the interview. It appropriately adjusts pre-set questions as needed to efficiently collect consumer responses.

[0452] Step 6:

[0453] The device records the interview responses in a database. The acquired data is saved in audio or text format for later analysis.

[0454] Step 7:

[0455] The server analyzes the collected data using analytical and evaluation tools. This involves extracting key topics and performing sentiment analysis based on consumer responses.

[0456] Step 8:

[0457] The server automatically generates a research report based on the analysis results. This report includes consumer insights and recommendations obtained.

[0458] Step 9:

[0459] The server provides the generated report to the user. The report can be viewed as a PDF file or in online dashboard format.

[0460] (Example 1)

[0461] 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."

[0462] The market research process requires significant time and resources, from selecting research groups and preparing and conducting interviews to analyzing the results. Traditional methods are inefficient, leading to increased costs and challenges in the accuracy of the results.

[0463] 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.

[0464] In this invention, the server includes data processing means, planning means, and communication control means. This enables efficient selection of the target group for investigation, automated interview execution, and rapid data analysis.

[0465] "Data processing means" refers to a device or system that collects and analyzes data necessary for appropriately selecting the target group for investigation.

[0466] A "plan setting means" is a device or process for automatically setting the selected survey target and the date and time of the survey.

[0467] "Communication control means" refers to a device or software for conducting interviews via digital communication.

[0468] "Information analysis means" refers to a device or function for analyzing interview results and generating a detailed report.

[0469] A "notification management system" is a device or process that notifies selected survey subjects about the survey and manages their responses.

[0470] A "dialogue management device" is a device or function that manages the progress of an interview conducted via digital communication using language analysis technology.

[0471] This invention relates to a system that streamlines the market research process using a generative AI agent. This system can handle everything from selecting the target group to conducting interviews and analyzing the results.

[0472] First, the user enters their market research objectives and information into the system. This information includes product category, age group, and region. The user enters and submits the information through an input form via a web browser.

[0473] Next, the server uses data processing tools to analyze the information sent by the user. Here, it leverages Google Cloud's database service to search for and analyze past consumer behavior data, using data analysis libraries such as Pandas. This data processing makes it possible to identify the target group for the survey.

[0474] Subsequently, the server uses SMS and email services to send the interview date, time, and participation link to the selected participants as a scheduling method. Specifically, it uses the Twilio API and SendGrid service. The message sent will include a confirmation link for participation in the interview, and participants will confirm their participation by clicking the key.

[0475] When the scheduled interview time arrives, the device uses communication control to automatically initiate the interview via a digital communication platform such as Zoom or Microsoft Teams. The interview is then conducted using natural language processing with a generative AI agent (e.g., a large-scale language model).

[0476] The data collected during interviews is stored on a server and analyzed using data analysis tools. Text data is analyzed using Python natural language processing libraries (such as spaCy and VADER), and responses are summarized and sentiment analyzed. Based on the results, a survey report is automatically generated. This report is created in HTML format using a Jinja template and exported as a PDF file. It is also provided as an online dashboard via Power BI, which users can view through a web browser.

[0477] As a concrete example, in a market research project for a new fashion product targeting women in their 20s, the system conducts online interviews with 500 consumers to provide rapid and highly accurate insights. An example of a prompt message could be, "Generate an interview script to investigate consumer opinions in the market for a new fashion accessory targeting women in their 20s."

[0478] This system allows users to significantly reduce the cost and time of market research and obtain more accurate research results.

[0479] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0480] Step 1:

[0481] Users input market research objectives and information into the system. This input includes information such as product category, target age group, and region. This input data is sent to the server via a web browser form. The server receives this data and stores it in its database.

[0482] Step 2:

[0483] The server uses data processing tools to analyze information received from users. Here, historical consumer behavior data and real-time market data are aggregated and analyzed. Specifically, queries are executed using a database service, and the data is stored in a data frame using Pandas. This generates profiles of the surveyed group.

[0484] Step 3:

[0485] The server sends the interview date and time, along with a participation link, to the selected research participants via the planning mechanism. The input includes a list of selected research participants and their contact information, which is used to send notifications via the Twilio API or email service. The output is a log of the notification transmissions.

[0486] Step 4:

[0487] When the interview date and time arrives, the device establishes digital communication using communication control means. It sets up a video conference using a platform such as Zoom or Microsoft Teams and starts the interview session via a generative AI model. The device verifies participants, monitors connection status, and troubleshoots as needed.

[0488] Step 5:

[0489] The server analyzes data collected through interviews. The input is interview data expressed in natural language, and sentiment analysis and key phrase extraction are performed using Python's NLP library. The analysis results are output as statistical data and used for report generation.

[0490] Step 6:

[0491] The server uses data analysis tools to generate a report based on the analysis results. This involves creating an HTML summary using a Jinja template and exporting it as a PDF file. It is also provided as an online dashboard via Power BI. Finally, users can view and download these results from a web browser.

[0492] (Application Example 1)

[0493] 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."

[0494] Traditional market research processes have been plagued by significant time and cost involved in selecting research subjects, conducting interviews, and analyzing results. Furthermore, it was difficult to appropriately select interview participants and collect feedback quickly and effectively.

[0495] 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.

[0496] In this invention, the server includes processing means for selecting research subjects and analyzing behavioral data; system means for automatically scheduling and inviting interviews with selected research subjects; digital dialogue means for conducting interviews and communicating in real time; data processing means for analyzing consumer opinions and generating reports; and response processing means for providing research-related feedback in real time via an online platform. This enables increased efficiency and rapid data collection and analysis throughout the entire market research process.

[0497] "Survey subjects" refer to individual participants selected as information providers whose information aligns with the objectives of the market research.

[0498] "Behavioral data" refers to data that includes consumers' past purchase history and online behavior history, and is used to analyze consumer behavior patterns.

[0499] "Processing means" refers to computational and algorithmic methods used to analyze digital information and perform specific tasks.

[0500] "Scheduling" is the process of systematically setting the date and time for specific events or tasks.

[0501] A "system means" refers to an integrated technological system, such as a combination of software and hardware, built to achieve a specific purpose.

[0502] "Digital dialogue means" refers to technological means used to conduct real-time voice or text-based dialogues via the internet.

[0503] "Data processing means" refers to technical means that support the process of analyzing and evaluating collected information and generating insights and reports based on that analysis.

[0504] A "response processing means" is a function that immediately processes feedback information obtained through an online platform and provides it in a format useful for research.

[0505] The system for implementing this invention is realized through the coordinated operation of a server and a terminal. The server first receives the market research objectives and target information entered by the user, and then analyzes the data based on this information. The analysis uses behavioral data, including past purchase history and consumer online behavior data. Based on this, the system uses machine learning frameworks such as Python and scikit-learn to select the most suitable research targets.

[0506] The server schedules interviews with selected research participants. AWS Lambda is used for scheduling, automatically sending out research invitations via email and SMS. This ensures efficient scheduling of participants.

[0507] Next, the device takes on the role of conducting the interview at the designated date and time. The digital dialogue is conducted using real-time communication platforms such as Zoom and Google Meet, and natural language processing powered by generative AI models is used to ensure the interview proceeds smoothly. The generative AI model automatically generates phrases and questions in accordance with the progress of the conversation, helping the interviewer to conduct question-and-answer sessions smoothly.

[0508] After the interview concludes, the server analyzes the collected data and generates a detailed report using ReportLab. The report is provided in PDF format, allowing users to gain in-depth insights tailored to their specific needs. Users can also receive real-time feedback via an online dashboard.

[0509] As a concrete example, when conducting market research on a new health food product targeting women in their 20s, the following prompt would be used:

[0510] "Target age group: 20s, Category: Health foods, Region: Tokyo. Please select survey participants and schedule interviews based on their past purchase history."

[0511] This format enables rapid and accurate market research, allowing companies to develop effective marketing strategies.

[0512] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0513] Step 1:

[0514] The server receives data from the user regarding the purpose and target of the market research. This input data includes details such as the target age group, product category, and region. Based on this information, the server collects relevant behavioral history and market trend data from the database to form an analysis platform.

[0515] Step 2:

[0516] The server uses the collected user behavior data to process it for selecting research subjects. This process employs machine learning algorithms such as scikit-learn to select the appropriate target group. As a result, a list of selected research subjects is output.

[0517] Step 3:

[0518] The server automatically schedules interviews with selected participants and sends invitations via email or SMS. Inputs include a list of selected participants and available time slots. AWS Lambda is used to automate this task, and the output provides confirmed interview dates and times.

[0519] Step 4:

[0520] When the interview date and time arrive, the device initiates the interview via a real-time communication platform such as Zoom. Here, a generative AI model is used to perform natural language processing and conduct the dialogue. The input is the content of the chat or call with the interviewee, and the output is a real-time generated interview script and new questions.

[0521] Step 5:

[0522] After the interview is complete, the server analyzes the collected data. The input data consists of responses and observations gathered during the interview. Based on this, an analysis model built in Python and ReportLab are used to generate a report showing consumer opinions and sentiment trends. The output is a detailed report in PDF format.

[0523] Step 6:

[0524] Users view reports generated through an online dashboard and develop marketing strategies based on the insights gained. The input is the generated report data, and the output is an action plan for strategy development.

[0525] 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.

[0526] This invention combines a market research system utilizing a generative AI agent with an emotion engine that recognizes user emotions. This system streamlines all processes from consumer interview selection and execution to analysis and report generation, and further provides emotion-based insights.

[0527] First, the user inputs information about the purpose of the market research and the target market into the system. Based on this, the server uses behavioral analysis tools to select an appropriate group of research participants. For the selected participants, the server automatically sets the date and time for interview invitations using scheduling tools, sends notifications, and manages responses.

[0528] During the interview phase, the device initiates a session via online chat or video call. A generative AI agent uses natural language processing to conduct the interview and collect responses from consumers. Additionally, an emotion engine identifies the user's emotions in real time, which is used to dynamically adjust the interview process and questions.

[0529] Emotional data collected during interviews is sent to a server along with the interview results and analyzed by analytical evaluation tools. Based on the emotional trend analysis provided by the emotion engine, a deeper understanding of the nuances of consumer feedback is achieved, leading to more accurate insights. Based on these analysis results, a detailed research report is automatically generated. This report includes key topics, emotional trends, and recommended actions, and is provided to the user in PDF or online dashboard format.

[0530] As a concrete example, in market research for a new healthcare product, the system automatically selects consumers and conducts interviews with approximately 100 people. Each interview is conducted by a generative AI agent, and an emotion engine analyzes consumer responses to uncover potential dissatisfactions and expectations regarding the product. This information is useful for strategically improving the product.

[0531] This invention enables sophisticated market research that takes consumer sentiment into account, improving the reliability and accuracy of research results.

[0532] The following describes the processing flow.

[0533] Step 1:

[0534] The user inputs information about the research objectives and target market into the system. This information includes the target product, consumer attributes, and the main areas of interest in the research.

[0535] Step 2:

[0536] The server uses behavioral analysis tools based on the input information, referencing historical data and real-time market trends to select an appropriate target group for the survey.

[0537] Step 3:

[0538] The server uses a scheduling mechanism to set interview dates and times for selected participants. Interview invitations are automatically sent via email or SMS, and responses are tracked and managed.

[0539] Step 4:

[0540] The device initiates an online chat or video call interview session at the scheduled date and time. A generative AI agent participates in the interview and leads the conversation.

[0541] Step 5:

[0542] A generative AI agent conducts the interview while collecting consumer responses in real time using natural language processing. Pre-set questions are used, but they are flexibly adjusted based on consumer responses.

[0543] Step 6:

[0544] The emotion engine identifies emotions from consumers' statements, facial expressions, and tone of voice during interviews. This allows the system to understand consumers' emotional responses and dynamically adjust the order and content of interview questions as needed.

[0545] Step 7:

[0546] The device transmits collected response data and sentiment data to the server in real time. This allows all data to be centrally managed in a central database.

[0547] Step 8:

[0548] The server analyzes the collected data using analytical and evaluation tools. This includes identifying key topics and consumer trends, including sentiment analysis using an emotion engine.

[0549] Step 9:

[0550] The server automatically generates a detailed research report based on the analysis results. The report includes the discovered insights and sentiment-based recommendations, and is provided to the user in PDF or online dashboard format.

[0551] Step 10:

[0552] Users review the provided reports and use the findings to improve products and develop marketing strategies.

[0553] (Example 2)

[0554] 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."

[0555] In the field of market research, traditional methods suffer from the significant time and effort required for selecting research subjects, conducting interviews, and analyzing results. Furthermore, obtaining detailed insights that take consumer sentiment into account is difficult, leaving challenges to the reliability and accuracy of market research.

[0556] 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.

[0557] In this invention, the server includes data processing means, time management means, dialogue means, information evaluation means, and emotion recognition means. This enables efficient selection of subjects for investigation, automatic date and time setting, real-time dialogue, and detailed data analysis based on emotions.

[0558] "Data processing means" refers to a device or program that performs processing to select subjects for investigation based on collected information.

[0559] A "time management tool" is a device or program that automatically sets the date and time of interviews for selected research subjects and manages their schedules.

[0560] "Dialogue means" refers to a device or program that uses a communication line to conduct dialogue and communicates with the subject of the investigation via online chat or video call.

[0561] An "information evaluation tool" is a device or program for analyzing dialogue results and generating output in the form of a report.

[0562] An "emotion recognition tool" is a device or program used to identify and analyze the emotions of a research subject in real time.

[0563] "Notification control means" refers to a device or program for notifying selected survey subjects and controlling and managing their replies and response status.

[0564] A "dialogue management device" is a device or program that uses natural language processing to manage dialogue over a communication line and enables natural communication with the research subject.

[0565] This invention is a system for efficiently conducting market research, automating the entire process from selecting research subjects and conducting interviews to analyzing results and generating reports. The system functions using a server, terminals, a generative AI model, and an emotion recognition engine.

[0566] The user first uses a device such as a PC or tablet to input information about the purpose of the market research and the target market into the system. This information is sent to a server, where data processing is performed. The data processing consists of software for analyzing past research data and consumer attribute data.

[0567] Next, the server automatically sets the interview date and time through a time management system and sends a notification to the subject using a communication system. At this time, a notification control system manages replies and automatically adjusts the schedule.

[0568] Once the interview begins, the device uses real-time dialogue methods to conduct online chat or video calls. The generative AI model conducts the interview through natural language processing, effectively collecting the interviewee's responses. Furthermore, emotion recognition methods identify emotions in real time from the interviewee's facial expressions and voice, and incorporate the obtained emotion data into the analysis.

[0569] Ultimately, the server comprehensively analyzes interview results and sentiment data using information evaluation tools, and automatically generates a detailed research report based on this analysis. This report is then converted into an easy-to-understand format and provided to the user as a PDF or online dashboard.

[0570] As a concrete example, in market research for a new healthcare product, the system conducts interviews with approximately 100 pre-selected consumers. Each interview is conducted by a generative AI model, and an emotion recognition engine analyzes the consumers' responses to uncover potential dissatisfactions and expectations regarding the product.

[0571] Examples of prompts for generative AI models:

[0572] "We are conducting market research for a new health supplement. Please set up interviews that take into account the health-conscious demographic of people aged 50 and over, and analyze their potential dissatisfactions and expectations."

[0573] In this way, the system streamlines all processes of market research and provides detailed, emotion-based insights, significantly improving the reliability and accuracy of research results.

[0574] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0575] Step 1:

[0576] The user enters information about their objectives and the target market into the terminal. This becomes the input data. The information includes the purpose of the research, details of the target market, and the insights they seek. The server organizes the received information and stores it in a database. This is the output data that forms the basis for subsequent processes.

[0577] Step 2:

[0578] The server uses data processing tools to analyze the input information and select the most suitable subjects by referring to past data and the attributes of the survey subjects. The server lists the selection results and outputs them as a subject list. This list includes identification information of the candidates who will be surveyed.

[0579] Step 3:

[0580] The server uses a time management system to automatically set the schedules of the selected participants. The input data includes a list of participants and date / time setting conditions. The server adjusts the dates and times to finalize the interview schedule for each participant. This schedule information becomes the output data and forms the basis for notifications.

[0581] Step 4:

[0582] The server uses a communication method to send interview invitation notifications to the target individuals. The notification control system manages the responses from the targets and readjusts the schedule if necessary. The input is the confirmed interview schedule, and the output is the notification sending status.

[0583] Step 5:

[0584] During the interview, the device initiates an online chat or video call via a dialogue method. The generative AI model uses natural language processing to collect responses and manage the conversation. The input data is the consumer's real-time responses, and the output is a record of the interview content.

[0585] Step 6:

[0586] The terminal transmits received voice and text data to an emotion recognition system, which then uses this data to perform real-time emotion analysis. The emotion recognition engine identifies the emotion information and structures the data for analysis. The output is a set of emotion data.

[0587] Step 7:

[0588] The server uses information evaluation tools to comprehensively analyze the collected interview content and sentiment data. The input data consists of interview records and sentiment data, and the server outputs consumer insights and recommendations based on the analysis. This forms the basis of the automatically generated research report.

[0589] Step 8:

[0590] The server generates the final research report and provides it to the user as a PDF or online dashboard. The input data is the analysis results, and the output is in the form of a research report for the user.

[0591] (Application Example 2)

[0592] 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."

[0593] In today's advertising market, accurately understanding consumer emotions and dynamically optimizing advertising strategies based on them is essential. However, conventional technologies struggle to analyze emotions in real time and adjust advertising based on that information. To solve this problem, an efficient system is needed that recognizes emotions and reflects them in advertising.

[0594] 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.

[0595] In this invention, the server includes behavioral analysis means for selecting a group of subjects to be surveyed, time management means for automatically setting the date and time of interviews with the selected subjects, real-time communication means for conducting interviews via online chat or video communication, and emotion analysis means for recognizing emotions and adjusting advertising content. This makes it possible to optimize advertising content in real time based on consumers' emotions.

[0596] "Behavioral analysis methods" refer to technical methods for analyzing consumer behavior in order to select a target group for a study.

[0597] "Time management means" refers to a technical method for automatically setting interview dates and times for selected research subjects.

[0598] "Real-time communication means" refers to communication technology used to conduct interviews in real time via online chat or video communication.

[0599] "Analysis and evaluation means" refers to a technical method for analyzing interview results and generating documents.

[0600] "Emotional analysis techniques" are technologies that recognize consumers' emotions and dynamically adjust advertising content based on that information.

[0601] To realize this invention, a server, terminal, and user cooperate to run the system. The server analyzes consumer behavior using behavioral analysis means and selects the target group for the study. A high-performance computer is used as the hardware, and behavioral analysis algorithms are installed as the software. After the selection is complete, the date and time of the interviews are automatically set by a time management means, and a schedule is created.

[0602] The terminal conducts interviews via online chat or video communication using real-time communication methods. A real-time communication platform via the internet is used for communication. This utilizes terminals equipped with high-resolution cameras and microphones.

[0603] During the interview, the built-in emotion analysis system recognizes the consumer's emotions in real time and immediately sends the results to the server. This data is analyzed by an analysis and evaluation system to generate insights for adjusting advertising content. The emotion analysis technology used here includes, for example, the Azure Emotion API and similar emotion recognition software.

[0604] Finally, the generative AI model dynamically generates ad content based on the analysis results, delivering ads optimized for the user. For example, if a consumer is excited after seeing an ad for a new car, the generative AI can use this emotional information to suggest additional details about the car's performance and design. An example of a prompt might be, "If a user frowns while watching a car ad, what additional information would be most appropriate?"

[0605] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0606] Step 1:

[0607] The server receives market information provided by users. Based on this, it selects research groups using behavioral analysis tools. The input is market information, and the output is a list of selected research groups. As part of data processing, consumer behavior patterns are analyzed and appropriate targets are selected.

[0608] Step 2:

[0609] The server automatically schedules interviews for selected research subjects and creates a schedule using a time management system. The input is a list of subjects, and the output is schedule information. The scheduling algorithm takes into account consumers' availability and suggests the optimal date and time.

[0610] Step 3:

[0611] The terminal initiates an online chat or video communication via an instant communication method to conduct an interview. Inputs are the interview question list and consumer responses, while output is the completed interview data. The terminal transmits and receives video and audio in real time and records consumer responses.

[0612] Step 4:

[0613] The emotion analysis system installed in the terminal collects emotion data from the consumer's facial expressions and voice during the interview and analyzes it in real time. The input is voice and video data, and the output is emotion data. Emotion recognition software performs the specific actions of classifying the consumer's emotional state.

[0614] Step 5:

[0615] The server integrates emotional data and interview results using analytical evaluation tools to perform analysis aimed at extracting deep insights. The input is emotional data and interview results, and the output is an analytical report. Using a generative AI model, the system meticulously evaluates consumer responses based on the obtained data and identifies areas for improvement in advertising content.

[0616] Step 6:

[0617] The server utilizes a generative AI model to generate ad content based on analysis results, optimize it for the user, and deliver it to them. The input is the analysis report, and the output is the optimized ad content. Using prompts, the AI ​​automatically generates effective ad copy and visuals.

[0618] 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.

[0619] 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.

[0620] 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.

[0621] [Fourth Embodiment]

[0622] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0623] 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.

[0624] 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).

[0625] 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.

[0626] 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.

[0627] 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).

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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.

[0632] 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.

[0633] 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.

[0634] 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".

[0635] This invention relates to a system that streamlines the entire market research process using a generative AI agent. This system can handle everything from selecting research subjects to conducting interviews and analyzing the results.

[0636] First, the user inputs information about the research objectives and target market into the system. This information includes product categories, target age groups, and regions. Based on the input information, the server uses behavioral analysis tools to select appropriate research groups. This selection is based on historical behavioral data and real-time market trend data.

[0637] Next, the server automatically sends interview invitations to the selected participants via a scheduling mechanism. Notifications are sent via email or SMS, and the system also includes a function to manage consumer responses.

[0638] When the scheduled interview time arrives, the device initiates the interview session. The interview is then conducted via online chat or video call using real-time communication methods. A generative AI agent utilizes natural language processing to interact with the consumer and manage the progress of the interview.

[0639] The data collected during interviews is analyzed on a server using analytical and evaluation tools. The analysis extracts key phrases and topics from the response data to capture trends in consumer sentiment and opinions. Based on the analysis results, a detailed research report is generated. This report is provided to the user in PDF file or online dashboard format.

[0640] As a concrete example, in market research for a new fashion product targeting women in their 20s, the system automatically selects the appropriate target group and conducts online interviews with 500 consumers. This allows for rapid acquisition of highly accurate insights, which are then used in product development and marketing strategies.

[0641] This invention makes it possible to significantly reduce the cost and time of market research and obtain highly accurate research results.

[0642] The following describes the processing flow.

[0643] Step 1:

[0644] The user enters the objectives of the market research and the target market information into the system. This stage includes information on the target product category and consumer attributes (age, gender, region, etc.).

[0645] Step 2:

[0646] The server uses behavioral analysis tools based on the input market information to select the target group for the survey. This includes matching consumer databases with real-time market trend data.

[0647] Step 3:

[0648] The server automatically sends interview invitations to selected research participants using a scheduling mechanism. Notifications are sent via email or SMS, and responses and participation confirmations from consumers are managed.

[0649] Step 4:

[0650] The device initiates the interview session according to the set date and time. The interview begins via online chat or video call using a real-time communication method.

[0651] Step 5:

[0652] A generative AI agent interacts with consumers through natural language processing and manages the progress of the interview. It appropriately adjusts pre-set questions as needed to efficiently collect consumer responses.

[0653] Step 6:

[0654] The device records the interview responses in a database. The acquired data is saved in audio or text format for later analysis.

[0655] Step 7:

[0656] The server analyzes the collected data using analytical and evaluation tools. This involves extracting key topics and performing sentiment analysis based on consumer responses.

[0657] Step 8:

[0658] The server automatically generates a research report based on the analysis results. This report includes consumer insights and recommendations obtained.

[0659] Step 9:

[0660] The server provides the generated report to the user. The report can be viewed as a PDF file or in online dashboard format.

[0661] (Example 1)

[0662] 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".

[0663] The market research process requires significant time and resources, from selecting research groups and preparing and conducting interviews to analyzing the results. Traditional methods are inefficient, leading to increased costs and challenges in the accuracy of the results.

[0664] 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.

[0665] In this invention, the server includes data processing means, planning means, and communication control means. This enables efficient selection of the target group for investigation, automated interview execution, and rapid data analysis.

[0666] "Data processing means" refers to a device or system that collects and analyzes data necessary for appropriately selecting the target group for investigation.

[0667] A "plan setting means" is a device or process for automatically setting the selected survey target and the date and time of the survey.

[0668] "Communication control means" refers to a device or software for conducting interviews via digital communication.

[0669] "Information analysis means" refers to a device or function for analyzing interview results and generating a detailed report.

[0670] A "notification management system" is a device or process that notifies selected survey subjects about the survey and manages their responses.

[0671] A "dialogue management device" is a device or function that manages the progress of an interview conducted via digital communication using language analysis technology.

[0672] This invention relates to a system that streamlines the market research process using a generative AI agent. This system can handle everything from selecting the target group to conducting interviews and analyzing the results.

[0673] First, the user enters their market research objectives and information into the system. This information includes product category, age group, and region. The user enters and submits the information through an input form via a web browser.

[0674] Next, the server uses data processing tools to analyze the information sent by the user. Here, it leverages Google Cloud's database service to search for and analyze past consumer behavior data, using data analysis libraries such as Pandas. This data processing makes it possible to identify the target group for the survey.

[0675] Subsequently, the server uses SMS and email services to send the interview date, time, and participation link to the selected participants as a scheduling method. Specifically, it uses the Twilio API and SendGrid service. The message sent will include a confirmation link for participation in the interview, and participants will confirm their participation by clicking the key.

[0676] When the scheduled interview time arrives, the device uses communication control to automatically initiate the interview via a digital communication platform such as Zoom or Microsoft Teams. The interview is then conducted using natural language processing with a generative AI agent (e.g., a large-scale language model).

[0677] The data collected during interviews is stored on a server and analyzed using data analysis tools. Text data is analyzed using Python natural language processing libraries (such as spaCy and VADER), and responses are summarized and sentiment analyzed. Based on the results, a survey report is automatically generated. This report is created in HTML format using a Jinja template and exported as a PDF file. It is also provided as an online dashboard via Power BI, which users can view through a web browser.

[0678] As a concrete example, in a market research project for a new fashion product targeting women in their 20s, the system conducts online interviews with 500 consumers to provide rapid and highly accurate insights. An example of a prompt message could be, "Generate an interview script to investigate consumer opinions in the market for a new fashion accessory targeting women in their 20s."

[0679] This system allows users to significantly reduce the cost and time of market research and obtain more accurate research results.

[0680] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0681] Step 1:

[0682] Users input market research objectives and information into the system. This input includes information such as product category, target age group, and region. This input data is sent to the server via a web browser form. The server receives this data and stores it in its database.

[0683] Step 2:

[0684] The server uses data processing tools to analyze information received from users. Here, historical consumer behavior data and real-time market data are aggregated and analyzed. Specifically, queries are executed using a database service, and the data is stored in a data frame using Pandas. This generates profiles of the surveyed group.

[0685] Step 3:

[0686] The server sends the interview date and time, along with a participation link, to the selected research participants via the planning mechanism. The input includes a list of selected research participants and their contact information, which is used to send notifications via the Twilio API or email service. The output is a log of the notification transmissions.

[0687] Step 4:

[0688] When the interview date and time arrives, the device establishes digital communication using communication control means. It sets up a video conference using a platform such as Zoom or Microsoft Teams and starts the interview session via a generative AI model. The device verifies participants, monitors connection status, and troubleshoots as needed.

[0689] Step 5:

[0690] The server analyzes data collected through interviews. The input is interview data expressed in natural language, and sentiment analysis and key phrase extraction are performed using Python's NLP library. The analysis results are output as statistical data and used for report generation.

[0691] Step 6:

[0692] The server uses data analysis tools to generate a report based on the analysis results. This involves creating an HTML summary using a Jinja template and exporting it as a PDF file. It is also provided as an online dashboard via Power BI. Finally, users can view and download these results from a web browser.

[0693] (Application Example 1)

[0694] 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".

[0695] Traditional market research processes have been plagued by significant time and cost involved in selecting research subjects, conducting interviews, and analyzing results. Furthermore, it was difficult to appropriately select interview participants and collect feedback quickly and effectively.

[0696] 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.

[0697] In this invention, the server includes processing means for selecting research subjects and analyzing behavioral data; system means for automatically scheduling and inviting interviews with selected research subjects; digital dialogue means for conducting interviews and communicating in real time; data processing means for analyzing consumer opinions and generating reports; and response processing means for providing research-related feedback in real time via an online platform. This enables increased efficiency and rapid data collection and analysis throughout the entire market research process.

[0698] "Survey subjects" refer to individual participants selected as information providers whose information aligns with the objectives of the market research.

[0699] "Behavioral data" refers to data that includes consumers' past purchase history and online behavior history, and is used to analyze consumer behavior patterns.

[0700] "Processing means" refers to computational and algorithmic methods used to analyze digital information and perform specific tasks.

[0701] "Scheduling" is the process of systematically setting the date and time for specific events or tasks.

[0702] A "system means" refers to an integrated technological system, such as a combination of software and hardware, built to achieve a specific purpose.

[0703] "Digital dialogue means" refers to technological means used to conduct real-time voice or text-based dialogues via the internet.

[0704] "Data processing means" refers to technical means that support the process of analyzing and evaluating collected information and generating insights and reports based on that analysis.

[0705] A "response processing means" is a function that immediately processes feedback information obtained through an online platform and provides it in a format useful for research.

[0706] The system for implementing this invention is realized through the coordinated operation of a server and a terminal. The server first receives the market research objectives and target information entered by the user, and then analyzes the data based on this information. The analysis uses behavioral data, including past purchase history and consumer online behavior data. Based on this, the system uses machine learning frameworks such as Python and scikit-learn to select the most suitable research targets.

[0707] The server schedules interviews with selected research participants. AWS Lambda is used for scheduling, automatically sending out research invitations via email and SMS. This ensures efficient scheduling of participants.

[0708] Next, the device takes on the role of conducting the interview at the designated date and time. The digital dialogue is conducted using real-time communication platforms such as Zoom and Google Meet, and natural language processing powered by generative AI models is used to ensure the interview proceeds smoothly. The generative AI model automatically generates phrases and questions in accordance with the progress of the conversation, helping the interviewer to conduct question-and-answer sessions smoothly.

[0709] After the interview concludes, the server analyzes the collected data and generates a detailed report using ReportLab. The report is provided in PDF format, allowing users to gain in-depth insights tailored to their specific needs. Users can also receive real-time feedback via an online dashboard.

[0710] As a concrete example, when conducting market research on a new health food product targeting women in their 20s, the following prompt would be used:

[0711] "Target age group: 20s, Category: Health foods, Region: Tokyo. Please select survey participants and schedule interviews based on their past purchase history."

[0712] This format enables rapid and accurate market research, allowing companies to develop effective marketing strategies.

[0713] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0714] Step 1:

[0715] The server receives data from the user regarding the purpose and target of the market research. This input data includes details such as the target age group, product category, and region. Based on this information, the server collects relevant behavioral history and market trend data from the database to form an analysis platform.

[0716] Step 2:

[0717] The server uses the collected user behavior data to process it for selecting research subjects. This process employs machine learning algorithms such as scikit-learn to select the appropriate target group. As a result, a list of selected research subjects is output.

[0718] Step 3:

[0719] The server automatically schedules interviews with selected participants and sends invitations via email or SMS. Inputs include a list of selected participants and available time slots. AWS Lambda is used to automate this task, and the output provides confirmed interview dates and times.

[0720] Step 4:

[0721] When the interview date and time arrive, the device initiates the interview via a real-time communication platform such as Zoom. Here, a generative AI model is used to perform natural language processing and conduct the dialogue. The input is the content of the chat or call with the interviewee, and the output is a real-time generated interview script and new questions.

[0722] Step 5:

[0723] After the interview is complete, the server analyzes the collected data. The input data consists of responses and observations gathered during the interview. Based on this, an analysis model built in Python and ReportLab are used to generate a report showing consumer opinions and sentiment trends. The output is a detailed report in PDF format.

[0724] Step 6:

[0725] Users view reports generated through an online dashboard and develop marketing strategies based on the insights gained. The input is the generated report data, and the output is an action plan for strategy development.

[0726] 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.

[0727] This invention combines a market research system utilizing a generative AI agent with an emotion engine that recognizes user emotions. This system streamlines all processes from consumer interview selection and execution to analysis and report generation, and further provides emotion-based insights.

[0728] First, the user inputs information about the purpose of the market research and the target market into the system. Based on this, the server uses behavioral analysis tools to select an appropriate group of research participants. For the selected participants, the server automatically sets the date and time for interview invitations using scheduling tools, sends notifications, and manages responses.

[0729] During the interview phase, the device initiates a session via online chat or video call. A generative AI agent uses natural language processing to conduct the interview and collect responses from consumers. Additionally, an emotion engine identifies the user's emotions in real time, which is used to dynamically adjust the interview process and questions.

[0730] Emotional data collected during interviews is sent to a server along with the interview results and analyzed by analytical evaluation tools. Based on the emotional trend analysis provided by the emotion engine, a deeper understanding of the nuances of consumer feedback is achieved, leading to more accurate insights. Based on these analysis results, a detailed research report is automatically generated. This report includes key topics, emotional trends, and recommended actions, and is provided to the user in PDF or online dashboard format.

[0731] As a concrete example, in market research for a new healthcare product, the system automatically selects consumers and conducts interviews with approximately 100 people. Each interview is conducted by a generative AI agent, and an emotion engine analyzes consumer responses to uncover potential dissatisfactions and expectations regarding the product. This information is useful for strategically improving the product.

[0732] This invention enables sophisticated market research that takes consumer sentiment into account, improving the reliability and accuracy of research results.

[0733] The following describes the processing flow.

[0734] Step 1:

[0735] The user inputs information about the research objectives and target market into the system. This information includes the target product, consumer attributes, and the main areas of interest in the research.

[0736] Step 2:

[0737] The server uses behavioral analysis tools based on the input information, referencing historical data and real-time market trends to select an appropriate target group for the survey.

[0738] Step 3:

[0739] The server uses a scheduling mechanism to set interview dates and times for selected participants. Interview invitations are automatically sent via email or SMS, and responses are tracked and managed.

[0740] Step 4:

[0741] The device initiates an online chat or video call interview session at the scheduled date and time. A generative AI agent participates in the interview and leads the conversation.

[0742] Step 5:

[0743] A generative AI agent conducts the interview while collecting consumer responses in real time using natural language processing. Pre-set questions are used, but they are flexibly adjusted based on consumer responses.

[0744] Step 6:

[0745] The emotion engine identifies emotions from consumers' statements, facial expressions, and tone of voice during interviews. This allows the system to understand consumers' emotional responses and dynamically adjust the order and content of interview questions as needed.

[0746] Step 7:

[0747] The device transmits collected response data and sentiment data to the server in real time. This allows all data to be centrally managed in a central database.

[0748] Step 8:

[0749] The server analyzes the collected data using analytical and evaluation tools. This includes identifying key topics and consumer trends, including sentiment analysis using an emotion engine.

[0750] Step 9:

[0751] The server automatically generates a detailed research report based on the analysis results. The report includes the discovered insights and sentiment-based recommendations, and is provided to the user in PDF or online dashboard format.

[0752] Step 10:

[0753] Users review the provided reports and use the findings to improve products and develop marketing strategies.

[0754] (Example 2)

[0755] 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".

[0756] In the field of market research, traditional methods suffer from the significant time and effort required for selecting research subjects, conducting interviews, and analyzing results. Furthermore, obtaining detailed insights that take consumer sentiment into account is difficult, leaving challenges to the reliability and accuracy of market research.

[0757] 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.

[0758] In this invention, the server includes data processing means, time management means, dialogue means, information evaluation means, and emotion recognition means. This enables efficient selection of subjects for investigation, automatic date and time setting, real-time dialogue, and detailed data analysis based on emotions.

[0759] "Data processing means" refers to a device or program that performs processing to select subjects for investigation based on collected information.

[0760] A "time management tool" is a device or program that automatically sets the date and time of interviews for selected research subjects and manages their schedules.

[0761] "Dialogue means" refers to a device or program that uses a communication line to conduct dialogue and communicates with the subject of the investigation via online chat or video call.

[0762] An "information evaluation tool" is a device or program for analyzing dialogue results and generating output in the form of a report.

[0763] An "emotion recognition tool" is a device or program used to identify and analyze the emotions of a research subject in real time.

[0764] "Notification control means" refers to a device or program for notifying selected survey subjects and controlling and managing their replies and response status.

[0765] A "dialogue management device" is a device or program that uses natural language processing to manage dialogue over a communication line and enables natural communication with the research subject.

[0766] This invention is a system for efficiently conducting market research, automating the entire process from selecting research subjects and conducting interviews to analyzing results and generating reports. The system functions using a server, terminals, a generative AI model, and an emotion recognition engine.

[0767] The user first uses a device such as a PC or tablet to input information about the purpose of the market research and the target market into the system. This information is sent to a server, where data processing is performed. The data processing consists of software for analyzing past research data and consumer attribute data.

[0768] Next, the server automatically sets the interview date and time through a time management system and sends a notification to the subject using a communication system. At this time, a notification control system manages replies and automatically adjusts the schedule.

[0769] Once the interview begins, the device uses real-time dialogue methods to conduct online chat or video calls. The generative AI model conducts the interview through natural language processing, effectively collecting the interviewee's responses. Furthermore, emotion recognition methods identify emotions in real time from the interviewee's facial expressions and voice, and incorporate the obtained emotion data into the analysis.

[0770] Ultimately, the server comprehensively analyzes interview results and sentiment data using information evaluation tools, and automatically generates a detailed research report based on this analysis. This report is then converted into an easy-to-understand format and provided to the user as a PDF or online dashboard.

[0771] As a concrete example, in market research for a new healthcare product, the system conducts interviews with approximately 100 pre-selected consumers. Each interview is conducted by a generative AI model, and an emotion recognition engine analyzes the consumers' responses to uncover potential dissatisfactions and expectations regarding the product.

[0772] Examples of prompts for generative AI models:

[0773] "We are conducting market research for a new health supplement. Please set up interviews that take into account the health-conscious demographic of people aged 50 and over, and analyze their potential dissatisfactions and expectations."

[0774] In this way, the system streamlines all processes of market research and provides detailed, emotion-based insights, significantly improving the reliability and accuracy of research results.

[0775] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0776] Step 1:

[0777] The user enters information about their objectives and the target market into the terminal. This becomes the input data. The information includes the purpose of the research, details of the target market, and the insights they seek. The server organizes the received information and stores it in a database. This is the output data that forms the basis for subsequent processes.

[0778] Step 2:

[0779] The server uses data processing tools to analyze the input information and select the most suitable subjects by referring to past data and the attributes of the survey subjects. The server lists the selection results and outputs them as a subject list. This list includes identification information of the candidates who will be surveyed.

[0780] Step 3:

[0781] The server uses a time management system to automatically set the schedules of the selected participants. The input data includes a list of participants and date / time setting conditions. The server adjusts the dates and times to finalize the interview schedule for each participant. This schedule information becomes the output data and forms the basis for notifications.

[0782] Step 4:

[0783] The server uses a communication method to send interview invitation notifications to the target individuals. The notification control system manages the responses from the targets and readjusts the schedule if necessary. The input is the confirmed interview schedule, and the output is the notification sending status.

[0784] Step 5:

[0785] During the interview, the device initiates an online chat or video call via a dialogue method. The generative AI model uses natural language processing to collect responses and manage the conversation. The input data is the consumer's real-time responses, and the output is a record of the interview content.

[0786] Step 6:

[0787] The terminal transmits received voice and text data to an emotion recognition system, which then uses this data to perform real-time emotion analysis. The emotion recognition engine identifies the emotion information and structures the data for analysis. The output is a set of emotion data.

[0788] Step 7:

[0789] The server uses information evaluation tools to comprehensively analyze the collected interview content and sentiment data. The input data consists of interview records and sentiment data, and the server outputs consumer insights and recommendations based on the analysis. This forms the basis of the automatically generated research report.

[0790] Step 8:

[0791] The server generates the final research report and provides it to the user as a PDF or online dashboard. The input data is the analysis results, and the output is in the form of a research report for the user.

[0792] (Application Example 2)

[0793] 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".

[0794] In today's advertising market, accurately understanding consumer emotions and dynamically optimizing advertising strategies based on them is essential. However, conventional technologies struggle to analyze emotions in real time and adjust advertising based on that information. To solve this problem, an efficient system is needed that recognizes emotions and reflects them in advertising.

[0795] 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.

[0796] In this invention, the server includes behavioral analysis means for selecting a group of subjects to be surveyed, time management means for automatically setting the date and time of interviews with the selected subjects, real-time communication means for conducting interviews via online chat or video communication, and emotion analysis means for recognizing emotions and adjusting advertising content. This makes it possible to optimize advertising content in real time based on consumers' emotions.

[0797] "Behavioral analysis methods" refer to technical methods for analyzing consumer behavior in order to select a target group for a study.

[0798] "Time management means" refers to a technical method for automatically setting interview dates and times for selected research subjects.

[0799] "Real-time communication means" refers to communication technology used to conduct interviews in real time via online chat or video communication.

[0800] "Analysis and evaluation means" refers to a technical method for analyzing interview results and generating documents.

[0801] "Emotional analysis techniques" are technologies that recognize consumers' emotions and dynamically adjust advertising content based on that information.

[0802] To realize this invention, a server, terminal, and user cooperate to run the system. The server analyzes consumer behavior using behavioral analysis means and selects the target group for the study. A high-performance computer is used as the hardware, and behavioral analysis algorithms are installed as the software. After the selection is complete, the date and time of the interviews are automatically set by a time management means, and a schedule is created.

[0803] The terminal conducts interviews via online chat or video communication using real-time communication methods. A real-time communication platform via the internet is used for communication. This utilizes terminals equipped with high-resolution cameras and microphones.

[0804] During the interview, the built-in emotion analysis system recognizes the consumer's emotions in real time and immediately sends the results to the server. This data is analyzed by an analysis and evaluation system to generate insights for adjusting advertising content. The emotion analysis technology used here includes, for example, the Azure Emotion API and similar emotion recognition software.

[0805] Finally, the generative AI model dynamically generates ad content based on the analysis results, delivering ads optimized for the user. For example, if a consumer is excited after seeing an ad for a new car, the generative AI can use this emotional information to suggest additional details about the car's performance and design. An example of a prompt might be, "If a user frowns while watching a car ad, what additional information would be most appropriate?"

[0806] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0807] Step 1:

[0808] The server receives market information provided by users. Based on this, it selects research groups using behavioral analysis tools. The input is market information, and the output is a list of selected research groups. As part of data processing, consumer behavior patterns are analyzed and appropriate targets are selected.

[0809] Step 2:

[0810] The server automatically schedules interviews for selected research subjects and creates a schedule using a time management system. The input is a list of subjects, and the output is schedule information. The scheduling algorithm takes into account consumers' availability and suggests the optimal date and time.

[0811] Step 3:

[0812] The terminal initiates an online chat or video communication via an instant communication method to conduct an interview. Inputs are the interview question list and consumer responses, while output is the completed interview data. The terminal transmits and receives video and audio in real time and records consumer responses.

[0813] Step 4:

[0814] The emotion analysis system installed in the terminal collects emotion data from the consumer's facial expressions and voice during the interview and analyzes it in real time. The input is voice and video data, and the output is emotion data. Emotion recognition software performs the specific actions of classifying the consumer's emotional state.

[0815] Step 5:

[0816] The server integrates emotional data and interview results using analytical evaluation tools to perform analysis aimed at extracting deep insights. The input is emotional data and interview results, and the output is an analytical report. Using a generative AI model, the system meticulously evaluates consumer responses based on the obtained data and identifies areas for improvement in advertising content.

[0817] Step 6:

[0818] The server utilizes a generative AI model to generate ad content based on analysis results, optimize it for the user, and deliver it to them. The input is the analysis report, and the output is the optimized ad content. Using prompts, the AI ​​automatically generates effective ad copy and visuals.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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."

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] The following is further disclosed regarding the embodiments described above.

[0841] (Claim 1)

[0842] Behavioral analysis methods for selecting the target group for the study,

[0843] A scheduling method that automatically sets the date and time of the selected research subjects and interviews,

[0844] Real-time communication methods for conducting interviews via online chat or video call,

[0845] An analytical and evaluation method for analyzing interview results and generating reports,

[0846] A system that includes this.

[0847] (Claim 2)

[0848] The system according to claim 1, further comprising a notification management means for notifying selected survey subjects and managing their responses.

[0849] (Claim 3)

[0850] The system according to claim 1, further comprising an interview management means for conducting interviews conducted via online chat or video call using natural language processing.

[0851] "Example 1"

[0852] (Claim 1)

[0853] A data processing method for selecting the target group for the study,

[0854] A planning mechanism that automatically sets the selected survey subjects and the date and time of the survey,

[0855] A communication control means for conducting interviews via digital communication,

[0856] Information analysis means for analyzing interview results and generating reports,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, further comprising a notification management means for notifying selected survey subjects and managing their responses.

[0860] (Claim 3)

[0861] The system according to claim 1, further comprising a dialogue management means for conducting interviews via digital communication using language analysis.

[0862] "Application Example 1"

[0863] (Claim 1)

[0864] A means for selecting survey subjects and processing behavioral data,

[0865] A system for automatically scheduling and inviting interviews to selected research participants,

[0866] A digital dialogue tool for conducting interviews and communicating in real time,

[0867] A data processing method for analyzing consumer opinions and generating reports,

[0868] A response processing means that provides real-time feedback related to the survey via an online platform,

[0869] A system that includes this.

[0870] (Claim 2)

[0871] The system according to claim 1, further comprising interaction management means for providing individual survey invitations to selected survey participants and facilitating a personalized survey experience.

[0872] (Claim 3)

[0873] The system according to claim 1, further comprising an algorithm processing means for selecting survey subjects based on consumers' purchase history and access history.

[0874] "Example 2 of combining an emotion engine"

[0875] (Claim 1)

[0876] A data processing method for selecting survey subjects,

[0877] A time management system for automatically scheduling dates for selected research subjects,

[0878] A means of dialogue that conducts dialogue via a communication line,

[0879] Information evaluation means for analyzing dialogue results and generating reports,

[0880] A means of recognizing emotions for identifying and analyzing them,

[0881] A system that includes this.

[0882] (Claim 2)

[0883] The system according to claim 1, further comprising notification control means for notifying selected survey subjects and controlling their responses.

[0884] (Claim 3)

[0885] The system according to claim 1, further comprising dialogue management means for managing dialogue using a communication line by natural language processing.

[0886] "Application example 2 when combining with an emotional engine"

[0887] (Claim 1)

[0888] Behavioral analysis methods for selecting the target group for the study,

[0889] A time management system that automatically sets the date and time for selected research subjects and interviews,

[0890] An immediate communication method for conducting interviews via online chat or video communication,

[0891] An analytical and evaluation means for analyzing interview results and generating documents,

[0892] A means of sentiment analysis for recognizing emotions and adjusting advertising content,

[0893] A system that includes this.

[0894] (Claim 2)

[0895] The system according to claim 1, further comprising a notification management means for notifying selected survey subjects and processing replies.

[0896] (Claim 3)

[0897] The system according to claim 1, further comprising a dialogue management means for conducting an interview via online chat or video communication using natural language processing. [Explanation of Symbols]

[0898] 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. Behavioral analysis methods for selecting the target group for the study, A scheduling method that automatically sets the date and time of the selected research subjects and interviews, Real-time communication methods for conducting interviews via online chat or video call, An analytical and evaluation method for analyzing interview results and generating reports, A system that includes this.

2. The system according to claim 1, further comprising a notification management means for notifying selected survey subjects and managing their responses.

3. The system according to claim 1, further comprising an interview management means for conducting interviews conducted via online chat or video call using natural language processing.

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