system

Abstract

Provide a system. 【Solution means】 Input means for receiving the survey purpose and target from the user, Generation means for automatically generating questions based on the survey purpose and target, Display means for displaying the generated questions to the user and making them editable, Collection means for collecting data based on the questions confirmed by the user, Analysis means for aggregating and analyzing the collected data in real time, Visualization means for visualizing the analysis results and providing them to the user, Output means for automatically generating the final report, A system including.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern research work, a great deal of time and resources are required for creating questionnaires and aggregating and analyzing data, so there is a strong demand for improving work efficiency. Also, it is difficult to visualize the collected data in a form that is easy to understand quickly and accurately, which causes a problem that the decision-making process is delayed. To address these issues, a system that automates each stage of the survey and can effectively utilize data is needed.

Means for Solving the Problems

[0005] This invention streamlines the questionnaire creation process by providing a generation means that automatically generates questions tailored to the research objectives and target audience based on user input. Furthermore, a display means that allows the generated questions to be displayed and edited enables users to design flexible surveys. In addition, it aggregates and analyzes collected data in real time, and enables intuitive viewing of results using visualization means. This supports rapid and accurate data-driven decision-making. Finally, by automatically outputting reports based on the analysis results, the entire research process can be consistently streamlined.

[0006] "User" refers to the entity that operates the system and conducts the survey.

[0007] "Input method" refers to the interface that allows users to input research objectives and subjects into the system.

[0008] "Generation method" refers to a function that automatically generates appropriate questions based on information entered by the user.

[0009] "Display means" refers to an interface that presents the generated questions to the user and allows them to edit them as needed.

[0010] "Data collection means" refers to the function that acquires data based on questions answered by the user and imports it into the system.

[0011] "Analysis tools" refer to functions that aggregate and analyze collected data in real time.

[0012] "Visualization means" refers to a function that visually displays analysis results in a way that is easy for users to understand.

[0013] "Output method" refers to a function that automatically generates a final report based on the analyzed data and provides it to the user. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0015] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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."

[0022] [First Embodiment]

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

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0029] 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.

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

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention provides a system that supports everything from the automatic generation of questionnaires to the analysis and visualization of data. Specific embodiments are described below.

[0036] This system provides an interface via the terminal when the user inputs the research objectives and target audience. The user can intuitively input research details into this interface. For example, they can input information about the objectives and target audience of a market research project for a new product.

[0037] The entered information is sent to the server, which uses a generation mechanism to automatically generate questions suitable for the user's purpose. This generation utilizes past survey data and statistical models to create specific and effective questions that match the objective.

[0038] The generated questions are displayed to the user via their device, and the user reviews them. The user can edit the displayed questions as needed, adjusting them to best suit their research needs. This flexibility allows for the creation of questionnaires optimized for specific needs.

[0039] Next, once the user has finished reviewing and editing the questions, the survey is conducted and data is collected through the collection methods. By receiving responses through digital forms and online platforms, the data is quickly transmitted to the server.

[0040] The server aggregates and analyzes the received data in real time using analytical tools. The analysis results are provided through visualization tools in visually easy-to-understand formats such as bar graphs and line graphs. This allows users to easily grasp data trends and important insights.

[0041] Ultimately, the server automatically generates a report based on the visualized analysis results and outputs it to the user's terminal. This report includes statistical analysis results and insights that users can use to make business decisions and formulate strategies.

[0042] In this way, the system streamlines the entire research process and enables high-quality data analysis, thereby strongly supporting users' decision-making.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user uses the terminal interface to input the purpose of the survey and information about the participants. Once input is complete, this information is sent to the server.

[0046] Step 2:

[0047] The server automatically generates appropriate questions using a generation mechanism based on the received information. A generation model is utilized to construct questions that are optimal for the target audience and purpose.

[0048] Step 3:

[0049] The server sends the generated questions to the terminal. The terminal displays the questions to the user, who then reviews them. The user edits the questions as needed and finalizes the questionnaire.

[0050] Step 4:

[0051] The user begins the survey using a finalized questionnaire. They distribute the questionnaires to the target audience and collect their responses.

[0052] Step 5:

[0053] The device transfers the collected response data to the server in real time. During this process, the data is automatically checked for any errors.

[0054] Step 6:

[0055] The server aggregates the collected data using analytical tools and performs necessary statistical analysis. This analysis includes basic statistics as well as relationship assessments.

[0056] Step 7:

[0057] The server processes the analysis results in a graphical format using visualization tools, making them easily understandable to the user.

[0058] Step 8:

[0059] The server automatically generates a report based on the visualized analytical data. This report includes detailed analysis and insights that users can utilize.

[0060] Step 9:

[0061] The terminal displays the generated report to the user. The user uses this report to make business decisions.

[0062] (Example 1)

[0063] 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."

[0064] Traditional market research systems often involved manual question creation and data analysis, resulting in a cumbersome and time-consuming research process. Furthermore, visualizing the resulting statistical information and creating reports was time-consuming, making it difficult for users to quickly obtain the insights necessary for decision-making.

[0065] 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.

[0066] In this invention, the server includes an input means for receiving information from the user regarding the purpose and subject of the survey, a generation means for automatically generating questions using a generative model based on the purpose and subject of the survey, and an analysis means for processing and interpreting the collected information in real time using statistical methods. This streamlines the entire survey process, allowing users to receive high-quality data analysis immediately and enabling rapid decision-making.

[0067] An "input device" is a device that has the function of receiving information from the user regarding the purpose and subject of the survey.

[0068] A "generative model" is an algorithm or process for automatically generating appropriate questions based on the user's research objectives and target audience.

[0069] A "display means" is a device that has the function of presenting the generated questions to the user and allowing them to modify them as needed.

[0070] "Collection means" refers to a device that has the function of collecting information based on questions confirmed by the user.

[0071] An "analysis tool" is a device that has the function of processing collected information in real time and interpreting it using statistical methods.

[0072] A "visualization means" is a device that has the function of visually representing and providing processing results to the user.

[0073] "Output means" refers to a device that has the function of automatically generating a final report and providing it to the user.

[0074] This invention is a system that provides high-quality data analysis through the automation of the survey process. Specifically, when a user inputs information about the purpose and target of a survey using a terminal, the terminal sends this information to a server. The server uses a generative AI model to automatically generate questions that are appropriate for the survey purpose specified by the user. This generation process utilizes historical data and statistical methods.

[0075] The generated questions are presented to the user via the terminal, and the user can review and modify them as needed. This process allows the user to obtain questions optimized for their research. Once the research begins, the terminal collects the responses and transfers the data to the server.

[0076] The server analyzes received data in real time, aggregating and interpreting it using statistical methods. The resulting analysis is provided in visual formats such as bar graphs and line graphs, and is automatically generated as an easy-to-understand report for the user. This allows users to quickly obtain insights that directly impact decision-making.

[0077] For example, if a user is conducting market research on health-conscious soft drinks, they would input the objective "Health-conscious market research" and attribute information such as "Target age: 18-34" into the terminal. Based on this information, the server automatically generates and presents the question, "What beverages do you consider drinking when you are health-conscious?"

[0078] An example of a prompt message would be: "Generate market research questions for a new health-conscious beverage."

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

[0080] Step 1:

[0081] The terminal receives information from the user regarding the purpose and target audience of the survey. This input may include, for example, "market research for a new product" or "target audience is women in their 20s." The terminal formats this input and prepares it for transmission to the server.

[0082] Step 2:

[0083] The server receives information sent from the terminal. Based on this input information, it uses a generative AI model to generate appropriate questions. Based on this prompt, the model outputs questions such as "What are the deciding factors for a purchase?". Historical data and statistical models are used for data calculations at this stage.

[0084] Step 3:

[0085] The generated questions are sent from the server to the terminal and displayed to the user. The user reviews the displayed questions and makes modifications or additions as needed. The user then determines the final set of questions, which the terminal sends back to the server.

[0086] Step 4:

[0087] The survey is conducted based on questions confirmed by the user. The device collects responses through online forms and platforms and sends them to the server as a dataset. Each response is quickly organized in digital format.

[0088] Step 5:

[0089] The server receives the collected data and performs analysis in real time. Statistical methods are used to aggregate the data and interpret trends and patterns. This process generates analysis results in a format suitable for visualization.

[0090] Step 6:

[0091] The analysis results are visualized on the server and represented as bar graphs and line graphs. The server automatically generates this visualized data as a report and sends it to the terminal. The terminal displays the report so that the user can review the final report.

[0092] Step 7:

[0093] Users review the final report on their devices and make decisions based on the data. Having gained concrete insights, users can then determine strategies and actions accordingly.

[0094] (Application Example 1)

[0095] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0096] In modern advertising, it is crucial to quickly and accurately measure the effectiveness of advertising on a target market and to develop optimal strategies based on this measurement. However, traditional methods require considerable time and effort for data collection, analysis, and the provision of effective feedback. To address this challenge, there is a need for a system that can analyze advertising effectiveness in real time and propose strategies quickly.

[0097] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0098] In this invention, the server includes input means for receiving information and targets from the user, inquiry generation means for automatically generating questions based on the information and targets, and output means for automatically generating a final report and proposing strategies to improve advertising effectiveness. This enables the user to measure the effectiveness of advertising campaigns in real time and immediately formulate the optimal strategy.

[0099] A "user" is an entity that utilizes a system and provides or adjusts information.

[0100] "Information" refers to the data and instructions necessary for the system to process data, such as the purpose and target of the investigation.

[0101] "Target" refers to the subject matter of the survey or analysis, and means a specific individual, group, or market.

[0102] "Input method" refers to an interface or tool for users to provide information.

[0103] "Inquiry generation means" refers to a process or device that generates relevant questions based on the input information.

[0104] "Presentation method" refers to a function or tool that displays the generated questions to the user and allows them to edit them as needed.

[0105] "Means of information gathering" refers to the methods and techniques used to collect information based on the confirmed questions.

[0106] "Analysis means" refers to a process or device for aggregating and analyzing collected information.

[0107] "Visualization methods" refer to techniques and tools for displaying analysis results in a format that is easy to understand intuitively.

[0108] A "report" refers to a document that includes automatically generated advertising strategy proposals based on analysis and visualization results.

[0109] The system for carrying out this invention includes a user terminal, a cloud-based server, and a network connecting them. The user first uses a smartphone with a dedicated application installed to input information about the research objectives and subjects. The user terminal then transmits this information to the cloud server.

[0110] The server uses an AI model based on the received information to automatically generate appropriate questions. These generated questions are displayed on the user's terminal via an intuitive interface. Users can edit these questions as needed.

[0111] Based on the edited questions, the server initiates the data collection process. This collection method utilizes digital platforms such as online forms, and the collected data is transmitted to the server in real time.

[0112] On the server, the collected data is rapidly analyzed using analytical tools. This analysis is performed using statistical methods and machine learning models, and the results are presented in an easy-to-understand format using visualization tools. Based on the analysis results, the server automatically generates a report that includes strategies for improving advertising effectiveness and outputs it to the user's terminal.

[0113] For example, if a clothing manufacturer launches a new advertising campaign targeting young people, users would input relevant target information into the app. Based on this input, the server generates a prompt message such as, "Generate a questionnaire to investigate the effectiveness of advertising targeting young people." This prompt is then fed into an AI model, which generates a set of relevant questions, enabling rapid campaign effectiveness measurement and strategic adjustment.

[0114] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0115] Step 1:

[0116] Users input information about the survey objectives and target audience via their smartphones. The entered information is then sent directly from the device to the cloud server. At this stage, user input is the primary processing target, and the entered data is used for subsequent question generation.

[0117] Step 2:

[0118] The server automatically generates appropriate questions using a generative AI model based on the information it receives. Specifically, it analyzes the information received from the user and inputs relevant prompts (e.g., "Please generate a questionnaire to investigate the effectiveness of advertising targeting young people.") into the generative AI model. Based on these prompts, the AI ​​model generates a set of questions, which are then output to the server for reception.

[0119] Step 3:

[0120] The generated questions are sent from the server to the user's terminal and displayed to the user through the interface. The user adjusts the details of the survey by editing the displayed questions. The edited questions are sent back to the server. The user's editing actions become input, and the edited questions are output.

[0121] Step 4:

[0122] The server initiates data collection using an online form based on the user's confirmed questions. This collection process receives responses from the target market and aggregates them on the server in real time. The data is stored on the server as specific responses to the questions.

[0123] Step 5:

[0124] The server performs data analysis using statistical methods and machine learning based on the collected data. The input is the collected raw data, and the analysis results in insights into advertising effectiveness and user trends. During this analysis process, data trends and outliers are identified, and information necessary for strategy formulation is generated.

[0125] Step 6:

[0126] The analysis results are visualized and provided to the user's device in an easy-to-understand format (e.g., graphs and charts). Furthermore, the server automatically generates a report based on the visualized information, including strategies to improve advertising effectiveness, and sends this report to the user's device. Based on this report, the user can adjust the direction of their advertising campaign.

[0127] 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.

[0128] This invention provides a system that recognizes user emotions and optimizes the survey process based on them. This system automatically generates questions according to the survey objectives and target audience entered by the user, and further analyzes the user's emotions using an emotion engine, thereby flexibly adjusting the survey experience.

[0129] The terminal provides an interface as an input method when the user starts a survey. When the user enters the survey purpose and target, that information is sent to the server. The server automatically generates appropriate survey questions using a generative model and simultaneously activates an emotion engine to collect the user's emotional data. Emotional data can be acquired through sensors such as cameras and microphones, and the emotional state is recognized based on the analysis of facial expressions and voice.

[0130] The generated questions are displayed on the device, and the user can review and edit them. During this process, the content and difficulty of the questions are adjusted using the results of the emotion engine, optimizing them for the user's current emotional state. For example, if the user is tired, the questions are made simpler; if they are stressed, positive feedback is provided.

[0131] When a survey is conducted, user response data, along with emotional state data, is collected on the server. The server analyzes the collected data in real time and performs additional analysis based on the emotional state. The results of this analysis are visualized using visualization tools as dashboards and graphs and provided to the user.

[0132] The report is automatically generated based on the analysis results and includes sentiment data. This allows users to obtain detailed analysis results that include emotional responses to the survey, leading to deeper insights and understanding. Using this system makes it possible to improve the accuracy of the survey and enhance the user experience.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] The user uses the terminal interface to input the purpose and target information of the survey. The information is sent to the server, and the emotion engine is activated.

[0136] Step 2:

[0137] The server automatically generates questions using a generative model based on the received survey information. It also acquires user emotion data through the camera and microphone, and analyzes the emotional state from facial expressions and voice.

[0138] Step 3:

[0139] The server uses the generated questions and sentiment analysis data to adjust the content and difficulty of the questions. Questions tailored to the user's emotions are then displayed on the terminal.

[0140] Step 4:

[0141] The device presents the user with questions optimized for their emotional state, and the user can review and edit the questions.

[0142] Step 5:

[0143] Once the user confirms the questions, the survey is conducted, and the device transfers the response data and emotional state data to the server in real time. During data transmission, the device checks the integrity of the data.

[0144] Step 6:

[0145] The server aggregates the collected response data and emotional state data using analytical tools and performs additional analysis based on emotions. Trends and correlations are detected.

[0146] Step 7:

[0147] The server visualizes the analysis results and sentiment data and displays them in a dashboard format, making them intuitively understandable to the user.

[0148] Step 8:

[0149] The final report is automatically generated and provided to the user via their device. The report includes insights that reflect sentiment data along with the research findings.

[0150] (Example 2)

[0151] 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".

[0152] The present invention aims to solve the problem of inefficient survey processes that do not take into account the emotional aspects of users that occur in conventional survey systems, and to improve the accuracy of surveys and enhance the survey experience by optimally adjusting survey questions based on the emotional state of the user.

[0153] 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.

[0154] In this invention, the server includes an information input means, a content generation means, and an emotion acquisition means. As a result, questions automatically generated based on the research objective and target are optimized to reflect the user's emotional state, enabling more effective research.

[0155] An "information input means" is an interface for receiving information from the user regarding the purpose and target of the survey and providing it to the system.

[0156] The "content generation means" is a function that automatically creates questions based on the entered survey objectives and target audience, and presents them to the user.

[0157] "Emotion acquisition methods" refer to technologies that collect user facial expressions and voice data through sensors to determine the user's emotional state.

[0158] "Display control means" refers to a function that presents the generated questions to the user, allows the user to review the content, and enables editing as needed.

[0159] An "optimization tool" is a function that adjusts the content and difficulty level of questions to match the user's current emotional state, thereby making the survey process more effective.

[0160] "Information gathering means" refers to a system used to collect data based on survey responses from users and to use it for analysis.

[0161] "Information analysis means" refers to technologies for analyzing collected data and sentiment data in real time and providing it to users.

[0162] A "method display means" is a function that visually displays the analysis results and provides them to the user in a way that is easy to understand.

[0163] "Information output means" refers to technology that automatically generates a detailed report based on the final analysis results and provides it to the user.

[0164] This invention is a system for optimizing survey questions according to the user's emotional state. The system consists of a terminal and a server, and specifically utilizes a generative AI model and emotion recognition technology.

[0165] The terminal provides an interface for users to input research objectives and targets. Once information is entered through this interface, the terminal transmits it to the server. The terminal is also equipped with sensors such as a camera and microphone, allowing it to collect emotional data by capturing the user's facial expressions and voice.

[0166] The server receives information about the survey objectives and target audience from the terminal and automatically generates survey questions using a generative AI model. In this process, the generative AI model constructs appropriate questions based on the input prompt sentences. Furthermore, the server activates an emotion engine and analyzes the emotion data obtained from the terminal. The emotion engine determines the user's emotional state and adjusts the content and difficulty of the questions accordingly.

[0167] The generated questions are sent from the server to the terminal and displayed to the user. The user can review these questions and edit them as needed. After the user completes the survey, the response data and sentiment data collected from the terminal are sent to the server.

[0168] The server integrates this data and performs real-time analysis. This analysis includes additional analysis based on emotional states. The analysis results are visualized and presented to the user in dashboard and graph formats. Finally, the server automatically generates a detailed report based on the analysis results and provides it to the user. This report includes insights, including users' emotional responses to the survey, which can improve the accuracy of the survey.

[0169] For example, if a user wants to conduct a "new product user satisfaction survey," setting the prompt to "generate questions about new product user satisfaction and optimize them using sentiment data" will cause the server to automatically generate the relevant questions and optimize their content according to the user's emotional state. This makes the survey more effective and efficient.

[0170] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0171] Step 1:

[0172] The terminal provides an interface for the user to input the survey objectives and target audience. The user uses this interface to input information about the specific objectives and scope of the survey. The entered data is recorded in text format on the terminal and sent directly to the next processing step.

[0173] Step 2:

[0174] The terminal transmits the entered survey objectives and target information to the server. This transmission takes place in real time over the network, and the server uses the received information as basic data for generating questions in the next step.

[0175] Step 3:

[0176] The server activates a generative AI model based on the received survey objective and target. The generative AI model automatically generates questions using the specified prompt text. Specifically, the generative AI model utilizes previously trained data to construct questions and related answer choices that are appropriate for the survey objective. The generated questions are stored as text data on the server.

[0177] Step 4:

[0178] The device uses sensors such as cameras and microphones to collect emotional data, including the user's facial expressions and voice data. This emotional data is processed in real time within the device, converted into a specific format, and sent to the server.

[0179] Step 5:

[0180] The server activates an emotion engine to analyze the received emotion data. This engine uses facial recognition and speech analysis algorithms to identify the user's emotional state. The emotional state is analyzed as numerical or categorical data and used to adjust the questions.

[0181] Step 6:

[0182] The server optimizes the questions based on the generated questions and the results of the sentiment data analysis. Specifically, it adjusts the content and difficulty of the questions according to the user's emotional state, making them easier for the user to answer. These customized questions are then sent back to the terminal.

[0183] Step 7:

[0184] The terminal displays a modified question to the user and prompts them to answer. The user enters their answers to the questions displayed on the terminal screen, and these answers are temporarily recorded on the terminal. The user's answers are sent to a server for intensive analysis in subsequent steps.

[0185] Step 8:

[0186] The server integrates collected response data and sentiment data and performs advanced analysis in real time. This analysis combines statistical methods with sentiment-based insights, and the results are prepared for display as visualized data.

[0187] Step 9:

[0188] The server visualizes the analysis results as dashboards and graphs, and displays them on the user's device. Through the analysis results, users can understand the full scope of the survey and their emotional responses.

[0189] Step 10:

[0190] The server automatically generates the final report, providing users with detailed insights along with the survey results. This report, including sentiment data obtained during the survey, is sent to the user in a format that allows for easy communication.

[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 customer feedback processes, presenting uniform questions without considering customers' emotional states can lead to low response rates and compromised feedback quality. This makes it difficult for companies to gain detailed insights to improve the customer experience. Furthermore, if the content and difficulty of survey questions are not adapted to customers' current emotional states, satisfaction and accuracy may be compromised.

[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 adjustment means for recognizing the user's emotional state and optimizing the content or difficulty level of questions based on that emotional state; analysis means for aggregating and analyzing the collected information and user emotional state data in real time; and visualization means for visualizing the analysis results and providing them to the user. This makes it possible to provide a feedback process adapted to each customer's emotional state and improve the quality of the customer experience.

[0196] "Input means" refers to a device or interface for receiving information from the user regarding the purpose and target of information collection.

[0197] "Generation means" refers to a device or system that automatically creates appropriate questions based on information provided by the user.

[0198] "Adjustment means" refers to a device or system that has the function of detecting the user's emotional state and optimizing the content and difficulty level of the questions according to that state.

[0199] "Display means" refers to a device or system that shows automatically generated questions to the user and allows them to edit them as needed.

[0200] "Collection means" refers to a device or system that collects information or data based on questions answered by the user.

[0201] "Analysis means" refers to a device or system for analyzing collected information and sentiment data in real time to obtain additional insights.

[0202] "Visualization means" refers to a device or system for visually displaying analysis results and providing them in a format that is easily understandable to the user.

[0203] "Output means" refers to a device or system that automatically generates and provides the final report to the user.

[0204] This invention provides a system that offers an effective information gathering process that takes into account the user's emotional state. The terminal provides an input means for receiving the purpose and target of information gathering from the user. Once the user inputs the purpose and target of the survey, that information is sent to the server. The server creates automatically generated questions using a generative AI model.

[0205] Furthermore, the server uses emotion recognition software to analyze data acquired from the camera and microphone to recognize the user's emotional state. This analysis utilizes emotion recognition libraries such as OpenCV and Affectiva. Based on the collected emotion data, the server adjusts the content and difficulty of the questions, optimizing them to suit the user's current emotional state.

[0206] At this point, the questions are displayed on the terminal, which the user can review and edit as needed. The emotional state data, along with the user's responses, is sent back to the server. The server uses this data to perform real-time analysis and visualize the results. The visualized information is provided to the user as a dashboard and graphs, and a final report is automatically generated. This allows the user to obtain detailed analysis results, including their emotional response to the survey.

[0207] As a concrete example, imagine a scenario where a customer provides feedback through an app after purchasing a new device. The app recognizes their emotional state from their facial expressions and voice, and then presents appropriate questions based on that. For example, a prompt might look like this:

[0208] "Customers are trying to provide feedback immediately after purchasing a product. Recognize their emotions from camera and audio data and detect one of the following: 'Joy,' 'Stress,' or 'Fatigue.' Based on this, generate the following question: 'How happy are you with your new product?' Provide questions that increase the likelihood of a positive response."

[0209] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0210] Step 1:

[0211] The user operates the terminal and inputs the purpose and target of the information to be collected. This information is transmitted to the server via the input device. The input content becomes the basic data when generating questions in the next step.

[0212] Step 2:

[0213] The server uses a generative AI model based on the received information to automatically generate questions suitable for information gathering. At this time, the user's purpose and target information are used as input, and highly relevant questions are created using natural language processing and set as the output.

[0214] Step 3:

[0215] The terminal displays the generated questions on the screen. The user reviews the displayed questions and edits them as needed. The final list of questions is adjusted to meet the latest collection requirements.

[0216] Step 4:

[0217] The device uses a camera and microphone to collect the user's facial expressions and voice data. This data is input into emotion recognition software to recognize the user's emotional state. The output is data indicating the user's current emotional state.

[0218] Step 5:

[0219] The server optimizes the generated questions based on emotional state data. Detailed questions are displayed if the user is agitated, while concise questions are displayed if they are fatigued. The input is emotional state data, and the output is a list of adjusted questions.

[0220] Step 6:

[0221] Users answer optimized questions and send their response data from their device to the server. The responses become crucial data for the next analysis step.

[0222] Step 7:

[0223] The server integrates response data and sentiment data and performs real-time analysis. This process uses statistical and sentiment analysis techniques to extract insights from user responses. The analysis results are output.

[0224] Step 8:

[0225] The server visualizes the analysis results and provides them to the terminal in the form of dashboards and graphs. The results are presented in a way that allows users to intuitively understand the analysis.

[0226] Step 9:

[0227] The server automatically generates a final report. This report includes detailed feedback information, including the results of a user sentiment analysis. The output is the final report that the user can review and evaluate.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] [Second Embodiment]

[0232] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0233] 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.

[0234] 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).

[0235] 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.

[0236] 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.

[0237] 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).

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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".

[0244] This invention provides a system that supports everything from the automatic generation of questionnaires to the analysis and visualization of data. Specific embodiments are described below.

[0245] This system provides an interface via the terminal when the user inputs the research objectives and target audience. The user can intuitively input research details into this interface. For example, they can input information about the objectives and target audience of a market research project for a new product.

[0246] The entered information is sent to the server, which uses a generation mechanism to automatically generate questions suitable for the user's purpose. This generation utilizes past survey data and statistical models to create specific and effective questions that match the objective.

[0247] The generated questions are displayed to the user via their device, and the user reviews them. The user can edit the displayed questions as needed, adjusting them to best suit their research needs. This flexibility allows for the creation of questionnaires optimized for specific needs.

[0248] Next, once the user has finished reviewing and editing the questions, the survey is conducted and data is collected through the collection methods. By receiving responses through digital forms and online platforms, the data is quickly transmitted to the server.

[0249] The server aggregates and analyzes the received data in real time using analytical tools. The analysis results are provided through visualization tools in visually easy-to-understand formats such as bar graphs and line graphs. This allows users to easily grasp data trends and important insights.

[0250] Ultimately, the server automatically generates a report based on the visualized analysis results and outputs it to the user's terminal. This report includes statistical analysis results and insights that users can use to make business decisions and formulate strategies.

[0251] In this way, the system streamlines the entire research process and enables high-quality data analysis, thereby strongly supporting users' decision-making.

[0252] The following describes the processing flow.

[0253] Step 1:

[0254] The user uses the terminal interface to input the purpose of the survey and information about the participants. Once input is complete, this information is sent to the server.

[0255] Step 2:

[0256] The server automatically generates appropriate questions using a generation mechanism based on the received information. A generation model is utilized to construct questions that are optimal for the target audience and purpose.

[0257] Step 3:

[0258] The server sends the generated questions to the terminal. The terminal displays the questions to the user, who then reviews them. The user edits the questions as needed and finalizes the questionnaire.

[0259] Step 4:

[0260] The user begins the survey using a finalized questionnaire. They distribute the questionnaires to the target audience and collect their responses.

[0261] Step 5:

[0262] The device transfers the collected response data to the server in real time. During this process, the data is automatically checked for any errors.

[0263] Step 6:

[0264] The server aggregates the collected data using analytical tools and performs necessary statistical analysis. This analysis includes basic statistics as well as relationship assessments.

[0265] Step 7:

[0266] The server processes the analysis results in a graphical format using visualization tools, making them easily understandable to the user.

[0267] Step 8:

[0268] The server automatically generates a report based on the visualized analytical data. This report includes detailed analysis and insights that users can utilize.

[0269] Step 9:

[0270] The terminal displays the generated report to the user. The user uses this report to make business decisions.

[0271] (Example 1)

[0272] 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."

[0273] Traditional market research systems often involved manual question creation and data analysis, resulting in a cumbersome and time-consuming research process. Furthermore, visualizing the resulting statistical information and creating reports was time-consuming, making it difficult for users to quickly obtain the insights necessary for decision-making.

[0274] 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.

[0275] In this invention, the server includes an input means for receiving information from the user regarding the purpose and subject of the survey, a generation means for automatically generating questions using a generative model based on the purpose and subject of the survey, and an analysis means for processing and interpreting the collected information in real time using statistical methods. This streamlines the entire survey process, allowing users to receive high-quality data analysis immediately and enabling rapid decision-making.

[0276] An "input device" is a device that has the function of receiving information from the user regarding the purpose and subject of the survey.

[0277] A "generation model" is an algorithm or process for automatically generating appropriate questions based on the user's survey objectives and targets.

[0278] A "display means" is a device having a function of presenting the generated questions to the user and making them modifiable as necessary.

[0279] A "collection means" is a device having a function of collecting information based on the questions confirmed by the user.

[0280] An "analysis means" is a device having a function of processing the collected information in real time and interpreting it using statistical methods.

[0281] A "visualization means" is a device having a function of visually representing the processing results and providing them to the user.

[0282] An "output means" is a device having a function of automatically generating a final report and providing it to the user.

[0283] This invention is a system that provides high-quality data analysis through the automation of the survey process. Specifically, when the user inputs information regarding the purpose and target of the survey using a terminal, the terminal transmits this information to the server. The server automatically generates questions suitable for the survey purpose specified by the user using a generation AI model. In this generation process, past data and statistical methods are utilized.

[0284] The generated questions are presented to the user through the terminal, and the user can confirm them and modify them as necessary. Through this process, the user can obtain questions optimized for their own survey. When the survey is started, the terminal collects the answers and transfers the data to the server.

[0285] The server analyzes the received data in real time and performs aggregation and interpretation using statistical methods. The obtained analysis results are provided in a visual form such as a bar graph or a line graph, and are automatically generated as reports that are easy for users to understand. This enables users to quickly obtain insights directly related to decision-making.

[0286] As a specific example, when a user conducts a market survey on health-oriented soft drinks, the user inputs attribute information such as the purpose of "health-oriented market survey" and "target age: 18 - 34" on the terminal. Based on this information, the server automatically generates a question "What beverages do you consider drinking when you are health-conscious?" and presents it to the user.

[0287] As an example of the prompt sentence, a form such as "Please generate questions for a market survey on new health-oriented drinks." can be considered.

[0288] The flow of the specific process in Example 1 will be described using FIG. 11.

[0289] Step 1:

[0290] The terminal receives information about the purpose and target of the survey from the user. This input includes, for example, "market survey of new products" and "target is women in their 20s". The terminal formats these inputs and prepares to send them to the server.

[0291] Step 2:

[0292] The server receives the information sent from the terminal. Based on this input information, it utilizes a generative AI model to generate appropriate questions. Based on this prompt sentence, the model outputs questions such as "What are the decisive factors for purchase?" Past data and statistical models are used for data calculations at this stage.

[0293] Step 3:

[0294] The generated questions are sent from the server to the terminal and displayed to the user. The user reviews the displayed questions and makes modifications or additions as needed. The user then determines the final set of questions, which the terminal sends back to the server.

[0295] Step 4:

[0296] The survey is conducted based on questions confirmed by the user. The device collects responses through online forms and platforms and sends them to the server as a dataset. Each response is quickly organized in digital format.

[0297] Step 5:

[0298] The server receives the collected data and performs analysis in real time. Statistical methods are used to aggregate the data and interpret trends and patterns. This process generates analysis results in a format suitable for visualization.

[0299] Step 6:

[0300] The analysis results are visualized on the server and represented as bar graphs and line graphs. The server automatically generates this visualized data as a report and sends it to the terminal. The terminal displays the report so that the user can review the final report.

[0301] Step 7:

[0302] Users review the final report on their devices and make decisions based on the data. Having gained concrete insights, users can then determine strategies and actions accordingly.

[0303] (Application Example 1)

[0304] 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."

[0305] In modern advertising campaigns, it is important to quickly and accurately measure the effectiveness of advertisements for the target market and formulate an optimal strategy based on this. However, conventional methods have required a great deal of time and effort from data collection to analysis and the provision of effective feedback. To solve this problem, there is a need for a system that analyzes advertising effectiveness in real time and quickly proposes strategies.

[0306] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following means.

[0307] In this invention, the server includes an input means for receiving information and a target from a user, an inquiry generation means for automatically generating an inquiry based on the information and the target, and an output means for automatically generating a final report and proposing a strategy for improving advertising effectiveness. As a result, the user can measure the effectiveness of an advertising campaign in real time and immediately formulate an optimal strategy.

[0308] A "user" is a subject that uses the system and provides information or makes adjustments.

[0309] "Information" refers to data and instructions necessary for the system to perform processing, such as the purpose of investigation and the target.

[0310] "Target" refers to well-known matters for investigation and analysis, and means a specific individual or group, or a market.

[0311] "Input means" refers to an interface or tool for a user to provide information.

[0312] "Inquiry generation means" refers to a process or device that generates related inquiries based on the input information.

[0313] "Presentation means" refers to a function or tool that displays the generated inquiry to the user and allows it to be editable as necessary.

[0314] "Means of information gathering" refers to the methods and techniques used to collect information based on the confirmed questions.

[0315] "Analysis means" refers to a process or device for aggregating and analyzing collected information.

[0316] "Visualization methods" refer to techniques and tools for displaying analysis results in a format that is easy to understand intuitively.

[0317] A "report" refers to a document that includes automatically generated advertising strategy proposals based on analysis and visualization results.

[0318] The system for carrying out this invention includes a user terminal, a cloud-based server, and a network connecting them. The user first uses a smartphone with a dedicated application installed to input information about the research objectives and subjects. The user terminal then transmits this information to the cloud server.

[0319] The server uses an AI model based on the received information to automatically generate appropriate questions. These generated questions are displayed on the user's terminal via an intuitive interface. Users can edit these questions as needed.

[0320] Based on the edited questions, the server initiates the data collection process. This collection method utilizes digital platforms such as online forms, and the collected data is transmitted to the server in real time.

[0321] On the server, the collected data is rapidly analyzed using analytical tools. This analysis is performed using statistical methods and machine learning models, and the results are presented in an easy-to-understand format using visualization tools. Based on the analysis results, the server automatically generates a report that includes strategies for improving advertising effectiveness and outputs it to the user's terminal.

[0322] For example, if a clothing manufacturer launches a new advertising campaign targeting young people, users would input relevant target information into the app. Based on this input, the server generates a prompt message such as, "Generate a questionnaire to investigate the effectiveness of advertising targeting young people." This prompt is then fed into an AI model, which generates a set of relevant questions, enabling rapid campaign effectiveness measurement and strategic adjustment.

[0323] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0324] Step 1:

[0325] Users input information about the survey objectives and target audience via their smartphones. The entered information is then sent directly from the device to the cloud server. At this stage, user input is the primary processing target, and the entered data is used for subsequent question generation.

[0326] Step 2:

[0327] The server automatically generates appropriate questions using a generative AI model based on the information it receives. Specifically, it analyzes the information received from the user and inputs relevant prompts (e.g., "Please generate a questionnaire to investigate the effectiveness of advertising targeting young people.") into the generative AI model. Based on these prompts, the AI ​​model generates a set of questions, which are then output to the server for reception.

[0328] Step 3:

[0329] The generated questions are sent from the server to the user's terminal and displayed to the user through the interface. The user adjusts the details of the survey by editing the displayed questions. The edited questions are sent back to the server. The user's editing actions become input, and the edited questions are output.

[0330] Step 4:

[0331] The server initiates data collection using an online form based on the user's confirmed questions. This collection process receives responses from the target market and aggregates them on the server in real time. The data is stored on the server as specific responses to the questions.

[0332] Step 5:

[0333] The server performs data analysis using statistical methods and machine learning based on the collected data. The input is the collected raw data, and the analysis results in insights into advertising effectiveness and user trends. During this analysis process, data trends and outliers are identified, and information necessary for strategy formulation is generated.

[0334] Step 6:

[0335] The analysis results are visualized and provided to the user's device in an easy-to-understand format (e.g., graphs and charts). Furthermore, the server automatically generates a report based on the visualized information, including strategies to improve advertising effectiveness, and sends this report to the user's device. Based on this report, the user can adjust the direction of their advertising campaign.

[0336] 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.

[0337] This invention provides a system that recognizes user emotions and optimizes the survey process based on them. This system automatically generates questions according to the survey objectives and target audience entered by the user, and further analyzes the user's emotions using an emotion engine, thereby flexibly adjusting the survey experience.

[0338] The terminal provides an interface as an input method when the user starts a survey. When the user enters the survey purpose and target, that information is sent to the server. The server automatically generates appropriate survey questions using a generative model and simultaneously activates an emotion engine to collect the user's emotional data. Emotional data can be acquired through sensors such as cameras and microphones, and the emotional state is recognized based on the analysis of facial expressions and voice.

[0339] The generated questions are displayed on the device, and the user can review and edit them. During this process, the content and difficulty of the questions are adjusted using the results of the emotion engine, optimizing them for the user's current emotional state. For example, if the user is tired, the questions are made simpler; if they are stressed, positive feedback is provided.

[0340] When a survey is conducted, user response data, along with emotional state data, is collected on the server. The server analyzes the collected data in real time and performs additional analysis based on the emotional state. The results of this analysis are visualized using visualization tools as dashboards and graphs and provided to the user.

[0341] The report is automatically generated based on the analysis results and includes sentiment data. This allows users to obtain detailed analysis results that include emotional responses to the survey, leading to deeper insights and understanding. Using this system makes it possible to improve the accuracy of the survey and enhance the user experience.

[0342] The following describes the processing flow.

[0343] Step 1:

[0344] The user uses the terminal interface to input the purpose and target information of the survey. The information is sent to the server, and the emotion engine is activated.

[0345] Step 2:

[0346] The server automatically generates questions using a generative model based on the received survey information. It also acquires user emotion data through the camera and microphone, and analyzes the emotional state from facial expressions and voice.

[0347] Step 3:

[0348] The server uses the generated questions and sentiment analysis data to adjust the content and difficulty of the questions. Questions tailored to the user's emotions are then displayed on the terminal.

[0349] Step 4:

[0350] The device presents the user with questions optimized for their emotional state, and the user can review and edit the questions.

[0351] Step 5:

[0352] Once the user confirms the questions, the survey is conducted, and the device transfers the response data and emotional state data to the server in real time. During data transmission, the device checks the integrity of the data.

[0353] Step 6:

[0354] The server aggregates the collected response data and emotional state data using analytical tools and performs additional analysis based on emotions. Trends and correlations are detected.

[0355] Step 7:

[0356] The server visualizes the analysis results and sentiment data and displays them in a dashboard format, making them intuitively understandable to the user.

[0357] Step 8:

[0358] The final report is automatically generated and provided to the user via their device. The report includes insights that reflect sentiment data along with the research findings.

[0359] (Example 2)

[0360] 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".

[0361] The present invention aims to solve the problem of inefficient survey processes that do not take into account the emotional aspects of users that occur in conventional survey systems, and to improve the accuracy of surveys and enhance the survey experience by optimally adjusting survey questions based on the emotional state of the user.

[0362] 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.

[0363] In this invention, the server includes an information input means, a content generation means, and an emotion acquisition means. As a result, questions automatically generated based on the research objective and target are optimized to reflect the user's emotional state, enabling more effective research.

[0364] An "information input means" is an interface for receiving information from the user regarding the purpose and target of the survey and providing it to the system.

[0365] The "content generation means" is a function that automatically creates questions based on the entered survey objectives and target audience, and presents them to the user.

[0366] "Emotion acquisition methods" refer to technologies that collect user facial expressions and voice data through sensors to determine the user's emotional state.

[0367] "Display control means" refers to a function that presents the generated questions to the user, allows the user to review the content, and enables editing as needed.

[0368] An "optimization tool" is a function that adjusts the content and difficulty level of questions to match the user's current emotional state, thereby making the survey process more effective.

[0369] "Information gathering means" refers to a system used to collect data based on survey responses from users and to use it for analysis.

[0370] "Information analysis means" refers to technologies for analyzing collected data and sentiment data in real time and providing it to users.

[0371] A "method display means" is a function that visually displays the analysis results and provides them to the user in a way that is easy to understand.

[0372] "Information output means" refers to technology that automatically generates a detailed report based on the final analysis results and provides it to the user.

[0373] This invention is a system for optimizing survey questions according to the user's emotional state. The system consists of a terminal and a server, and specifically utilizes a generative AI model and emotion recognition technology.

[0374] The terminal provides an interface for users to input research objectives and targets. Once information is entered through this interface, the terminal transmits it to the server. The terminal is also equipped with sensors such as a camera and microphone, allowing it to collect emotional data by capturing the user's facial expressions and voice.

[0375] The server receives information about the survey objectives and target audience from the terminal and automatically generates survey questions using a generative AI model. In this process, the generative AI model constructs appropriate questions based on the input prompt sentences. Furthermore, the server activates an emotion engine and analyzes the emotion data obtained from the terminal. The emotion engine determines the user's emotional state and adjusts the content and difficulty of the questions accordingly.

[0376] The generated questions are sent from the server to the terminal and displayed to the user. The user can review these questions and edit them as needed. After the user completes the survey, the response data and sentiment data collected from the terminal are sent to the server.

[0377] The server integrates this data and performs real-time analysis. This analysis includes additional analysis based on emotional states. The analysis results are visualized and presented to the user in dashboard and graph formats. Finally, the server automatically generates a detailed report based on the analysis results and provides it to the user. This report includes insights, including users' emotional responses to the survey, which can improve the accuracy of the survey.

[0378] For example, if a user wants to conduct a "new product user satisfaction survey," setting the prompt to "generate questions about new product user satisfaction and optimize them using sentiment data" will cause the server to automatically generate the relevant questions and optimize their content according to the user's emotional state. This makes the survey more effective and efficient.

[0379] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0380] Step 1:

[0381] The terminal provides an interface for the user to input the survey objectives and target audience. The user uses this interface to input information about the specific objectives and scope of the survey. The entered data is recorded in text format on the terminal and sent directly to the next processing step.

[0382] Step 2:

[0383] The terminal transmits the entered survey objectives and target information to the server. This transmission takes place in real time over the network, and the server uses the received information as basic data for generating questions in the next step.

[0384] Step 3:

[0385] The server activates a generative AI model based on the received survey objective and target. The generative AI model automatically generates questions using the specified prompt text. Specifically, the generative AI model utilizes previously trained data to construct questions and related answer choices that are appropriate for the survey objective. The generated questions are stored as text data on the server.

[0386] Step 4:

[0387] The device uses sensors such as cameras and microphones to collect emotional data, including the user's facial expressions and voice data. This emotional data is processed in real time within the device, converted into a specific format, and sent to the server.

[0388] Step 5:

[0389] The server activates an emotion engine to analyze the received emotion data. This engine uses facial recognition and speech analysis algorithms to identify the user's emotional state. The emotional state is analyzed as numerical or categorical data and used to adjust the questions.

[0390] Step 6:

[0391] The server optimizes the questions based on the generated questions and the results of the sentiment data analysis. Specifically, it adjusts the content and difficulty of the questions according to the user's emotional state, making them easier for the user to answer. These customized questions are then sent back to the terminal.

[0392] Step 7:

[0393] The terminal displays a modified question to the user and prompts them to answer. The user enters their answers to the questions displayed on the terminal screen, and these answers are temporarily recorded on the terminal. The user's answers are sent to a server for intensive analysis in subsequent steps.

[0394] Step 8:

[0395] The server integrates collected response data and sentiment data and performs advanced analysis in real time. This analysis combines statistical methods with sentiment-based insights, and the results are prepared for display as visualized data.

[0396] Step 9:

[0397] The server visualizes the analysis results as dashboards and graphs, and displays them on the user's device. Through the analysis results, users can understand the full scope of the survey and their emotional responses.

[0398] Step 10:

[0399] The server automatically generates the final report, providing users with detailed insights along with the survey results. This report, including sentiment data obtained during the survey, is sent to the user in a format that allows for easy communication.

[0400] (Application Example 2)

[0401] 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."

[0402] In customer feedback processes, presenting uniform questions without considering customers' emotional states can lead to low response rates and compromised feedback quality. This makes it difficult for companies to gain detailed insights to improve the customer experience. Furthermore, if the content and difficulty of survey questions are not adapted to customers' current emotional states, satisfaction and accuracy may be compromised.

[0403] 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.

[0404] In this invention, the server includes adjustment means for recognizing the user's emotional state and optimizing the content or difficulty level of questions based on that emotional state; analysis means for aggregating and analyzing the collected information and user emotional state data in real time; and visualization means for visualizing the analysis results and providing them to the user. This makes it possible to provide a feedback process adapted to each customer's emotional state and improve the quality of the customer experience.

[0405] "Input means" refers to a device or interface for receiving information from the user regarding the purpose and target of information collection.

[0406] "Generation means" refers to a device or system that automatically creates appropriate questions based on information provided by the user.

[0407] "Adjustment means" refers to a device or system that has the function of detecting the user's emotional state and optimizing the content and difficulty level of the questions according to that state.

[0408] "Display means" refers to a device or system that shows automatically generated questions to the user and allows them to edit them as needed.

[0409] "Collection means" refers to a device or system that collects information or data based on questions answered by the user.

[0410] "Analysis means" refers to a device or system for analyzing collected information and sentiment data in real time to obtain additional insights.

[0411] "Visualization means" refers to a device or system for visually displaying analysis results and providing them in a format that is easily understandable to the user.

[0412] "Output means" refers to a device or system that automatically generates and provides the final report to the user.

[0413] This invention provides a system that offers an effective information gathering process that takes into account the user's emotional state. The terminal provides an input means for receiving the purpose and target of information gathering from the user. Once the user inputs the purpose and target of the survey, that information is sent to the server. The server creates automatically generated questions using a generative AI model.

[0414] Furthermore, the server uses emotion recognition software to analyze data acquired from the camera and microphone to recognize the user's emotional state. This analysis utilizes emotion recognition libraries such as OpenCV and Affectiva. Based on the collected emotion data, the server adjusts the content and difficulty of the questions, optimizing them to suit the user's current emotional state.

[0415] At this point, the questions are displayed on the terminal, which the user can review and edit as needed. The emotional state data, along with the user's responses, is sent back to the server. The server uses this data to perform real-time analysis and visualize the results. The visualized information is provided to the user as a dashboard and graphs, and a final report is automatically generated. This allows the user to obtain detailed analysis results, including their emotional response to the survey.

[0416] As a concrete example, imagine a scenario where a customer provides feedback through an app after purchasing a new device. The app recognizes their emotional state from their facial expressions and voice, and then presents appropriate questions based on that. For example, a prompt might look like this:

[0417] "Customers are trying to provide feedback immediately after purchasing a product. Recognize their emotions from camera and audio data and detect one of the following: 'Joy,' 'Stress,' or 'Fatigue.' Based on this, generate the following question: 'How happy are you with your new product?' Provide questions that increase the likelihood of a positive response."

[0418] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0419] Step 1:

[0420] The user operates the terminal and inputs the purpose and target of the information to be collected. This information is transmitted to the server via the input device. The input content becomes the basic data when generating questions in the next step.

[0421] Step 2:

[0422] The server uses a generative AI model based on the received information to automatically generate questions suitable for information gathering. At this time, the user's purpose and target information are used as input, and highly relevant questions are created using natural language processing and set as the output.

[0423] Step 3:

[0424] The terminal displays the generated questions on the screen. The user reviews the displayed questions and edits them as needed. The final list of questions is adjusted to meet the latest collection requirements.

[0425] Step 4:

[0426] The device uses a camera and microphone to collect the user's facial expressions and voice data. This data is input into emotion recognition software to recognize the user's emotional state. The output is data indicating the user's current emotional state.

[0427] Step 5:

[0428] The server optimizes the generated questions based on emotional state data. Detailed questions are displayed if the user is agitated, while concise questions are displayed if they are fatigued. The input is emotional state data, and the output is a list of adjusted questions.

[0429] Step 6:

[0430] Users answer optimized questions and send their response data from their device to the server. The responses become crucial data for the next analysis step.

[0431] Step 7:

[0432] The server integrates response data and sentiment data and performs real-time analysis. This process uses statistical and sentiment analysis techniques to extract insights from user responses. The analysis results are output.

[0433] Step 8:

[0434] The server visualizes the analysis results and provides them to the terminal in the form of dashboards and graphs. The results are presented in a way that allows users to intuitively understand the analysis.

[0435] Step 9:

[0436] The server automatically generates a final report. This report includes detailed feedback information, including the results of a user sentiment analysis. The output is the final report that the user can review and evaluate.

[0437] 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.

[0438] 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.

[0439] 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.

[0440] [Third Embodiment]

[0441] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0442] 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.

[0443] 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).

[0444] 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.

[0445] 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.

[0446] 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).

[0447] 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.

[0448] 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.

[0449] 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.

[0450] 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.

[0451] 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.

[0452] 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".

[0453] This invention provides a system that supports everything from the automatic generation of questionnaires to the analysis and visualization of data. Specific embodiments are described below.

[0454] This system provides an interface via the terminal when the user inputs the research objectives and target audience. The user can intuitively input research details into this interface. For example, they can input information about the objectives and target audience of a market research project for a new product.

[0455] The entered information is sent to the server, which uses a generation mechanism to automatically generate questions suitable for the user's purpose. This generation utilizes past survey data and statistical models to create specific and effective questions that match the objective.

[0456] The generated questions are displayed to the user via their device, and the user reviews them. The user can edit the displayed questions as needed, adjusting them to best suit their research needs. This flexibility allows for the creation of questionnaires optimized for specific needs.

[0457] Next, once the user has finished reviewing and editing the questions, the survey is conducted and data is collected through the collection methods. By receiving responses through digital forms and online platforms, the data is quickly transmitted to the server.

[0458] The server aggregates and analyzes the received data in real time using analytical tools. The analysis results are provided through visualization tools in visually easy-to-understand formats such as bar graphs and line graphs. This allows users to easily grasp data trends and important insights.

[0459] Ultimately, the server automatically generates a report based on the visualized analysis results and outputs it to the user's terminal. This report includes statistical analysis results and insights that users can use to make business decisions and formulate strategies.

[0460] In this way, the system streamlines the entire research process and enables high-quality data analysis, thereby strongly supporting users' decision-making.

[0461] The following describes the processing flow.

[0462] Step 1:

[0463] The user uses the terminal interface to input the purpose of the survey and information about the participants. Once input is complete, this information is sent to the server.

[0464] Step 2:

[0465] The server automatically generates appropriate questions using a generation mechanism based on the received information. A generation model is utilized to construct questions that are optimal for the target audience and purpose.

[0466] Step 3:

[0467] The server sends the generated questions to the terminal. The terminal displays the questions to the user, who then reviews them. The user edits the questions as needed and finalizes the questionnaire.

[0468] Step 4:

[0469] The user begins the survey using a finalized questionnaire. They distribute the questionnaires to the target audience and collect their responses.

[0470] Step 5:

[0471] The device transfers the collected response data to the server in real time. During this process, the data is automatically checked for any errors.

[0472] Step 6:

[0473] The server aggregates the collected data using analytical tools and performs necessary statistical analysis. This analysis includes basic statistics as well as relationship assessments.

[0474] Step 7:

[0475] The server processes the analysis results in a graphical format using visualization tools, making them easily understandable to the user.

[0476] Step 8:

[0477] The server automatically generates a report based on the visualized analytical data. This report includes detailed analysis and insights that users can utilize.

[0478] Step 9:

[0479] The terminal displays the generated report to the user. The user uses this report to make business decisions.

[0480] (Example 1)

[0481] 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."

[0482] Traditional market research systems often involved manual question creation and data analysis, resulting in a cumbersome and time-consuming research process. Furthermore, visualizing the resulting statistical information and creating reports was time-consuming, making it difficult for users to quickly obtain the insights necessary for decision-making.

[0483] 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.

[0484] In this invention, the server includes an input means for receiving information from the user regarding the purpose and subject of the survey, a generation means for automatically generating questions using a generative model based on the purpose and subject of the survey, and an analysis means for processing and interpreting the collected information in real time using statistical methods. This streamlines the entire survey process, allowing users to receive high-quality data analysis immediately and enabling rapid decision-making.

[0485] An "input device" is a device that has the function of receiving information from the user regarding the purpose and subject of the survey.

[0486] A "generative model" is an algorithm or process for automatically generating appropriate questions based on the user's research objectives and target audience.

[0487] A "display means" is a device that has the function of presenting the generated questions to the user and allowing them to modify them as needed.

[0488] "Collection means" refers to a device that has the function of collecting information based on questions confirmed by the user.

[0489] An "analysis tool" is a device that has the function of processing collected information in real time and interpreting it using statistical methods.

[0490] A "visualization means" is a device that has the function of visually representing and providing processing results to the user.

[0491] "Output means" refers to a device that has the function of automatically generating a final report and providing it to the user.

[0492] This invention is a system that provides high-quality data analysis through the automation of the survey process. Specifically, when a user inputs information about the purpose and target of a survey using a terminal, the terminal sends this information to a server. The server uses a generative AI model to automatically generate questions that are appropriate for the survey purpose specified by the user. This generation process utilizes historical data and statistical methods.

[0493] The generated questions are presented to the user via the terminal, and the user can review and modify them as needed. This process allows the user to obtain questions optimized for their research. Once the research begins, the terminal collects the responses and transfers the data to the server.

[0494] The server analyzes received data in real time, aggregating and interpreting it using statistical methods. The resulting analysis is provided in visual formats such as bar graphs and line graphs, and is automatically generated as an easy-to-understand report for the user. This allows users to quickly obtain insights that directly impact decision-making.

[0495] For example, if a user is conducting market research on health-conscious soft drinks, they would input the objective "Health-conscious market research" and attribute information such as "Target age: 18-34" into the terminal. Based on this information, the server automatically generates and presents the question, "What beverages do you consider drinking when you are health-conscious?"

[0496] An example of a prompt message would be: "Generate market research questions for a new health-conscious beverage."

[0497] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0498] Step 1:

[0499] The terminal receives information from the user regarding the purpose and target audience of the survey. This input may include, for example, "market research for a new product" or "target audience is women in their 20s." The terminal formats this input and prepares it for transmission to the server.

[0500] Step 2:

[0501] The server receives information sent from the terminal. Based on this input information, it uses a generative AI model to generate appropriate questions. Based on this prompt, the model outputs questions such as "What are the deciding factors for a purchase?". Historical data and statistical models are used for data calculations at this stage.

[0502] Step 3:

[0503] The generated questions are sent from the server to the terminal and displayed to the user. The user reviews the displayed questions and makes modifications or additions as needed. The user then determines the final set of questions, which the terminal sends back to the server.

[0504] Step 4:

[0505] The survey is conducted based on questions confirmed by the user. The device collects responses through online forms and platforms and sends them to the server as a dataset. Each response is quickly organized in digital format.

[0506] Step 5:

[0507] The server receives the collected data and performs analysis in real time. Statistical methods are used to aggregate the data and interpret trends and patterns. This process generates analysis results in a format suitable for visualization.

[0508] Step 6:

[0509] The analysis results are visualized on the server and represented as bar graphs and line graphs. The server automatically generates this visualized data as a report and sends it to the terminal. The terminal displays the report so that the user can review the final report.

[0510] Step 7:

[0511] Users review the final report on their devices and make decisions based on the data. Having gained concrete insights, users can then determine strategies and actions accordingly.

[0512] (Application Example 1)

[0513] 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."

[0514] In modern advertising, it is crucial to quickly and accurately measure the effectiveness of advertising on a target market and to develop optimal strategies based on this measurement. However, traditional methods require considerable time and effort for data collection, analysis, and the provision of effective feedback. To address this challenge, there is a need for a system that can analyze advertising effectiveness in real time and propose strategies quickly.

[0515] 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.

[0516] In this invention, the server includes input means for receiving information and targets from the user, inquiry generation means for automatically generating questions based on the information and targets, and output means for automatically generating a final report and proposing strategies to improve advertising effectiveness. This enables the user to measure the effectiveness of advertising campaigns in real time and immediately formulate the optimal strategy.

[0517] A "user" is an entity that utilizes a system and provides or adjusts information.

[0518] "Information" refers to the data and instructions necessary for the system to process data, such as the purpose and target of the investigation.

[0519] "Target" refers to the subject matter of the survey or analysis, and means a specific individual, group, or market.

[0520] "Input method" refers to an interface or tool for users to provide information.

[0521] "Inquiry generation means" refers to a process or device that generates relevant questions based on the input information.

[0522] "Presentation method" refers to a function or tool that displays the generated questions to the user and allows them to edit them as needed.

[0523] "Means of information gathering" refers to the methods and techniques used to collect information based on the confirmed questions.

[0524] "Analysis means" refers to a process or device for aggregating and analyzing collected information.

[0525] "Visualization methods" refer to techniques and tools for displaying analysis results in a format that is easy to understand intuitively.

[0526] A "report" refers to a document that includes automatically generated advertising strategy proposals based on analysis and visualization results.

[0527] The system for carrying out this invention includes a user terminal, a cloud-based server, and a network connecting them. The user first uses a smartphone with a dedicated application installed to input information about the research objectives and subjects. The user terminal then transmits this information to the cloud server.

[0528] The server uses an AI model based on the received information to automatically generate appropriate questions. These generated questions are displayed on the user's terminal via an intuitive interface. Users can edit these questions as needed.

[0529] Based on the edited questions, the server initiates the data collection process. This collection method utilizes digital platforms such as online forms, and the collected data is transmitted to the server in real time.

[0530] On the server, the collected data is rapidly analyzed using analytical tools. This analysis is performed using statistical methods and machine learning models, and the results are presented in an easy-to-understand format using visualization tools. Based on the analysis results, the server automatically generates a report that includes strategies for improving advertising effectiveness and outputs it to the user's terminal.

[0531] For example, if a clothing manufacturer launches a new advertising campaign targeting young people, users would input relevant target information into the app. Based on this input, the server generates a prompt message such as, "Generate a questionnaire to investigate the effectiveness of advertising targeting young people." This prompt is then fed into an AI model, which generates a set of relevant questions, enabling rapid campaign effectiveness measurement and strategic adjustment.

[0532] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0533] Step 1:

[0534] Users input information about the survey objectives and target audience via their smartphones. The entered information is then sent directly from the device to the cloud server. At this stage, user input is the primary processing target, and the entered data is used for subsequent question generation.

[0535] Step 2:

[0536] The server automatically generates appropriate questions using a generative AI model based on the information it receives. Specifically, it analyzes the information received from the user and inputs relevant prompts (e.g., "Please generate a questionnaire to investigate the effectiveness of advertising targeting young people.") into the generative AI model. Based on these prompts, the AI ​​model generates a set of questions, which are then output to the server for reception.

[0537] Step 3:

[0538] The generated questions are sent from the server to the user's terminal and displayed to the user through the interface. The user adjusts the details of the survey by editing the displayed questions. The edited questions are sent back to the server. The user's editing actions become input, and the edited questions are output.

[0539] Step 4:

[0540] The server initiates data collection using an online form based on the user's confirmed questions. This collection process receives responses from the target market and aggregates them on the server in real time. The data is stored on the server as specific responses to the questions.

[0541] Step 5:

[0542] The server performs data analysis using statistical methods and machine learning based on the collected data. The input is the collected raw data, and the analysis results in insights into advertising effectiveness and user trends. During this analysis process, data trends and outliers are identified, and information necessary for strategy formulation is generated.

[0543] Step 6:

[0544] The analysis results are visualized and provided to the user's device in an easy-to-understand format (e.g., graphs and charts). Furthermore, the server automatically generates a report based on the visualized information, including strategies to improve advertising effectiveness, and sends this report to the user's device. Based on this report, the user can adjust the direction of their advertising campaign.

[0545] 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.

[0546] This invention provides a system that recognizes user emotions and optimizes the survey process based on them. This system automatically generates questions according to the survey objectives and target audience entered by the user, and further analyzes the user's emotions using an emotion engine, thereby flexibly adjusting the survey experience.

[0547] The terminal provides an interface as an input method when the user starts a survey. When the user enters the survey purpose and target, that information is sent to the server. The server automatically generates appropriate survey questions using a generative model and simultaneously activates an emotion engine to collect the user's emotional data. Emotional data can be acquired through sensors such as cameras and microphones, and the emotional state is recognized based on the analysis of facial expressions and voice.

[0548] The generated questions are displayed on the device, and the user can review and edit them. During this process, the content and difficulty of the questions are adjusted using the results of the emotion engine, optimizing them for the user's current emotional state. For example, if the user is tired, the questions are made simpler; if they are stressed, positive feedback is provided.

[0549] When a survey is conducted, user response data, along with emotional state data, is collected on the server. The server analyzes the collected data in real time and performs additional analysis based on the emotional state. The results of this analysis are visualized using visualization tools as dashboards and graphs and provided to the user.

[0550] The report is automatically generated based on the analysis results and includes sentiment data. This allows users to obtain detailed analysis results that include emotional responses to the survey, leading to deeper insights and understanding. Using this system makes it possible to improve the accuracy of the survey and enhance the user experience.

[0551] The following describes the processing flow.

[0552] Step 1:

[0553] The user uses the terminal interface to input the purpose and target information of the survey. The information is sent to the server, and the emotion engine is activated.

[0554] Step 2:

[0555] The server automatically generates questions using a generative model based on the received survey information. It also acquires user emotion data through the camera and microphone, and analyzes the emotional state from facial expressions and voice.

[0556] Step 3:

[0557] The server uses the generated questions and sentiment analysis data to adjust the content and difficulty of the questions. Questions tailored to the user's emotions are then displayed on the terminal.

[0558] Step 4:

[0559] The device presents the user with questions optimized for their emotional state, and the user can review and edit the questions.

[0560] Step 5:

[0561] Once the user confirms the questions, the survey is conducted, and the device transfers the response data and emotional state data to the server in real time. During data transmission, the device checks the integrity of the data.

[0562] Step 6:

[0563] The server aggregates the collected response data and emotional state data using analytical tools and performs additional analysis based on emotions. Trends and correlations are detected.

[0564] Step 7:

[0565] The server visualizes the analysis results and sentiment data and displays them in a dashboard format, making them intuitively understandable to the user.

[0566] Step 8:

[0567] The final report is automatically generated and provided to the user via their device. The report includes insights that reflect sentiment data along with the research findings.

[0568] (Example 2)

[0569] 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."

[0570] The present invention aims to solve the problem of inefficient survey processes that do not take into account the emotional aspects of users that occur in conventional survey systems, and to improve the accuracy of surveys and enhance the survey experience by optimally adjusting survey questions based on the emotional state of the user.

[0571] 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.

[0572] In this invention, the server includes an information input means, a content generation means, and an emotion acquisition means. As a result, questions automatically generated based on the research objective and target are optimized to reflect the user's emotional state, enabling more effective research.

[0573] An "information input means" is an interface for receiving information from the user regarding the purpose and target of the survey and providing it to the system.

[0574] The "content generation means" is a function that automatically creates questions based on the entered survey objectives and target audience, and presents them to the user.

[0575] "Emotion acquisition methods" refer to technologies that collect user facial expressions and voice data through sensors to determine the user's emotional state.

[0576] "Display control means" refers to a function that presents the generated questions to the user, allows the user to review the content, and enables editing as needed.

[0577] An "optimization tool" is a function that adjusts the content and difficulty level of questions to match the user's current emotional state, thereby making the survey process more effective.

[0578] "Information gathering means" refers to a system used to collect data based on survey responses from users and to use it for analysis.

[0579] "Information analysis means" refers to technologies for analyzing collected data and sentiment data in real time and providing it to users.

[0580] A "method display means" is a function that visually displays the analysis results and provides them to the user in a way that is easy to understand.

[0581] "Information output means" refers to technology that automatically generates a detailed report based on the final analysis results and provides it to the user.

[0582] This invention is a system for optimizing survey questions according to the user's emotional state. The system consists of a terminal and a server, and specifically utilizes a generative AI model and emotion recognition technology.

[0583] The terminal provides an interface for users to input research objectives and targets. Once information is entered through this interface, the terminal transmits it to the server. The terminal is also equipped with sensors such as a camera and microphone, allowing it to collect emotional data by capturing the user's facial expressions and voice.

[0584] The server receives information about the survey objectives and target audience from the terminal and automatically generates survey questions using a generative AI model. In this process, the generative AI model constructs appropriate questions based on the input prompt sentences. Furthermore, the server activates an emotion engine and analyzes the emotion data obtained from the terminal. The emotion engine determines the user's emotional state and adjusts the content and difficulty of the questions accordingly.

[0585] The generated questions are sent from the server to the terminal and displayed to the user. The user can review these questions and edit them as needed. After the user completes the survey, the response data and sentiment data collected from the terminal are sent to the server.

[0586] The server integrates this data and performs real-time analysis. This analysis includes additional analysis based on emotional states. The analysis results are visualized and presented to the user in dashboard and graph formats. Finally, the server automatically generates a detailed report based on the analysis results and provides it to the user. This report includes insights, including users' emotional responses to the survey, which can improve the accuracy of the survey.

[0587] For example, if a user wants to conduct a "new product user satisfaction survey," setting the prompt to "generate questions about new product user satisfaction and optimize them using sentiment data" will cause the server to automatically generate the relevant questions and optimize their content according to the user's emotional state. This makes the survey more effective and efficient.

[0588] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0589] Step 1:

[0590] The terminal provides an interface for the user to input the survey objectives and target audience. The user uses this interface to input information about the specific objectives and scope of the survey. The entered data is recorded in text format on the terminal and sent directly to the next processing step.

[0591] Step 2:

[0592] The terminal transmits the entered survey objectives and target information to the server. This transmission takes place in real time over the network, and the server uses the received information as basic data for generating questions in the next step.

[0593] Step 3:

[0594] The server activates a generative AI model based on the received survey objective and target. The generative AI model automatically generates questions using the specified prompt text. Specifically, the generative AI model utilizes previously trained data to construct questions and related answer choices that are appropriate for the survey objective. The generated questions are stored as text data on the server.

[0595] Step 4:

[0596] The device uses sensors such as cameras and microphones to collect emotional data, including the user's facial expressions and voice data. This emotional data is processed in real time within the device, converted into a specific format, and sent to the server.

[0597] Step 5:

[0598] The server activates an emotion engine to analyze the received emotion data. This engine uses facial recognition and speech analysis algorithms to identify the user's emotional state. The emotional state is analyzed as numerical or categorical data and used to adjust the questions.

[0599] Step 6:

[0600] The server optimizes the questions based on the generated questions and the results of the sentiment data analysis. Specifically, it adjusts the content and difficulty of the questions according to the user's emotional state, making them easier for the user to answer. These customized questions are then sent back to the terminal.

[0601] Step 7:

[0602] The terminal displays a modified question to the user and prompts them to answer. The user enters their answers to the questions displayed on the terminal screen, and these answers are temporarily recorded on the terminal. The user's answers are sent to a server for intensive analysis in subsequent steps.

[0603] Step 8:

[0604] The server integrates collected response data and sentiment data and performs advanced analysis in real time. This analysis combines statistical methods with sentiment-based insights, and the results are prepared for display as visualized data.

[0605] Step 9:

[0606] The server visualizes the analysis results as dashboards and graphs, and displays them on the user's device. Through the analysis results, users can understand the full scope of the survey and their emotional responses.

[0607] Step 10:

[0608] The server automatically generates the final report, providing users with detailed insights along with the survey results. This report, including sentiment data obtained during the survey, is sent to the user in a format that allows for easy communication.

[0609] (Application Example 2)

[0610] 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."

[0611] In customer feedback processes, presenting uniform questions without considering customers' emotional states can lead to low response rates and compromised feedback quality. This makes it difficult for companies to gain detailed insights to improve the customer experience. Furthermore, if the content and difficulty of survey questions are not adapted to customers' current emotional states, satisfaction and accuracy may be compromised.

[0612] 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.

[0613] In this invention, the server includes adjustment means for recognizing the user's emotional state and optimizing the content or difficulty level of questions based on that emotional state; analysis means for aggregating and analyzing the collected information and user emotional state data in real time; and visualization means for visualizing the analysis results and providing them to the user. This makes it possible to provide a feedback process adapted to each customer's emotional state and improve the quality of the customer experience.

[0614] "Input means" refers to a device or interface for receiving information from the user regarding the purpose and target of information collection.

[0615] "Generation means" refers to a device or system that automatically creates appropriate questions based on information provided by the user.

[0616] "Adjustment means" refers to a device or system that has the function of detecting the user's emotional state and optimizing the content and difficulty level of the questions according to that state.

[0617] "Display means" refers to a device or system that shows automatically generated questions to the user and allows them to edit them as needed.

[0618] "Collection means" refers to a device or system that collects information or data based on questions answered by the user.

[0619] "Analysis means" refers to a device or system for analyzing collected information and sentiment data in real time to obtain additional insights.

[0620] "Visualization means" refers to a device or system for visually displaying analysis results and providing them in a format that is easily understandable to the user.

[0621] "Output means" refers to a device or system that automatically generates and provides the final report to the user.

[0622] This invention provides a system that offers an effective information gathering process that takes into account the user's emotional state. The terminal provides an input means for receiving the purpose and target of information gathering from the user. Once the user inputs the purpose and target of the survey, that information is sent to the server. The server creates automatically generated questions using a generative AI model.

[0623] Furthermore, the server uses emotion recognition software to analyze data acquired from the camera and microphone to recognize the user's emotional state. This analysis utilizes emotion recognition libraries such as OpenCV and Affectiva. Based on the collected emotion data, the server adjusts the content and difficulty of the questions, optimizing them to suit the user's current emotional state.

[0624] At this point, the questions are displayed on the terminal, which the user can review and edit as needed. The emotional state data, along with the user's responses, is sent back to the server. The server uses this data to perform real-time analysis and visualize the results. The visualized information is provided to the user as a dashboard and graphs, and a final report is automatically generated. This allows the user to obtain detailed analysis results, including their emotional response to the survey.

[0625] As a concrete example, imagine a scenario where a customer provides feedback through an app after purchasing a new device. The app recognizes their emotional state from their facial expressions and voice, and then presents appropriate questions based on that. For example, a prompt might look like this:

[0626] "Customers are trying to provide feedback immediately after purchasing a product. Recognize their emotions from camera and audio data and detect one of the following: 'Joy,' 'Stress,' or 'Fatigue.' Based on this, generate the following question: 'How happy are you with your new product?' Provide questions that increase the likelihood of a positive response."

[0627] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0628] Step 1:

[0629] The user operates the terminal and inputs the purpose and target of the information to be collected. This information is transmitted to the server via the input device. The input content becomes the basic data when generating questions in the next step.

[0630] Step 2:

[0631] The server uses a generative AI model based on the received information to automatically generate questions suitable for information gathering. At this time, the user's purpose and target information are used as input, and highly relevant questions are created using natural language processing and set as the output.

[0632] Step 3:

[0633] The terminal displays the generated questions on the screen. The user reviews the displayed questions and edits them as needed. The final list of questions is adjusted to meet the latest collection requirements.

[0634] Step 4:

[0635] The device uses a camera and microphone to collect the user's facial expressions and voice data. This data is input into emotion recognition software to recognize the user's emotional state. The output is data indicating the user's current emotional state.

[0636] Step 5:

[0637] The server optimizes the generated questions based on emotional state data. Detailed questions are displayed if the user is agitated, while concise questions are displayed if they are fatigued. The input is emotional state data, and the output is a list of adjusted questions.

[0638] Step 6:

[0639] Users answer optimized questions and send their response data from their device to the server. The responses become crucial data for the next analysis step.

[0640] Step 7:

[0641] The server integrates response data and sentiment data and performs real-time analysis. This process uses statistical and sentiment analysis techniques to extract insights from user responses. The analysis results are output.

[0642] Step 8:

[0643] The server visualizes the analysis results and provides them to the terminal in the form of dashboards and graphs. The results are presented in a way that allows users to intuitively understand the analysis.

[0644] Step 9:

[0645] The server automatically generates a final report. This report includes detailed feedback information, including the results of a user sentiment analysis. The output is the final report that the user can review and evaluate.

[0646] 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.

[0647] 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.

[0648] 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.

[0649] [Fourth Embodiment]

[0650] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0651] 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.

[0652] 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).

[0653] 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.

[0654] 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.

[0655] 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).

[0656] 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.

[0657] 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.

[0658] 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.

[0659] 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.

[0660] 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.

[0661] 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.

[0662] 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".

[0663] This invention provides a system that supports everything from the automatic generation of questionnaires to the analysis and visualization of data. Specific embodiments are described below.

[0664] This system provides an interface via the terminal when the user inputs the research objectives and target audience. The user can intuitively input research details into this interface. For example, they can input information about the objectives and target audience of a market research project for a new product.

[0665] The entered information is sent to the server, which uses a generation mechanism to automatically generate questions suitable for the user's purpose. This generation utilizes past survey data and statistical models to create specific and effective questions that match the objective.

[0666] The generated questions are displayed to the user via their device, and the user reviews them. The user can edit the displayed questions as needed, adjusting them to best suit their research needs. This flexibility allows for the creation of questionnaires optimized for specific needs.

[0667] Next, once the user has finished reviewing and editing the questions, the survey is conducted and data is collected through the collection methods. By receiving responses through digital forms and online platforms, the data is quickly transmitted to the server.

[0668] The server aggregates and analyzes the received data in real time using analytical tools. The analysis results are provided through visualization tools in visually easy-to-understand formats such as bar graphs and line graphs. This allows users to easily grasp data trends and important insights.

[0669] Ultimately, the server automatically generates a report based on the visualized analysis results and outputs it to the user's terminal. This report includes statistical analysis results and insights that users can use to make business decisions and formulate strategies.

[0670] In this way, the system streamlines the entire research process and enables high-quality data analysis, thereby strongly supporting users' decision-making.

[0671] The following describes the processing flow.

[0672] Step 1:

[0673] The user uses the terminal interface to input the purpose of the survey and information about the participants. Once input is complete, this information is sent to the server.

[0674] Step 2:

[0675] The server automatically generates appropriate questions using a generation mechanism based on the received information. A generation model is utilized to construct questions that are optimal for the target audience and purpose.

[0676] Step 3:

[0677] The server sends the generated questions to the terminal. The terminal displays the questions to the user, who then reviews them. The user edits the questions as needed and finalizes the questionnaire.

[0678] Step 4:

[0679] The user begins the survey using a finalized questionnaire. They distribute the questionnaires to the target audience and collect their responses.

[0680] Step 5:

[0681] The device transfers the collected response data to the server in real time. During this process, the data is automatically checked for any errors.

[0682] Step 6:

[0683] The server aggregates the collected data using analytical tools and performs necessary statistical analysis. This analysis includes basic statistics as well as relationship assessments.

[0684] Step 7:

[0685] The server processes the analysis results in a graphical format using visualization tools, making them easily understandable to the user.

[0686] Step 8:

[0687] The server automatically generates a report based on the visualized analytical data. This report includes detailed analysis and insights that users can utilize.

[0688] Step 9:

[0689] The terminal displays the generated report to the user. The user uses this report to make business decisions.

[0690] (Example 1)

[0691] 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".

[0692] Traditional market research systems often involved manual question creation and data analysis, resulting in a cumbersome and time-consuming research process. Furthermore, visualizing the resulting statistical information and creating reports was time-consuming, making it difficult for users to quickly obtain the insights necessary for decision-making.

[0693] 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.

[0694] In this invention, the server includes an input means for receiving information from the user regarding the purpose and subject of the survey, a generation means for automatically generating questions using a generative model based on the purpose and subject of the survey, and an analysis means for processing and interpreting the collected information in real time using statistical methods. This streamlines the entire survey process, allowing users to receive high-quality data analysis immediately and enabling rapid decision-making.

[0695] An "input device" is a device that has the function of receiving information from the user regarding the purpose and subject of the survey.

[0696] A "generative model" is an algorithm or process for automatically generating appropriate questions based on the user's research objectives and target audience.

[0697] A "display means" is a device that has the function of presenting the generated questions to the user and allowing them to modify them as needed.

[0698] "Collection means" refers to a device that has the function of collecting information based on questions confirmed by the user.

[0699] An "analysis tool" is a device that has the function of processing collected information in real time and interpreting it using statistical methods.

[0700] A "visualization means" is a device that has the function of visually representing and providing processing results to the user.

[0701] "Output means" refers to a device that has the function of automatically generating a final report and providing it to the user.

[0702] This invention is a system that provides high-quality data analysis through the automation of the survey process. Specifically, when a user inputs information about the purpose and target of a survey using a terminal, the terminal sends this information to a server. The server uses a generative AI model to automatically generate questions that are appropriate for the survey purpose specified by the user. This generation process utilizes historical data and statistical methods.

[0703] The generated questions are presented to the user via the terminal, and the user can review and modify them as needed. This process allows the user to obtain questions optimized for their research. Once the research begins, the terminal collects the responses and transfers the data to the server.

[0704] The server analyzes received data in real time, aggregating and interpreting it using statistical methods. The resulting analysis is provided in visual formats such as bar graphs and line graphs, and is automatically generated as an easy-to-understand report for the user. This allows users to quickly obtain insights that directly impact decision-making.

[0705] For example, if a user is conducting market research on health-conscious soft drinks, they would input the objective "Health-conscious market research" and attribute information such as "Target age: 18-34" into the terminal. Based on this information, the server automatically generates and presents the question, "What beverages do you consider drinking when you are health-conscious?"

[0706] An example of a prompt message would be: "Generate market research questions for a new health-conscious beverage."

[0707] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0708] Step 1:

[0709] The terminal receives information from the user regarding the purpose and target audience of the survey. This input may include, for example, "market research for a new product" or "target audience is women in their 20s." The terminal formats this input and prepares it for transmission to the server.

[0710] Step 2:

[0711] The server receives information sent from the terminal. Based on this input information, it uses a generative AI model to generate appropriate questions. Based on this prompt, the model outputs questions such as "What are the deciding factors for a purchase?". Historical data and statistical models are used for data calculations at this stage.

[0712] Step 3:

[0713] The generated questions are sent from the server to the terminal and displayed to the user. The user reviews the displayed questions and makes modifications or additions as needed. The user then determines the final set of questions, which the terminal sends back to the server.

[0714] Step 4:

[0715] The survey is conducted based on questions confirmed by the user. The device collects responses through online forms and platforms and sends them to the server as a dataset. Each response is quickly organized in digital format.

[0716] Step 5:

[0717] The server receives the collected data and performs analysis in real time. Statistical methods are used to aggregate the data and interpret trends and patterns. This process generates analysis results in a format suitable for visualization.

[0718] Step 6:

[0719] The analysis results are visualized on the server and represented as bar graphs and line graphs. The server automatically generates this visualized data as a report and sends it to the terminal. The terminal displays the report so that the user can review the final report.

[0720] Step 7:

[0721] Users review the final report on their devices and make decisions based on the data. Having gained concrete insights, users can then determine strategies and actions accordingly.

[0722] (Application Example 1)

[0723] 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".

[0724] In modern advertising, it is crucial to quickly and accurately measure the effectiveness of advertising on a target market and to develop optimal strategies based on this measurement. However, traditional methods require considerable time and effort for data collection, analysis, and the provision of effective feedback. To address this challenge, there is a need for a system that can analyze advertising effectiveness in real time and propose strategies quickly.

[0725] 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.

[0726] In this invention, the server includes input means for receiving information and targets from the user, inquiry generation means for automatically generating questions based on the information and targets, and output means for automatically generating a final report and proposing strategies to improve advertising effectiveness. This enables the user to measure the effectiveness of advertising campaigns in real time and immediately formulate the optimal strategy.

[0727] A "user" is an entity that utilizes a system and provides or adjusts information.

[0728] "Information" refers to the data and instructions necessary for the system to process data, such as the purpose and target of the investigation.

[0729] "Target" refers to the subject matter of the survey or analysis, and means a specific individual, group, or market.

[0730] "Input method" refers to an interface or tool for users to provide information.

[0731] "Inquiry generation means" refers to a process or device that generates relevant questions based on the input information.

[0732] "Presentation method" refers to a function or tool that displays the generated questions to the user and allows them to edit them as needed.

[0733] "Means of information gathering" refers to the methods and techniques used to collect information based on the confirmed questions.

[0734] "Analysis means" refers to a process or device for aggregating and analyzing collected information.

[0735] "Visualization methods" refer to techniques and tools for displaying analysis results in a format that is easy to understand intuitively.

[0736] A "report" refers to a document that includes automatically generated advertising strategy proposals based on analysis and visualization results.

[0737] The system for carrying out this invention includes a user terminal, a cloud-based server, and a network connecting them. The user first uses a smartphone with a dedicated application installed to input information about the research objectives and subjects. The user terminal then transmits this information to the cloud server.

[0738] The server uses an AI model based on the received information to automatically generate appropriate questions. These generated questions are displayed on the user's terminal via an intuitive interface. Users can edit these questions as needed.

[0739] Based on the edited questions, the server initiates the data collection process. This collection method utilizes digital platforms such as online forms, and the collected data is transmitted to the server in real time.

[0740] On the server, the collected data is rapidly analyzed using analytical tools. This analysis is performed using statistical methods and machine learning models, and the results are presented in an easy-to-understand format using visualization tools. Based on the analysis results, the server automatically generates a report that includes strategies for improving advertising effectiveness and outputs it to the user's terminal.

[0741] For example, if a clothing manufacturer launches a new advertising campaign targeting young people, users would input relevant target information into the app. Based on this input, the server generates a prompt message such as, "Generate a questionnaire to investigate the effectiveness of advertising targeting young people." This prompt is then fed into an AI model, which generates a set of relevant questions, enabling rapid campaign effectiveness measurement and strategic adjustment.

[0742] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0743] Step 1:

[0744] Users input information about the survey objectives and target audience via their smartphones. The entered information is then sent directly from the device to the cloud server. At this stage, user input is the primary processing target, and the entered data is used for subsequent question generation.

[0745] Step 2:

[0746] The server automatically generates appropriate questions using a generative AI model based on the information it receives. Specifically, it analyzes the information received from the user and inputs relevant prompts (e.g., "Please generate a questionnaire to investigate the effectiveness of advertising targeting young people.") into the generative AI model. Based on these prompts, the AI ​​model generates a set of questions, which are then output to the server for reception.

[0747] Step 3:

[0748] The generated questions are sent from the server to the user's terminal and displayed to the user through the interface. The user adjusts the details of the survey by editing the displayed questions. The edited questions are sent back to the server. The user's editing actions become input, and the edited questions are output.

[0749] Step 4:

[0750] The server initiates data collection using an online form based on the user's confirmed questions. This collection process receives responses from the target market and aggregates them on the server in real time. The data is stored on the server as specific responses to the questions.

[0751] Step 5:

[0752] The server performs data analysis using statistical methods and machine learning based on the collected data. The input is the collected raw data, and the analysis results in insights into advertising effectiveness and user trends. During this analysis process, data trends and outliers are identified, and information necessary for strategy formulation is generated.

[0753] Step 6:

[0754] The analysis results are visualized and provided to the user's device in an easy-to-understand format (e.g., graphs and charts). Furthermore, the server automatically generates a report based on the visualized information, including strategies to improve advertising effectiveness, and sends this report to the user's device. Based on this report, the user can adjust the direction of their advertising campaign.

[0755] 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.

[0756] This invention provides a system that recognizes user emotions and optimizes the survey process based on them. This system automatically generates questions according to the survey objectives and target audience entered by the user, and further analyzes the user's emotions using an emotion engine, thereby flexibly adjusting the survey experience.

[0757] The terminal provides an interface as an input method when the user starts a survey. When the user enters the survey purpose and target, that information is sent to the server. The server automatically generates appropriate survey questions using a generative model and simultaneously activates an emotion engine to collect the user's emotional data. Emotional data can be acquired through sensors such as cameras and microphones, and the emotional state is recognized based on the analysis of facial expressions and voice.

[0758] The generated questions are displayed on the device, and the user can review and edit them. During this process, the content and difficulty of the questions are adjusted using the results of the emotion engine, optimizing them for the user's current emotional state. For example, if the user is tired, the questions are made simpler; if they are stressed, positive feedback is provided.

[0759] When a survey is conducted, user response data, along with emotional state data, is collected on the server. The server analyzes the collected data in real time and performs additional analysis based on the emotional state. The results of this analysis are visualized using visualization tools as dashboards and graphs and provided to the user.

[0760] The report is automatically generated based on the analysis results and includes sentiment data. This allows users to obtain detailed analysis results that include emotional responses to the survey, leading to deeper insights and understanding. Using this system makes it possible to improve the accuracy of the survey and enhance the user experience.

[0761] The following describes the processing flow.

[0762] Step 1:

[0763] The user uses the terminal interface to input the purpose and target information of the survey. The information is sent to the server, and the emotion engine is activated.

[0764] Step 2:

[0765] The server automatically generates questions using a generative model based on the received survey information. It also acquires user emotion data through the camera and microphone, and analyzes the emotional state from facial expressions and voice.

[0766] Step 3:

[0767] The server uses the generated questions and sentiment analysis data to adjust the content and difficulty of the questions. Questions tailored to the user's emotions are then displayed on the terminal.

[0768] Step 4:

[0769] The device presents the user with questions optimized for their emotional state, and the user can review and edit the questions.

[0770] Step 5:

[0771] Once the user confirms the questions, the survey is conducted, and the device transfers the response data and emotional state data to the server in real time. During data transmission, the device checks the integrity of the data.

[0772] Step 6:

[0773] The server aggregates the collected response data and emotional state data using analytical tools and performs additional analysis based on emotions. Trends and correlations are detected.

[0774] Step 7:

[0775] The server visualizes the analysis results and sentiment data and displays them in a dashboard format, making them intuitively understandable to the user.

[0776] Step 8:

[0777] The final report is automatically generated and provided to the user via their device. The report includes insights that reflect sentiment data along with the research findings.

[0778] (Example 2)

[0779] 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".

[0780] The present invention aims to solve the problem of inefficient survey processes that do not take into account the emotional aspects of users that occur in conventional survey systems, and to improve the accuracy of surveys and enhance the survey experience by optimally adjusting survey questions based on the emotional state of the user.

[0781] 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.

[0782] In this invention, the server includes an information input means, a content generation means, and an emotion acquisition means. As a result, questions automatically generated based on the research objective and target are optimized to reflect the user's emotional state, enabling more effective research.

[0783] An "information input means" is an interface for receiving information from the user regarding the purpose and target of the survey and providing it to the system.

[0784] The "content generation means" is a function that automatically creates questions based on the entered survey objectives and target audience, and presents them to the user.

[0785] "Emotion acquisition methods" refer to technologies that collect user facial expressions and voice data through sensors to determine the user's emotional state.

[0786] "Display control means" refers to a function that presents the generated questions to the user, allows the user to review the content, and enables editing as needed.

[0787] An "optimization tool" is a function that adjusts the content and difficulty level of questions to match the user's current emotional state, thereby making the survey process more effective.

[0788] "Information gathering means" refers to a system used to collect data based on survey responses from users and to use it for analysis.

[0789] "Information analysis means" refers to technologies for analyzing collected data and sentiment data in real time and providing it to users.

[0790] A "method display means" is a function that visually displays the analysis results and provides them to the user in a way that is easy to understand.

[0791] "Information output means" refers to technology that automatically generates a detailed report based on the final analysis results and provides it to the user.

[0792] This invention is a system for optimizing survey questions according to the user's emotional state. The system consists of a terminal and a server, and specifically utilizes a generative AI model and emotion recognition technology.

[0793] The terminal provides an interface for users to input research objectives and targets. Once information is entered through this interface, the terminal transmits it to the server. The terminal is also equipped with sensors such as a camera and microphone, allowing it to collect emotional data by capturing the user's facial expressions and voice.

[0794] The server receives information about the survey objectives and target audience from the terminal and automatically generates survey questions using a generative AI model. In this process, the generative AI model constructs appropriate questions based on the input prompt sentences. Furthermore, the server activates an emotion engine and analyzes the emotion data obtained from the terminal. The emotion engine determines the user's emotional state and adjusts the content and difficulty of the questions accordingly.

[0795] The generated questions are sent from the server to the terminal and displayed to the user. The user can review these questions and edit them as needed. After the user completes the survey, the response data and sentiment data collected from the terminal are sent to the server.

[0796] The server integrates this data and performs real-time analysis. This analysis includes additional analysis based on emotional states. The analysis results are visualized and presented to the user in dashboard and graph formats. Finally, the server automatically generates a detailed report based on the analysis results and provides it to the user. This report includes insights, including users' emotional responses to the survey, which can improve the accuracy of the survey.

[0797] For example, if a user wants to conduct a "new product user satisfaction survey," setting the prompt to "generate questions about new product user satisfaction and optimize them using sentiment data" will cause the server to automatically generate the relevant questions and optimize their content according to the user's emotional state. This makes the survey more effective and efficient.

[0798] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0799] Step 1:

[0800] The terminal provides an interface for the user to input the survey objectives and target audience. The user uses this interface to input information about the specific objectives and scope of the survey. The entered data is recorded in text format on the terminal and sent directly to the next processing step.

[0801] Step 2:

[0802] The terminal transmits the entered survey objectives and target information to the server. This transmission takes place in real time over the network, and the server uses the received information as basic data for generating questions in the next step.

[0803] Step 3:

[0804] The server activates a generative AI model based on the received survey objective and target. The generative AI model automatically generates questions using the specified prompt text. Specifically, the generative AI model utilizes previously trained data to construct questions and related answer choices that are appropriate for the survey objective. The generated questions are stored as text data on the server.

[0805] Step 4:

[0806] The device uses sensors such as cameras and microphones to collect emotional data, including the user's facial expressions and voice data. This emotional data is processed in real time within the device, converted into a specific format, and sent to the server.

[0807] Step 5:

[0808] The server activates an emotion engine to analyze the received emotion data. This engine uses facial recognition and speech analysis algorithms to identify the user's emotional state. The emotional state is analyzed as numerical or categorical data and used to adjust the questions.

[0809] Step 6:

[0810] The server optimizes the questions based on the generated questions and the results of the sentiment data analysis. Specifically, it adjusts the content and difficulty of the questions according to the user's emotional state, making them easier for the user to answer. These customized questions are then sent back to the terminal.

[0811] Step 7:

[0812] The terminal displays a modified question to the user and prompts them to answer. The user enters their answers to the questions displayed on the terminal screen, and these answers are temporarily recorded on the terminal. The user's answers are sent to a server for intensive analysis in subsequent steps.

[0813] Step 8:

[0814] The server integrates collected response data and sentiment data and performs advanced analysis in real time. This analysis combines statistical methods with sentiment-based insights, and the results are prepared for display as visualized data.

[0815] Step 9:

[0816] The server visualizes the analysis results as dashboards and graphs, and displays them on the user's device. Through the analysis results, users can understand the full scope of the survey and their emotional responses.

[0817] Step 10:

[0818] The server automatically generates the final report, providing users with detailed insights along with the survey results. This report, including sentiment data obtained during the survey, is sent to the user in a format that allows for easy communication.

[0819] (Application Example 2)

[0820] 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".

[0821] In customer feedback processes, presenting uniform questions without considering customers' emotional states can lead to low response rates and compromised feedback quality. This makes it difficult for companies to gain detailed insights to improve the customer experience. Furthermore, if the content and difficulty of survey questions are not adapted to customers' current emotional states, satisfaction and accuracy may be compromised.

[0822] 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.

[0823] In this invention, the server includes adjustment means for recognizing the user's emotional state and optimizing the content or difficulty level of questions based on that emotional state; analysis means for aggregating and analyzing the collected information and user emotional state data in real time; and visualization means for visualizing the analysis results and providing them to the user. This makes it possible to provide a feedback process adapted to each customer's emotional state and improve the quality of the customer experience.

[0824] "Input means" refers to a device or interface for receiving information from the user regarding the purpose and target of information collection.

[0825] "Generation means" refers to a device or system that automatically creates appropriate questions based on information provided by the user.

[0826] "Adjustment means" refers to a device or system that has the function of detecting the user's emotional state and optimizing the content and difficulty level of the questions according to that state.

[0827] "Display means" refers to a device or system that shows automatically generated questions to the user and allows them to edit them as needed.

[0828] "Collection means" refers to a device or system that collects information or data based on questions answered by the user.

[0829] "Analysis means" refers to a device or system for analyzing collected information and sentiment data in real time to obtain additional insights.

[0830] "Visualization means" refers to a device or system for visually displaying analysis results and providing them in a format that is easily understandable to the user.

[0831] "Output means" refers to a device or system that automatically generates and provides the final report to the user.

[0832] This invention provides a system that offers an effective information gathering process that takes into account the user's emotional state. The terminal provides an input means for receiving the purpose and target of information gathering from the user. Once the user inputs the purpose and target of the survey, that information is sent to the server. The server creates automatically generated questions using a generative AI model.

[0833] Furthermore, the server uses emotion recognition software to analyze data acquired from the camera and microphone to recognize the user's emotional state. This analysis utilizes emotion recognition libraries such as OpenCV and Affectiva. Based on the collected emotion data, the server adjusts the content and difficulty of the questions, optimizing them to suit the user's current emotional state.

[0834] At this point, the questions are displayed on the terminal, which the user can review and edit as needed. The emotional state data, along with the user's responses, is sent back to the server. The server uses this data to perform real-time analysis and visualize the results. The visualized information is provided to the user as a dashboard and graphs, and a final report is automatically generated. This allows the user to obtain detailed analysis results, including their emotional response to the survey.

[0835] As a concrete example, imagine a scenario where a customer provides feedback through an app after purchasing a new device. The app recognizes their emotional state from their facial expressions and voice, and then presents appropriate questions based on that. For example, a prompt might look like this:

[0836] "Customers are trying to provide feedback immediately after purchasing a product. Recognize their emotions from camera and audio data and detect one of the following: 'Joy,' 'Stress,' or 'Fatigue.' Based on this, generate the following question: 'How happy are you with your new product?' Provide questions that increase the likelihood of a positive response."

[0837] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0838] Step 1:

[0839] The user operates the terminal and inputs the purpose and target of the information to be collected. This information is transmitted to the server via the input device. The input content becomes the basic data when generating questions in the next step.

[0840] Step 2:

[0841] The server uses a generative AI model based on the received information to automatically generate questions suitable for information gathering. At this time, the user's purpose and target information are used as input, and highly relevant questions are created using natural language processing and set as the output.

[0842] Step 3:

[0843] The terminal displays the generated questions on the screen. The user reviews the displayed questions and edits them as needed. The final list of questions is adjusted to meet the latest collection requirements.

[0844] Step 4:

[0845] The device uses a camera and microphone to collect the user's facial expressions and voice data. This data is input into emotion recognition software to recognize the user's emotional state. The output is data indicating the user's current emotional state.

[0846] Step 5:

[0847] The server optimizes the generated questions based on emotional state data. Detailed questions are displayed if the user is agitated, while concise questions are displayed if they are fatigued. The input is emotional state data, and the output is a list of adjusted questions.

[0848] Step 6:

[0849] Users answer optimized questions and send their response data from their device to the server. The responses become crucial data for the next analysis step.

[0850] Step 7:

[0851] The server integrates response data and sentiment data and performs real-time analysis. This process uses statistical and sentiment analysis techniques to extract insights from user responses. The analysis results are output.

[0852] Step 8:

[0853] The server visualizes the analysis results and provides them to the terminal in the form of dashboards and graphs. The results are presented in a way that allows users to intuitively understand the analysis.

[0854] Step 9:

[0855] The server automatically generates a final report. This report includes detailed feedback information, including the results of a user sentiment analysis. The output is the final report that the user can review and evaluate.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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."

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] 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 as being incorporated by reference.

[0877] The following is further disclosed regarding the embodiments described above.

[0878] (Claim 1)

[0879] An input method for receiving the survey objective and target from the user,

[0880] A generation means for automatically generating questions based on the aforementioned survey objectives and target audience,

[0881] A display means that displays the generated question to the user and makes it editable,

[0882] A data collection method that collects data based on questions answered by the user,

[0883] An analysis means for aggregating and analyzing the collected data in real time,

[0884] A visualization method for visualizing analysis results and providing them to the user,

[0885] An output method for automatically generating the final report,

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The system according to claim 1, characterized in that the generation means generates questions using a generation model.

[0889] (Claim 3)

[0890] The system according to claim 1, characterized in that the analysis means analyzes data using statistical methods.

[0891] "Example 1"

[0892] (Claim 1)

[0893] An input method for receiving information from users regarding the purpose and target of the survey,

[0894] Based on the purpose and target of the aforementioned survey, a generation means for automatically generating questions using a generative model,

[0895] A display means that presents the generated questions to the user and allows them to modify them as needed,

[0896] A means of collecting information based on the questions that the user has finalized,

[0897] An analytical means for processing and interpreting the collected information in real time using statistical methods,

[0898] A visualization means that visually represents the processing results and provides them to the user,

[0899] An output means for automatically generating the final report,

[0900] A system that includes this.

[0901] (Claim 2)

[0902] The system according to claim 1, characterized in that the generation means generates questions by utilizing an algorithmic model.

[0903] (Claim 3)

[0904] The system according to claim 1, characterized in that the analytical means interprets data by utilizing a statistical process.

[0905] "Application Example 1"

[0906] (Claim 1)

[0907] An input means for receiving information and subjects from the user,

[0908] A query generation means that automatically generates questions based on the aforementioned information and subject,

[0909] A presentation means that displays the generated question to the user and makes it editable,

[0910] A means of collecting information based on questions confirmed by the user,

[0911] An analysis means for aggregating and analyzing the collected information in real time,

[0912] A visualization method for visualizing analysis results and providing them to the user,

[0913] An output method that automatically generates a final report and proposes strategies to improve advertising effectiveness,

[0914] An information processing system that includes this.

[0915] (Claim 2)

[0916] The information processing system according to claim 1, characterized in that the query generation means generates questions using a trained model.

[0917] (Claim 3)

[0918] The information processing system according to claim 1, characterized in that the analysis means analyzes information using an analysis method.

[0919] "Example 2 of combining an emotion engine"

[0920] (Claim 1)

[0921] An information input method for receiving the purpose and target of the survey from the user,

[0922] Content generation means for automatically generating questions based on the aforementioned survey objectives and target audience,

[0923] A means of acquiring emotions that senses and collects the emotional state of the user,

[0924] A display control means that displays the generated question to the user and makes it editable,

[0925] An optimization method that optimizes the content of questions based on the user's emotional state,

[0926] An information gathering method that collects verification data based on questions answered by the user,

[0927] Information analysis means for analyzing the collected verification data and sentiment data in real time,

[0928] A method for displaying and providing analysis results to the user,

[0929] An information output means for automatically generating the final report,

[0930] A system that includes this.

[0931] (Claim 2)

[0932] The system according to claim 1, characterized in that the content generation means generates questions using a generation model and adjusts the content and difficulty level of the questions based on sentiment data.

[0933] (Claim 3)

[0934] The system according to claim 1, characterized in that the information analysis means performs analysis based on user sentiment data in addition to statistical methods.

[0935] "Application example 2 when combining with an emotional engine"

[0936] (Claim 1)

[0937] An input method for receiving the purpose and target of information collection from the user,

[0938] A generation means for automatically generating questions based on the purpose and target of the aforementioned information collection,

[0939] An adjustment means that recognizes the user's emotional state and optimizes the content or difficulty level of the questions based on that emotional state,

[0940] A display means that displays the generated question to the user and makes it editable,

[0941] A means of collecting information based on questions confirmed by the user,

[0942] An analysis means for aggregating and analyzing the collected information and user emotional state data in real time,

[0943] A visualization method for visualizing analysis results and providing them to the user,

[0944] An output method for automatically generating the final report,

[0945] A system that includes this.

[0946] (Claim 2)

[0947] The system according to claim 1, characterized in that the generation means generates questions using an AI model.

[0948] (Claim 3)

[0949] The system according to claim 1, characterized in that the analysis means analyzes information using statistical methods and further performs additional analysis based on emotional state. [Explanation of Symbols]

[0950] 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. An input method for receiving the survey objective and target from the user, A generation means for automatically generating questions based on the aforementioned survey objectives and target audience, A display means that displays the generated question to the user and makes it editable, A data collection method that collects data based on questions answered by the user, An analysis means for aggregating and analyzing the collected data in real time, A visualization method for visualizing analysis results and providing them to the user, An output method for automatically generating the final report, A system that includes this.

2. The system according to claim 1, characterized in that the generation means generates questions using a generation model.

3. The system according to claim 1, characterized in that the analysis means analyzes data using statistical methods.

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