system

Abstract

The system according to this embodiment aims to efficiently create, implement, and analyze questionnaires for needs assessment. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, an implementation unit, and an analysis unit. The reception unit receives input of survey content. The generation unit creates a questionnaire based on the information received by the reception unit. The implementation unit administers the questionnaire created by the generation unit. The analysis unit analyzes the questionnaire results collected by the implementation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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 the prior art, there is a problem that it takes time and cost to create, implement, and analyze questionnaires for needs surveys.

[0005] The system according to the embodiment aims to efficiently create, implement, and analyze questionnaires for needs surveys.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, an implementation unit, and an analysis unit. The reception unit receives an input of survey content. The generation unit creates a questionnaire based on the information received by the reception unit. The implementation unit implements the questionnaire created by the generation unit. The analysis unit analyzes the questionnaire results collected by the implementation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently create, administer, and analyze questionnaires for needs assessment. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. 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).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​system according to an embodiment of the present invention is a system for streamlining needs assessment. This AI system works by allowing a user to input a rough survey topic, after which an AI agent sequentially creates a questionnaire, conducts a virtual questionnaire, and analyzes the questionnaire results. This significantly reduces time and costs, enabling rapid and accurate needs assessment. For example, a user might input something like, "Would distributing shares to specific communication service users encourage new contracts or deter cancellations?" This information is input into the AI ​​agent. Next, the AI ​​agent creates a questionnaire based on the input information. The AI ​​agent automatically generates questions according to the survey topic and creates the questionnaire. For example, it generates questions such as "Attractiveness of being distributed shares" and "Effectiveness in proportion to usage amount." The created questionnaire is then conducted as a virtual questionnaire. The AI ​​agent conducts the questionnaire with virtual target individuals and collects responses. This allows for rapid data collection without conducting an actual survey. The collected questionnaire results are analyzed by the AI ​​agent. The AI ​​agent analyzes the response data to determine the presence or absence of needs and the degree of effectiveness. For example, it provides analysis results such as "82% find it very attractive," "effective with a 10% or more increase in usage," "expected new customer retention rate of 70%," "expected churn prevention rate of 80%," and "expected net increase rate of 5%." This allows users to simply input a rough survey request, and the AI ​​agent will handle the creation, implementation, and analysis of the questionnaire, enabling rapid and accurate needs assessment. This significantly reduces time and costs, resulting in efficient research. In this way, the AI ​​system can conduct needs assessments efficiently.

[0029] The AI ​​system according to this embodiment comprises a reception unit, a generation unit, an implementation unit, and an analysis unit. The reception unit receives input of survey content. The reception unit provides, for example, an interface for users to input rough survey content. The generation unit creates a questionnaire based on the information received by the reception unit. The generation unit creates a questionnaire by, for example, automatically generating question items according to the survey content. The generation unit can generate question items based on the survey content using, for example, a text generation AI. The implementation unit conducts the questionnaire created by the generation unit. The implementation unit conducts the questionnaire on, for example, a virtual target audience and collects responses. The implementation unit can provide, for example, a simulation environment for conducting a virtual questionnaire. The analysis unit analyzes the questionnaire results collected by the implementation unit. The analysis unit analyzes the collected response data to determine the presence or absence of needs and the degree of effectiveness. The analysis unit can analyze the response data using, for example, data mining technology. As a result, the AI ​​system according to this embodiment can efficiently perform everything from inputting survey content to creating, conducting, and analyzing questionnaires.

[0030] The reception desk accepts input of survey details. For example, the reception desk provides an interface for users to input rough survey details. Specifically, it provides an interface with text boxes where users can freely input the purpose, target audience, and expected results of the survey, as well as dropdown menus where users can select survey content from a list of options. Furthermore, the reception desk is equipped with natural language processing technology to automatically analyze the content entered by the user and extract necessary information. For example, if a user enters "I want to conduct market research on a new product," the reception desk will extract keywords such as "new product" and "market research" and provide appropriate information to the generation department. The reception desk also provides feedback on the content entered by the user, and if the input is insufficient or ambiguous, it will ask additional questions to confirm more specific survey details. In this way, the reception desk provides an environment in which users can easily and efficiently input survey details and supports the smooth progress of subsequent processing.

[0031] The generation unit creates questionnaires based on information received by the reception unit. For example, the generation unit automatically generates questions tailored to the survey content and creates the questionnaire. Specifically, the generation unit can generate questions based on the survey content using text generation AI. The text generation AI generates questions that are optimal for the user's survey content based on a large amount of pre-trained questionnaire data and question patterns. For example, when conducting a "market survey for a new product," the generation unit automatically creates specific questions such as "Do you have any experience using the new product?" and "What features do you expect from the new product?" The generation unit also has an algorithm to optimize the format and order of questions, allowing it to structure the questionnaire in a way that is easy for respondents to answer. Furthermore, the generation unit also provides an editing function that allows users to add or modify specific questions, enabling customization according to user requests. As a result, the generation unit can quickly create high-quality questionnaires that match the user's research objectives.

[0032] The implementation unit conducts the questionnaires created by the generation unit. For example, the implementation unit conducts questionnaires to virtual subjects and collects responses. Specifically, the implementation unit can provide a simulation environment for conducting virtual questionnaires. This simulation environment can reproduce how virtual subjects actually answer questionnaires and collect realistic data. For example, the implementation unit can set the attributes and behavioral patterns of virtual subjects and simulate the response trends of the questionnaire. The implementation unit can also distribute questionnaires to actual subjects through an online platform and collect responses. For example, it can distribute questionnaire links via email or social media, allowing subjects to answer online. Furthermore, the implementation unit has the function to collect response data in real time and store it in a database, and the collected data is used for analysis by the subsequent analysis unit. This allows the implementation unit to conduct questionnaires efficiently and effectively and collect reliable data.

[0033] The Analysis Department analyzes the survey results collected by the Implementation Department. For example, the Analysis Department analyzes the collected response data to determine the presence or absence of needs and the degree of effectiveness. Specifically, the Analysis Department can analyze the response data using data mining techniques. Data mining techniques are methods for extracting useful patterns and trends from collected data and visualizing survey results. For example, response data can be clustered to identify respondent groups with common characteristics, or association rules can be used to clarify the relationships between specific response patterns. The Analysis Department can also use machine learning algorithms to predict future trends and needs from the response data. For example, based on past response data, fluctuations in demand for specific products or services can be predicted and used to formulate marketing strategies. Furthermore, the Analysis Department has the function to output the analysis results in report format and provide them to users. The reports include an overview of the analysis results, detailed analysis results, and visual information using graphs and charts, and are designed to be easily understood by users. In this way, the Analysis Department can effectively analyze the collected data and provide useful information to users.

[0034] The generation unit can automatically generate questions according to the survey content. The generation unit can, for example, use an algorithm to generate appropriate questions based on the survey content. The generation unit can, for example, use a text generation AI to generate questions based on the survey content. The generation unit can, for example, adjust the type and order of the questions. The generation unit can, for example, adjust the difficulty and level of detail of the questions. As a result, the generation unit improves the efficiency of questionnaire creation by automatically generating questions according to the survey content. For example, rule-based generation algorithms or machine learning-based generation algorithms can be used to generate questions. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can generate questions using a generation AI model that takes the survey content as input and outputs questions.

[0035] The implementation unit can conduct a survey on a virtual target audience and collect responses. The implementation unit can, for example, provide a simulation environment for conducting a survey on a virtual target audience. The implementation unit can, for example, conduct an appropriate survey considering the attribute information of the virtual target audience. The implementation unit can, for example, adjust the survey method based on the age group, occupation, and interests of the virtual target audience. The implementation unit can, for example, collect the response data of the virtual target audience and provide it to the analysis unit. This allows the implementation unit to quickly collect data by conducting a survey on a virtual target audience. The attribute information of the virtual target audience includes, for example, age, gender, occupation, and interests. Some or all of the above processing in the implementation unit may be performed using, for example, AI, or not using AI. For example, the implementation unit can conduct a survey using an AI model that takes the attribute information of a virtual target audience as input and outputs a survey method.

[0036] The analysis department can analyze the collected response data to determine whether there is a need and the degree of effectiveness. The analysis department can, for example, use data mining techniques to analyze the collected response data. The analysis department can, for example, analyze trends and patterns in the response data to determine whether there is a need. The analysis department can, for example, analyze satisfaction levels and areas for improvement in the response data to determine the degree of effectiveness. The analysis department can, for example, provide specific analysis results based on the response data. In this way, the analysis department can determine whether there is a need and the degree of effectiveness by analyzing the collected response data. For example, trends in responses and frequently occurring opinions can be used to determine whether there is a need. For example, satisfaction levels in responses and suggestions for improvement can be used to determine the degree of effectiveness. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can analyze the response data using an AI model that takes the collected response data as input and outputs whether there is a need and the degree of effectiveness.

[0037] The analysis department can provide specific analysis results. For example, the analysis department can provide specific analysis results based on collected response data. For example, the analysis department can analyze trends and patterns in response data and provide specific analysis results. For example, the analysis department can analyze satisfaction levels and areas for improvement in response data and provide specific analysis results. For example, the analysis department can provide specific analysis results in the form of graphs, reports, statistical data, etc., based on response data. In this way, the analysis department deepens the understanding of the survey results by providing specific analysis results. Specific analysis results include, for example, graphs, reports, and statistical data. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can provide analysis results using an AI model that takes collected response data as input and outputs specific analysis results.

[0038] The reception desk can analyze the user's past survey history and suggest the optimal input method. For example, the reception desk can automatically display survey content that the user has frequently entered in the past as a suggestion. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest survey content to be used at a specific time of day based on the user's past survey history. In this way, the reception desk can suggest the optimal input method by analyzing the user's past survey history. Past survey history includes, for example, past response data, survey type, and survey frequency. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can suggest an input method using an AI model that takes the user's past survey history as input and outputs the optimal input method.

[0039] The reception desk can filter the input content based on the user's current work situation and areas of interest when the user enters survey content. For example, the reception desk can prioritize displaying survey content related to the project the user is currently working on. For example, the reception desk can suggest highly relevant survey content based on the user's areas of interest. For example, the reception desk can filter and display appropriate survey content according to the user's work situation. In this way, the reception desk can provide highly relevant survey content by filtering the input content based on the user's work situation and areas of interest. Work situation includes, for example, the type of work, the progress of the work, and the priority of the work. Areas of interest include, for example, topics of interest, areas of expertise, hobbies, etc. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can filter the input content using an AI model that takes the user's work situation and areas of interest as input and filters the input content.

[0040] The reception unit can prioritize accepting highly relevant input content when users input survey details, taking into account their geographical location information. For example, if a user is in a specific region, the reception unit will prioritize displaying survey content related to that region. For example, the reception unit can suggest highly relevant survey content based on the user's current location. For example, the reception unit can filter and display the most relevant survey content by taking into account the user's geographical location information. In this way, the reception unit can provide highly relevant survey content by taking into account the user's geographical location information. Geographical location information includes, for example, GPS data, IP addresses, and location services. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can accept input content using an AI model that takes the user's geographical location information as input and prioritizes accepting highly relevant input content.

[0041] The reception desk can analyze the user's social media activity when inputting survey content and suggest relevant input content. For example, the reception desk can suggest relevant survey content based on topics the user has shown interest in on social media. For example, the reception desk can predict and suggest survey content that the user might be interested in based on their social media activity. For example, the reception desk can analyze the user's statements and posts on social media and display relevant survey content. In this way, the reception desk can suggest relevant survey content by analyzing the user's social media activity. Social media activity includes, for example, posts, follower count, and engagement rate. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can suggest input content using an AI model that takes the user's social media activity as input and suggests relevant input content.

[0042] The generation unit can adjust the level of detail of the questions based on the importance of the survey content when generating the questionnaire. For example, the generation unit can generate detailed questions for important survey content. For example, the generation unit can generate concise questions for low-priority survey content. The generation unit can adjust the number and level of detail of the questions according to the importance of the survey content. In this way, the generation unit can generate appropriate questions by adjusting the level of detail of the questions based on the importance of the survey content. Adjustments to the level of detail of the questions include, for example, the specificity of the questions, the depth of the questions, and the number of questions. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate questions using an AI model that takes the importance of the survey content as input and adjusts the level of detail of the questions.

[0043] The generation unit can apply different generation algorithms depending on the category of the survey content when generating questionnaires. For example, for market research, the generation unit can apply an algorithm that generates questions related to a specific market. For example, for customer satisfaction surveys, the generation unit can apply an algorithm that generates questions to measure customer satisfaction. For example, for internal company surveys, the generation unit can apply an algorithm that generates questions to collect employee opinions. In this way, the generation unit can generate appropriate questionnaires by applying different generation algorithms depending on the category of the survey content. Generation algorithms include, for example, rule-based generation and machine learning-based generation. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can generate questionnaires using an AI model that takes the category of the survey content as input and applies different generation algorithms.

[0044] The generation unit can determine the priority of survey questions based on the submission deadline for the survey content when generating the questionnaire. For example, the generation unit can prioritize generating questions for urgent survey content. For example, the generation unit can prioritize generating questions for survey content with an approaching submission deadline. The generation unit can adjust the priority of survey questions based on the submission deadline for the survey content. In this way, the generation unit can generate appropriate questions by determining the priority of survey questions based on the submission deadline for the survey content. Factors used to determine the priority of survey questions include, for example, importance, urgency, and relevance. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not. For example, the generation unit can take the submission deadline for the survey content as input and generate survey questions using an AI model that determines the priority of survey questions.

[0045] The generation unit can adjust the order of questions based on the relevance of the survey content when generating a questionnaire. For example, the generation unit can prioritize the placement of questions that are highly relevant to the survey content. The generation unit can adjust the order of questions based on the relevance of the survey content. For example, the generation unit can optimize the order of questions by considering the relevance of the survey content. As a result, the generation unit can generate an appropriate questionnaire by adjusting the order of questions based on the relevance of the survey content. Factors for adjusting the order of questions include, for example, logical flow, ease of answering, and relevance of questions. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can generate a questionnaire using an AI model that takes the relevance of the survey content as input and adjusts the order of questions.

[0046] The implementation unit can optimize the implementation method when conducting a survey by considering the attribute information of a hypothetical target. For example, the implementation unit can select an appropriate survey format according to the age group of the hypothetical target. For example, the implementation unit can conduct a survey that includes highly relevant questions according to the occupation of the hypothetical target. For example, the implementation unit can select the optimal survey format based on the interests of the hypothetical target. In this way, the implementation unit can conduct an appropriate survey by considering the attribute information of the hypothetical target. The attribute information of the hypothetical target includes, for example, age, gender, occupation, etc. Some or all of the above processing in the implementation unit may be performed using, for example, AI, or not using AI. For example, the implementation unit can conduct a survey using an AI model that takes the attribute information of the hypothetical target as input and optimizes the implementation method.

[0047] The implementation unit can customize the implementation method when conducting a survey by referring to the respondents' past response history. For example, the implementation unit can conduct a survey that includes highly relevant questions based on the content of surveys that respondents have answered in the past. For example, the implementation unit can conduct a survey that includes questions that are likely to be of interest to respondents based on their past response history. For example, the implementation unit can analyze respondents' past response history and select the most suitable survey format. This allows the implementation unit to conduct an appropriate survey by referring to respondents' past response history. Past response history includes, for example, past response content, response trends, and response frequency. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, the implementation unit can conduct a survey using an AI model that takes respondents' past response history as input and customizes the implementation method.

[0048] The implementation unit can select an implementation method when conducting a survey, taking into account the geographical distribution of the respondents. For example, if the respondents are concentrated in a particular area, the implementation unit can conduct a survey that includes questions relevant to that area. For example, the implementation unit can select the optimal survey format based on the geographical distribution of the respondents. For example, the implementation unit can conduct a survey that includes highly relevant questions, taking into account the geographical distribution of the respondents. In this way, the implementation unit can conduct an appropriate survey by taking into account the geographical distribution of the respondents. Geographical distribution includes, for example, regional response trends and region-specific needs. Some or all of the above processing by the implementation unit may be performed using, for example, AI, or not using AI. For example, the implementation unit can conduct a survey using an AI model that takes the geographical distribution of the respondents as input and selects an implementation method.

[0049] The implementation department can analyze the social media activity of the target audience and propose an implementation method when conducting a survey. For example, the implementation department can conduct a survey that includes relevant questions based on topics that the target audience has shown interest in on social media. For example, the implementation department can conduct a survey that includes questions that are likely to be of interest to the target audience based on their social media activity. For example, the implementation department can analyze the comments and posts of the target audience on social media and conduct a survey that includes relevant questions. In this way, the implementation department can conduct an appropriate survey by analyzing the target audience's social media activity. Social media activity includes, for example, the content of posts, the number of followers, and the engagement rate. Some or all of the above processing by the implementation department may be performed using AI, for example, or not using AI. For example, the implementation department can conduct a survey using an AI model that takes the target audience's social media activity as input and proposes an implementation method.

[0050] The analysis department can predict current survey results by referring to past survey data during analysis. For example, the analysis department predicts current survey results based on past survey data. For example, the analysis department can predict current survey results by analyzing trends from past survey data. For example, the analysis department can predict trends in current survey results by referring to past survey data. Thus, the analysis department can predict current survey results by referring to past survey data. Past survey data includes, for example, past responses, survey types, and survey frequencies. Some or all of the above processing in the analysis department may be performed using, for example, AI, or not using AI. For example, the analysis department can predict survey results using an AI model that takes past survey data as input and predicts current survey results.

[0051] The analysis department can apply different analytical methods to each category of research content during analysis. For example, for market research, the analysis department can apply analytical methods related to a specific market. For example, for customer satisfaction surveys, the analysis department can apply analytical methods that measure customer satisfaction. For example, for internal surveys, the analysis department can apply analytical methods that collect employee opinions. This allows the analysis department to perform highly accurate analysis by applying different analytical methods to each category of research content. Analytical methods include, for example, statistical methods, data mining methods, and text analysis methods. Some or all of the above processing in the analysis department may be performed using, for example, AI, or not using AI. For example, the analysis department can analyze the survey results using an AI model that takes the categories of research content as input and applies different analytical methods.

[0052] The analysis department can analyze changes in analysis results based on the submission timing of survey data. For example, the analysis department can compare changes in analysis results for survey data submitted at different times. For example, the analysis department can analyze trends in analysis results based on submission timing. For example, the analysis department can predict changes in analysis results for each submission time. This allows the analysis department to grasp trends by analyzing changes in analysis results based on the submission timing of survey data. Submission timing includes, for example, the submission deadline, the timing of submission, and the frequency of submission. Some or all of the above processes in the analysis department may be performed using, for example, AI, or not using AI. For example, the analysis department can analyze survey results using an AI model that takes submission timing as input and analyzes changes in analysis results.

[0053] The analysis department can provide analysis results by referring to relevant market data related to the survey content during the analysis. For example, the analysis department provides analysis results based on market data related to the survey content. For example, the analysis department can supplement the analysis results by referring to relevant market data related to the survey content. For example, the analysis department can improve the reliability of the analysis results based on market data related to the survey content. In this way, the analysis department can provide highly reliable analysis results by referring to relevant market data related to the survey content. Relevant market data includes, for example, market trends, competitor data, and consumer trends. Some or all of the above processing in the analysis department may be performed using, for example, AI, or not using AI. For example, the analysis department can analyze the survey results using an AI model that takes relevant market data as input and provides analysis results.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The generation unit can adjust the level of detail of the questions based on the importance of the survey content when generating the questionnaire. For example, it can generate detailed questions for important survey content, and concise questions for lower-priority survey content. Furthermore, it can adjust the number and level of detail of the questions according to the importance of the survey content. In this way, the generation unit can generate appropriate questions by adjusting the level of detail of the questions based on the importance of the survey content. Adjusting the level of detail of the questions includes the specificity of the questions, the depth of the questions, and the number of questions. Some or all of the above processing in the generation unit may be performed using AI, or it may be performed without using AI. For example, the generation unit can generate questions using an AI model that takes the importance of the survey content as input and adjusts the level of detail of the questions.

[0056] The reception desk can analyze the user's past survey history and suggest the optimal input method. For example, it can automatically display survey content that the user has frequently entered in the past as a suggestion. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest survey content to be used during specific time periods based on the user's past survey history. In this way, the reception desk can suggest the optimal input method by analyzing the user's past survey history. Past survey history includes past response data, survey type, and survey frequency. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can suggest an input method using an AI model that takes the user's past survey history as input and outputs the optimal input method.

[0057] The generation unit can apply different generation algorithms depending on the category of the survey content when generating questionnaires. For example, for market research, an algorithm that generates questions related to a specific market can be applied. For customer satisfaction surveys, an algorithm that generates questions to measure customer satisfaction can be applied. Furthermore, for internal company surveys, an algorithm that generates questions to collect employee opinions can be applied. In this way, the generation unit can generate appropriate questionnaires by applying different generation algorithms depending on the category of the survey content. Generation algorithms include rule-based generation and machine learning-based generation. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can generate questionnaires using an AI model that takes the category of the survey content as input and applies different generation algorithms.

[0058] The implementation unit can optimize the survey method by considering the attribute information of a hypothetical target group when conducting the survey. For example, it can select an appropriate survey format according to the age group of the hypothetical target group. It can also conduct a survey that includes highly relevant questions according to the occupation of the hypothetical target group. Furthermore, it can select the optimal survey format based on the interests of the hypothetical target group. In this way, the implementation unit can conduct an appropriate survey by considering the attribute information of the hypothetical target group. The attribute information of the hypothetical target group includes age, gender, occupation, etc. Some or all of the above processing in the implementation unit may be performed using AI or not. For example, the implementation unit can conduct the survey using an AI model that optimizes the implementation method by taking the attribute information of the hypothetical target group as input.

[0059] The analysis department can predict current survey results by referring to past survey data during analysis. For example, it can predict current survey results based on past survey data. It can also predict current survey results by analyzing trends from past survey data. Furthermore, it can predict trends in current survey results by referring to past survey data. In this way, the analysis department can predict current survey results by referring to past survey data. Past survey data includes past responses, survey types, and survey frequencies. Some or all of the above processing in the analysis department may be performed using AI or not. For example, the analysis department can use past survey data as input and predict current survey results using an AI model that predicts current survey results.

[0060] The implementation unit can customize the survey method by referring to the respondents' past response history when conducting the survey. For example, it can conduct a survey that includes highly relevant questions based on the content of surveys the respondents have answered in the past. It can also conduct a survey that includes questions that are likely to interest the respondents based on their past response history. Furthermore, it can analyze the respondents' past response history and select the most suitable survey format. In this way, the implementation unit can conduct an appropriate survey by referring to the respondents' past response history. Past response history includes past response content, response trends, and response frequency. Some or all of the above processing in the implementation unit may be performed using AI or not. For example, the implementation unit can conduct a survey using an AI model that takes the respondents' past response history as input and customizes the implementation method.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk accepts input of survey details. For example, it provides an interface for users to input rough survey details. Step 2: The generation unit creates a questionnaire based on the information received by the reception unit. For example, it automatically generates questions according to the survey content and creates the questionnaire. The generation unit can generate questions based on the survey content using text generation AI. Step 3: The implementation unit conducts the questionnaire created by the generation unit. For example, it conducts the questionnaire on a virtual target group and collects responses. The implementation unit can provide a simulation environment for conducting the virtual questionnaire. Step 4: The analysis department analyzes the survey results collected by the implementation department. For example, it analyzes the collected response data to determine whether there is a need and the degree of effectiveness. The analysis department can use data mining techniques to analyze the response data.

[0063] (Example of form 2) The AI ​​system according to an embodiment of the present invention is a system for streamlining needs assessment. This AI system works by allowing a user to input a rough survey topic, after which an AI agent sequentially creates a questionnaire, conducts a virtual questionnaire, and analyzes the questionnaire results. This significantly reduces time and costs, enabling rapid and accurate needs assessment. For example, a user might input something like, "Would distributing shares to specific communication service users encourage new contracts or deter cancellations?" This information is input into the AI ​​agent. Next, the AI ​​agent creates a questionnaire based on the input information. The AI ​​agent automatically generates questions according to the survey topic and creates the questionnaire. For example, it generates questions such as "Attractiveness of being distributed shares" and "Effectiveness in proportion to usage amount." The created questionnaire is then conducted as a virtual questionnaire. The AI ​​agent conducts the questionnaire with virtual target individuals and collects responses. This allows for rapid data collection without conducting an actual survey. The collected questionnaire results are analyzed by the AI ​​agent. The AI ​​agent analyzes the response data to determine the presence or absence of needs and the degree of effectiveness. For example, it provides analysis results such as "82% find it very attractive," "effective with a 10% or more increase in usage," "expected new customer retention rate of 70%," "expected churn prevention rate of 80%," and "expected net increase rate of 5%." This allows users to simply input a rough survey request, and the AI ​​agent will handle the creation, implementation, and analysis of the questionnaire, enabling rapid and accurate needs assessment. This significantly reduces time and costs, resulting in efficient research. In this way, the AI ​​system can conduct needs assessments efficiently.

[0064] The AI ​​system according to this embodiment comprises a reception unit, a generation unit, an implementation unit, and an analysis unit. The reception unit receives input of survey content. The reception unit provides, for example, an interface for users to input rough survey content. The generation unit creates a questionnaire based on the information received by the reception unit. The generation unit creates a questionnaire by, for example, automatically generating question items according to the survey content. The generation unit can generate question items based on the survey content using, for example, a text generation AI. The implementation unit conducts the questionnaire created by the generation unit. The implementation unit conducts the questionnaire on, for example, a virtual target audience and collects responses. The implementation unit can provide, for example, a simulation environment for conducting a virtual questionnaire. The analysis unit analyzes the questionnaire results collected by the implementation unit. The analysis unit analyzes the collected response data to determine the presence or absence of needs and the degree of effectiveness. The analysis unit can analyze the response data using, for example, data mining technology. As a result, the AI ​​system according to this embodiment can efficiently perform everything from inputting survey content to creating, conducting, and analyzing questionnaires.

[0065] The reception desk accepts input of survey details. For example, the reception desk provides an interface for users to input rough survey details. Specifically, it provides an interface with text boxes where users can freely input the purpose, target audience, and expected results of the survey, as well as dropdown menus where users can select survey content from a list of options. Furthermore, the reception desk is equipped with natural language processing technology to automatically analyze the content entered by the user and extract necessary information. For example, if a user enters "I want to conduct market research on a new product," the reception desk will extract keywords such as "new product" and "market research" and provide appropriate information to the generation department. The reception desk also provides feedback on the content entered by the user, and if the input is insufficient or ambiguous, it will ask additional questions to confirm more specific survey details. In this way, the reception desk provides an environment in which users can easily and efficiently input survey details and supports the smooth progress of subsequent processing.

[0066] The generation unit creates questionnaires based on information received by the reception unit. For example, the generation unit automatically generates questions tailored to the survey content and creates the questionnaire. Specifically, the generation unit can generate questions based on the survey content using text generation AI. The text generation AI generates questions that are optimal for the user's survey content based on a large amount of pre-trained questionnaire data and question patterns. For example, when conducting a "market survey for a new product," the generation unit automatically creates specific questions such as "Do you have any experience using the new product?" and "What features do you expect from the new product?" The generation unit also has an algorithm to optimize the format and order of questions, allowing it to structure the questionnaire in a way that is easy for respondents to answer. Furthermore, the generation unit also provides an editing function that allows users to add or modify specific questions, enabling customization according to user requests. As a result, the generation unit can quickly create high-quality questionnaires that match the user's research objectives.

[0067] The implementation unit conducts the questionnaires created by the generation unit. For example, the implementation unit conducts questionnaires to virtual subjects and collects responses. Specifically, the implementation unit can provide a simulation environment for conducting virtual questionnaires. This simulation environment can reproduce how virtual subjects actually answer questionnaires and collect realistic data. For example, the implementation unit can set the attributes and behavioral patterns of virtual subjects and simulate the response trends of the questionnaire. The implementation unit can also distribute questionnaires to actual subjects through an online platform and collect responses. For example, it can distribute questionnaire links via email or social media, allowing subjects to answer online. Furthermore, the implementation unit has the function to collect response data in real time and store it in a database, and the collected data is used for analysis by the subsequent analysis unit. This allows the implementation unit to conduct questionnaires efficiently and effectively and collect reliable data.

[0068] The Analysis Department analyzes the survey results collected by the Implementation Department. For example, the Analysis Department analyzes the collected response data to determine the presence or absence of needs and the degree of effectiveness. Specifically, the Analysis Department can analyze the response data using data mining techniques. Data mining techniques are methods for extracting useful patterns and trends from collected data and visualizing survey results. For example, response data can be clustered to identify respondent groups with common characteristics, or association rules can be used to clarify the relationships between specific response patterns. The Analysis Department can also use machine learning algorithms to predict future trends and needs from the response data. For example, based on past response data, fluctuations in demand for specific products or services can be predicted and used to formulate marketing strategies. Furthermore, the Analysis Department has the function to output the analysis results in report format and provide them to users. The reports include an overview of the analysis results, detailed analysis results, and visual information using graphs and charts, and are designed to be easily understood by users. In this way, the Analysis Department can effectively analyze the collected data and provide useful information to users.

[0069] The generation unit can automatically generate questions according to the survey content. The generation unit can, for example, use an algorithm to generate appropriate questions based on the survey content. The generation unit can, for example, use a text generation AI to generate questions based on the survey content. The generation unit can, for example, adjust the type and order of the questions. The generation unit can, for example, adjust the difficulty and level of detail of the questions. As a result, the generation unit improves the efficiency of questionnaire creation by automatically generating questions according to the survey content. For example, rule-based generation algorithms or machine learning-based generation algorithms can be used to generate questions. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can generate questions using a generation AI model that takes the survey content as input and outputs questions.

[0070] The implementation unit can conduct a survey on a virtual target audience and collect responses. The implementation unit can, for example, provide a simulation environment for conducting a survey on a virtual target audience. The implementation unit can, for example, conduct an appropriate survey considering the attribute information of the virtual target audience. The implementation unit can, for example, adjust the survey method based on the age group, occupation, and interests of the virtual target audience. The implementation unit can, for example, collect the response data of the virtual target audience and provide it to the analysis unit. This allows the implementation unit to quickly collect data by conducting a survey on a virtual target audience. The attribute information of the virtual target audience includes, for example, age, gender, occupation, and interests. Some or all of the above processing in the implementation unit may be performed using, for example, AI, or not using AI. For example, the implementation unit can conduct a survey using an AI model that takes the attribute information of a virtual target audience as input and outputs a survey method.

[0071] The analysis department can analyze the collected response data to determine whether there is a need and the degree of effectiveness. The analysis department can, for example, use data mining techniques to analyze the collected response data. The analysis department can, for example, analyze trends and patterns in the response data to determine whether there is a need. The analysis department can, for example, analyze satisfaction levels and areas for improvement in the response data to determine the degree of effectiveness. The analysis department can, for example, provide specific analysis results based on the response data. In this way, the analysis department can determine whether there is a need and the degree of effectiveness by analyzing the collected response data. For example, trends in responses and frequently occurring opinions can be used to determine whether there is a need. For example, satisfaction levels in responses and suggestions for improvement can be used to determine the degree of effectiveness. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can analyze the response data using an AI model that takes the collected response data as input and outputs whether there is a need and the degree of effectiveness.

[0072] The analysis department can provide specific analysis results. For example, the analysis department can provide specific analysis results based on collected response data. For example, the analysis department can analyze trends and patterns in response data and provide specific analysis results. For example, the analysis department can analyze satisfaction levels and areas for improvement in response data and provide specific analysis results. For example, the analysis department can provide specific analysis results in the form of graphs, reports, statistical data, etc., based on response data. In this way, the analysis department deepens the understanding of the survey results by providing specific analysis results. Specific analysis results include, for example, graphs, reports, and statistical data. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can provide analysis results using an AI model that takes collected response data as input and outputs specific analysis results.

[0073] The reception desk can estimate the user's emotions and adjust the input interface for the survey based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. For example, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of the survey. In this way, the reception desk reduces the user's input burden by adjusting the input interface according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can adjust the input interface using an AI model that takes user emotion data as input and outputs a method for adjusting the input interface.

[0074] The reception desk can analyze the user's past survey history and suggest the optimal input method. For example, the reception desk can automatically display survey content that the user has frequently entered in the past as a suggestion. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest survey content to be used at a specific time of day based on the user's past survey history. In this way, the reception desk can suggest the optimal input method by analyzing the user's past survey history. Past survey history includes, for example, past response data, survey type, and survey frequency. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can suggest an input method using an AI model that takes the user's past survey history as input and outputs the optimal input method.

[0075] The reception desk can filter the input content based on the user's current work situation and areas of interest when the user enters survey content. For example, the reception desk can prioritize displaying survey content related to the project the user is currently working on. For example, the reception desk can suggest highly relevant survey content based on the user's areas of interest. For example, the reception desk can filter and display appropriate survey content according to the user's work situation. In this way, the reception desk can provide highly relevant survey content by filtering the input content based on the user's work situation and areas of interest. Work situation includes, for example, the type of work, the progress of the work, and the priority of the work. Areas of interest include, for example, topics of interest, areas of expertise, hobbies, etc. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can filter the input content using an AI model that takes the user's work situation and areas of interest as input and filters the input content.

[0076] The reception unit can estimate the user's emotions and prioritize input content based on the estimated emotions. For example, if the user is stressed, the reception unit can prioritize displaying important input content. For example, if the user is relaxed, the reception unit can prioritize displaying detailed input content. For example, if the user is in a hurry, the reception unit can prioritize displaying content that can be entered quickly. In this way, the reception unit can prioritize important content by prioritizing input content according to the user's emotions. User emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can take user emotion data as input and determine the priority of input content using an AI model that determines the priority of input content.

[0077] The reception unit can prioritize accepting highly relevant input content when users input survey details, taking into account their geographical location information. For example, if a user is in a specific region, the reception unit will prioritize displaying survey content related to that region. For example, the reception unit can suggest highly relevant survey content based on the user's current location. For example, the reception unit can filter and display the most relevant survey content by taking into account the user's geographical location information. In this way, the reception unit can provide highly relevant survey content by taking into account the user's geographical location information. Geographical location information includes, for example, GPS data, IP addresses, and location services. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can accept input content using an AI model that takes the user's geographical location information as input and prioritizes accepting highly relevant input content.

[0078] The reception desk can analyze the user's social media activity when inputting survey content and suggest relevant input content. For example, the reception desk can suggest relevant survey content based on topics the user has shown interest in on social media. For example, the reception desk can predict and suggest survey content that the user might be interested in based on their social media activity. For example, the reception desk can analyze the user's statements and posts on social media and display relevant survey content. In this way, the reception desk can suggest relevant survey content by analyzing the user's social media activity. Social media activity includes, for example, posts, follower count, and engagement rate. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can suggest input content using an AI model that takes the user's social media activity as input and suggests relevant input content.

[0079] The generation unit can estimate the user's emotions and adjust the wording of the questionnaire based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a questionnaire using friendly language. For example, if the user is in a hurry, the generation unit can generate a concise and to-the-point questionnaire. For example, if the user is excited, the generation unit can generate a questionnaire using a visually stimulating design. In this way, the generation unit improves the response rate by adjusting the wording of the questionnaire according to the user's emotions. The adjustment of the wording of the questionnaire is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI is, for example, a text generation AI or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can take user emotion data as input and generate a questionnaire using an AI model that adjusts the wording of the questionnaire.

[0080] The generation unit can adjust the level of detail of the questions based on the importance of the survey content when generating the questionnaire. For example, the generation unit can generate detailed questions for important survey content. For example, the generation unit can generate concise questions for low-priority survey content. The generation unit can adjust the number and level of detail of the questions according to the importance of the survey content. In this way, the generation unit can generate appropriate questions by adjusting the level of detail of the questions based on the importance of the survey content. Adjustments to the level of detail of the questions include, for example, the specificity of the questions, the depth of the questions, and the number of questions. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate questions using an AI model that takes the importance of the survey content as input and adjusts the level of detail of the questions.

[0081] The generation unit can apply different generation algorithms depending on the category of the survey content when generating questionnaires. For example, for market research, the generation unit can apply an algorithm that generates questions related to a specific market. For example, for customer satisfaction surveys, the generation unit can apply an algorithm that generates questions to measure customer satisfaction. For example, for internal company surveys, the generation unit can apply an algorithm that generates questions to collect employee opinions. In this way, the generation unit can generate appropriate questionnaires by applying different generation algorithms depending on the category of the survey content. Generation algorithms include, for example, rule-based generation and machine learning-based generation. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can generate questionnaires using an AI model that takes the category of the survey content as input and applies different generation algorithms.

[0082] The generation unit can estimate the user's emotions and adjust the length of the questionnaire based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point questionnaire. For example, if the user is relaxed, the generation unit can generate a longer questionnaire with detailed explanations. For example, if the user is excited, the generation unit can generate a questionnaire with visually stimulating effects. In this way, the generation unit improves the response rate by adjusting the length of the questionnaire according to the user's emotions. The adjustment of the questionnaire length is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI is, for example, a text generation AI or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can take user emotion data as input and generate a questionnaire using an AI model that adjusts the length of the questionnaire.

[0083] The generation unit can determine the priority of survey questions based on the submission deadline for the survey content when generating the questionnaire. For example, the generation unit can prioritize generating questions for urgent survey content. For example, the generation unit can prioritize generating questions for survey content with an approaching submission deadline. The generation unit can adjust the priority of survey questions based on the submission deadline for the survey content. In this way, the generation unit can generate appropriate questions by determining the priority of survey questions based on the submission deadline for the survey content. Factors used to determine the priority of survey questions include, for example, importance, urgency, and relevance. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not. For example, the generation unit can take the submission deadline for the survey content as input and generate survey questions using an AI model that determines the priority of survey questions.

[0084] The generation unit can adjust the order of questions based on the relevance of the survey content when generating a questionnaire. For example, the generation unit can prioritize the placement of questions that are highly relevant to the survey content. The generation unit can adjust the order of questions based on the relevance of the survey content. For example, the generation unit can optimize the order of questions by considering the relevance of the survey content. As a result, the generation unit can generate an appropriate questionnaire by adjusting the order of questions based on the relevance of the survey content. Factors for adjusting the order of questions include, for example, logical flow, ease of answering, and relevance of questions. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can generate a questionnaire using an AI model that takes the relevance of the survey content as input and adjusts the order of questions.

[0085] The implementation unit can estimate the user's emotions and adjust the survey method based on the estimated emotions. For example, if the user is relaxed, the implementation unit can conduct a survey using friendly language. For example, if the user is in a hurry, the implementation unit can conduct a concise and to-the-point survey. For example, if the user is excited, the implementation unit can conduct a survey using a visually stimulating design. By adjusting the survey method according to the user's emotions, the implementation unit can improve the response rate. The adjustment of the survey method is achieved, for example, using an emotion estimation function with an emotion engine or generative AI. Generative AI is, for example, a text generation AI or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, the implementation unit can conduct a survey using an AI model that takes user emotion data as input and adjusts the survey method.

[0086] The implementation unit can optimize the implementation method when conducting a survey by considering the attribute information of a hypothetical target. For example, the implementation unit can select an appropriate survey format according to the age group of the hypothetical target. For example, the implementation unit can conduct a survey that includes highly relevant questions according to the occupation of the hypothetical target. For example, the implementation unit can select the optimal survey format based on the interests of the hypothetical target. In this way, the implementation unit can conduct an appropriate survey by considering the attribute information of the hypothetical target. The attribute information of the hypothetical target includes, for example, age, gender, occupation, etc. Some or all of the above processing in the implementation unit may be performed using, for example, AI, or not using AI. For example, the implementation unit can conduct a survey using an AI model that takes the attribute information of the hypothetical target as input and optimizes the implementation method.

[0087] The implementation unit can customize the implementation method when conducting a survey by referring to the respondents' past response history. For example, the implementation unit can conduct a survey that includes highly relevant questions based on the content of surveys that respondents have answered in the past. For example, the implementation unit can conduct a survey that includes questions that are likely to be of interest to respondents based on their past response history. For example, the implementation unit can analyze respondents' past response history and select the most suitable survey format. This allows the implementation unit to conduct an appropriate survey by referring to respondents' past response history. Past response history includes, for example, past response content, response trends, and response frequency. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, the implementation unit can conduct a survey using an AI model that takes respondents' past response history as input and customizes the implementation method.

[0088] The implementation unit can estimate the user's emotions and adjust the order in which the questionnaire is administered based on the estimated emotions. For example, if the user is stressed, the implementation unit can prioritize important questions. For example, if the user is relaxed, the implementation unit can prioritize detailed questions. For example, if the user is in a hurry, the implementation unit can prioritize questions that can be answered quickly. In this way, the implementation unit can improve the response rate by adjusting the order in which the questionnaire is administered according to the user's emotions. The adjustment of the order in which the questionnaire is administered is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, for example, a text generation AI or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, the implementation unit can administer the questionnaire using an AI model that takes user emotion data as input and adjusts the order in which the questionnaire is administered.

[0089] The implementation unit can select an implementation method when conducting a survey, taking into account the geographical distribution of the respondents. For example, if the respondents are concentrated in a particular area, the implementation unit can conduct a survey that includes questions relevant to that area. For example, the implementation unit can select the optimal survey format based on the geographical distribution of the respondents. For example, the implementation unit can conduct a survey that includes highly relevant questions, taking into account the geographical distribution of the respondents. In this way, the implementation unit can conduct an appropriate survey by taking into account the geographical distribution of the respondents. Geographical distribution includes, for example, regional response trends and region-specific needs. Some or all of the above processing by the implementation unit may be performed using, for example, AI, or not using AI. For example, the implementation unit can conduct a survey using an AI model that takes the geographical distribution of the respondents as input and selects an implementation method.

[0090] The implementation department can analyze the social media activity of the target audience and propose an implementation method when conducting a survey. For example, the implementation department can conduct a survey that includes relevant questions based on topics that the target audience has shown interest in on social media. For example, the implementation department can conduct a survey that includes questions that are likely to be of interest to the target audience based on their social media activity. For example, the implementation department can analyze the comments and posts of the target audience on social media and conduct a survey that includes relevant questions. In this way, the implementation department can conduct an appropriate survey by analyzing the target audience's social media activity. Social media activity includes, for example, the content of posts, the number of followers, and the engagement rate. Some or all of the above processing by the implementation department may be performed using AI, for example, or not using AI. For example, the implementation department can conduct a survey using an AI model that takes the target audience's social media activity as input and proposes an implementation method.

[0091] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, the analysis unit can provide an easy-to-understand display by adjusting the display method of the analysis results according to the user's emotions. The adjustment of the display method of the analysis results is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, for example, a text generation AI or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can take user emotion data as input and display the analysis results using an AI model that adjusts the display method of the analysis results.

[0092] The analysis department can predict current survey results by referring to past survey data during analysis. For example, the analysis department predicts current survey results based on past survey data. For example, the analysis department can predict current survey results by analyzing trends from past survey data. For example, the analysis department can predict trends in current survey results by referring to past survey data. Thus, the analysis department can predict current survey results by referring to past survey data. Past survey data includes, for example, past responses, survey types, and survey frequencies. Some or all of the above processing in the analysis department may be performed using, for example, AI, or not using AI. For example, the analysis department can predict survey results using an AI model that takes past survey data as input and predicts current survey results.

[0093] The analysis department can apply different analytical methods to each category of research content during analysis. For example, for market research, the analysis department can apply analytical methods related to a specific market. For example, for customer satisfaction surveys, the analysis department can apply analytical methods that measure customer satisfaction. For example, for internal surveys, the analysis department can apply analytical methods that collect employee opinions. This allows the analysis department to perform highly accurate analysis by applying different analytical methods to each category of research content. Analytical methods include, for example, statistical methods, data mining methods, and text analysis methods. Some or all of the above processing in the analysis department may be performed using, for example, AI, or not using AI. For example, the analysis department can analyze the survey results using an AI model that takes the categories of research content as input and applies different analytical methods.

[0094] The analysis unit can estimate the user's emotions and adjust the importance of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can prioritize displaying important analysis results. For example, if the user is relaxed, the analysis unit can prioritize displaying detailed analysis results. For example, if the user is in a hurry, the analysis unit can prioritize displaying concise analysis results. In this way, the analysis unit can prioritize displaying important information by adjusting the importance of the analysis results according to the user's emotions. The adjustment of the importance of the analysis results is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, for example, text generation AI or multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can take user emotion data as input and display the analysis results using an AI model that adjusts the importance of the analysis results.

[0095] The analysis department can analyze changes in analysis results based on the submission timing of survey data. For example, the analysis department can compare changes in analysis results for survey data submitted at different times. For example, the analysis department can analyze trends in analysis results based on submission timing. For example, the analysis department can predict changes in analysis results for each submission time. This allows the analysis department to grasp trends by analyzing changes in analysis results based on the submission timing of survey data. Submission timing includes, for example, the submission deadline, the timing of submission, and the frequency of submission. Some or all of the above processes in the analysis department may be performed using, for example, AI, or not using AI. For example, the analysis department can analyze survey results using an AI model that takes submission timing as input and analyzes changes in analysis results.

[0096] The analysis department can provide analysis results by referring to relevant market data related to the survey content during the analysis. For example, the analysis department provides analysis results based on market data related to the survey content. For example, the analysis department can supplement the analysis results by referring to relevant market data related to the survey content. For example, the analysis department can improve the reliability of the analysis results based on market data related to the survey content. In this way, the analysis department can provide highly reliable analysis results by referring to relevant market data related to the survey content. Relevant market data includes, for example, market trends, competitor data, and consumer trends. Some or all of the above processing in the analysis department may be performed using, for example, AI, or not using AI. For example, the analysis department can analyze the survey results using an AI model that takes relevant market data as input and provides analysis results.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The reception desk can estimate the user's emotions and adjust the input interface for the survey based on the estimated emotions. For example, if the user is stressed, a simple interface can be provided, minimizing the input steps. If the user is relaxed, detailed input options can be provided, and customizable input methods can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized, allowing for quick input of the survey content. In this way, the reception desk can reduce the user's input burden by adjusting the input interface according to the user's emotions. The estimation of the user's emotions is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can adjust the input interface using an AI model that takes user emotion data as input and outputs methods for adjusting the input interface.

[0099] The generation unit can adjust the level of detail of the questions based on the importance of the survey content when generating the questionnaire. For example, it can generate detailed questions for important survey content, and concise questions for lower-priority survey content. Furthermore, it can adjust the number and level of detail of the questions according to the importance of the survey content. In this way, the generation unit can generate appropriate questions by adjusting the level of detail of the questions based on the importance of the survey content. Adjusting the level of detail of the questions includes the specificity of the questions, the depth of the questions, and the number of questions. Some or all of the above processing in the generation unit may be performed using AI, or it may be performed without using AI. For example, the generation unit can generate questions using an AI model that takes the importance of the survey content as input and adjusts the level of detail of the questions.

[0100] The implementation unit can estimate the user's emotions and adjust the survey method based on the estimated emotions. For example, if the user is relaxed, the survey can be conducted using friendly language. If the user is in a hurry, a concise and to-the-point survey can be conducted. Furthermore, if the user is excited, a survey can be conducted using a visually stimulating design. In this way, the implementation unit can improve the response rate by adjusting the survey method according to the user's emotions. The adjustment of the survey method is achieved using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the implementation unit may be performed using AI or not. For example, the implementation unit can conduct a survey using an AI model that takes user emotion data as input and adjusts the survey method.

[0101] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. In this way, the analysis unit can make the display easy to understand by adjusting the display method of the analysis results according to the user's emotions. The adjustment of the display method of the analysis results is achieved using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can take user emotion data as input and display the analysis results using an AI model that adjusts the display method of the analysis results.

[0102] The reception desk can analyze the user's past survey history and suggest the optimal input method. For example, it can automatically display survey content that the user has frequently entered in the past as a suggestion. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest survey content to be used during specific time periods based on the user's past survey history. In this way, the reception desk can suggest the optimal input method by analyzing the user's past survey history. Past survey history includes past response data, survey type, and survey frequency. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can suggest an input method using an AI model that takes the user's past survey history as input and outputs the optimal input method.

[0103] The generation unit can apply different generation algorithms depending on the category of the survey content when generating questionnaires. For example, for market research, an algorithm that generates questions related to a specific market can be applied. For customer satisfaction surveys, an algorithm that generates questions to measure customer satisfaction can be applied. Furthermore, for internal company surveys, an algorithm that generates questions to collect employee opinions can be applied. In this way, the generation unit can generate appropriate questionnaires by applying different generation algorithms depending on the category of the survey content. Generation algorithms include rule-based generation and machine learning-based generation. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can generate questionnaires using an AI model that takes the category of the survey content as input and applies different generation algorithms.

[0104] The implementation unit can optimize the survey method by considering the attribute information of a hypothetical target group when conducting the survey. For example, it can select an appropriate survey format according to the age group of the hypothetical target group. It can also conduct a survey that includes highly relevant questions according to the occupation of the hypothetical target group. Furthermore, it can select the optimal survey format based on the interests of the hypothetical target group. In this way, the implementation unit can conduct an appropriate survey by considering the attribute information of the hypothetical target group. The attribute information of the hypothetical target group includes age, gender, occupation, etc. Some or all of the above processing in the implementation unit may be performed using AI or not. For example, the implementation unit can conduct the survey using an AI model that optimizes the implementation method by taking the attribute information of the hypothetical target group as input.

[0105] The analysis department can predict current survey results by referring to past survey data during analysis. For example, it can predict current survey results based on past survey data. It can also predict current survey results by analyzing trends from past survey data. Furthermore, it can predict trends in current survey results by referring to past survey data. In this way, the analysis department can predict current survey results by referring to past survey data. Past survey data includes past responses, survey types, and survey frequencies. Some or all of the above processing in the analysis department may be performed using AI or not. For example, the analysis department can use past survey data as input and predict current survey results using an AI model that predicts current survey results.

[0106] The analysis unit can estimate the user's emotions and adjust the importance of the analysis results based on the estimated emotions. For example, if the user is stressed, important analysis results can be displayed preferentially. If the user is relaxed, detailed analysis results can be displayed preferentially. Furthermore, if the user is in a hurry, concise analysis results can be displayed preferentially. In this way, the analysis unit can prioritize the display of important information by adjusting the importance of the analysis results according to the user's emotions. The adjustment of the importance of the analysis results is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can take user emotion data as input and display the analysis results using an AI model that adjusts the importance of the analysis results.

[0107] The implementation unit can customize the survey method by referring to the respondents' past response history when conducting the survey. For example, it can conduct a survey that includes highly relevant questions based on the content of surveys the respondents have answered in the past. It can also conduct a survey that includes questions that are likely to interest the respondents based on their past response history. Furthermore, it can analyze the respondents' past response history and select the most suitable survey format. In this way, the implementation unit can conduct an appropriate survey by referring to the respondents' past response history. Past response history includes past response content, response trends, and response frequency. Some or all of the above processing in the implementation unit may be performed using AI or not. For example, the implementation unit can conduct a survey using an AI model that takes the respondents' past response history as input and customizes the implementation method.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The reception desk accepts input of survey details. For example, it provides an interface for users to input rough survey details. Step 2: The generation unit creates a questionnaire based on the information received by the reception unit. For example, it automatically generates questions according to the survey content and creates the questionnaire. The generation unit can generate questions based on the survey content using text generation AI. Step 3: The implementation unit conducts the questionnaire created by the generation unit. For example, it conducts the questionnaire on a virtual target group and collects responses. The implementation unit can provide a simulation environment for conducting the virtual questionnaire. Step 4: The analysis department analyzes the survey results collected by the implementation department. For example, it analyzes the collected response data to determine whether there is a need and the degree of effectiveness. The analysis department can use data mining techniques to analyze the response data.

[0110] 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.

[0111] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements described above, including the reception unit, generation unit, implementation unit, and analysis unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and provides an interface for the user to input rough survey content. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically generates question items according to the survey content and creates a questionnaire. The implementation unit is implemented by, for example, the control unit 46A of the smart device 14 and conducts the questionnaire on a virtual target and collects responses. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected response data to determine whether there is a need and the degree of effectiveness. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0117] 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.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0119] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] 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.

[0121] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] 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.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the reception unit, generation unit, implementation unit, and analysis unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and provides an interface for the user to input rough survey content. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically generates question items according to the survey content and creates a questionnaire. The implementation unit is implemented by, for example, the control unit 46A of the smart glasses 214 and conducts the questionnaire on a virtual target person and collects responses. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected response data to determine whether there is a need and the degree of effectiveness. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0133] 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.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0135] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] 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.

[0137] 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.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] 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.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the reception unit, generation unit, implementation unit, and analysis unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and provides an interface for the user to input rough survey content. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically generates question items according to the survey content and creates a questionnaire. The implementation unit is implemented by, for example, the control unit 46A of the headset terminal 314 and conducts the questionnaire on a virtual target person and collects responses. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected response data to determine whether there is a need and the degree of effectiveness. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0149] 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.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0151] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] 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.

[0153] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] 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.

[0155] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] 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.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the reception unit, generation unit, implementation unit, and analysis unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and provides an interface for the user to input rough survey content. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically generates question items according to the survey content and creates a questionnaire. The implementation unit is implemented by, for example, the control unit 46A of the robot 414 and conducts the questionnaire on a virtual target person and collects responses. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected response data to determine whether there is a need and the degree of effectiveness. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0163] 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.

[0164] Figure 9 shows the 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.

[0165] 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.

[0166] 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.

[0167] 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, and motorcycles, 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 based, for example, 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.

[0168] 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."

[0169] 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.

[0170] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] 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 other things 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.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) The reception desk accepts input of survey details, A generation unit that creates a questionnaire based on the information received by the reception unit, An implementation unit that conducts the questionnaire created by the generation unit, The system includes an analysis unit that analyzes the questionnaire results collected by the implementation unit. A system characterized by the following features. (Note 2) The generating unit is Automatically generates survey questions based on the survey content. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned implementation unit is Conduct a survey on a hypothetical target group and collect their responses. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit is The collected response data is analyzed to determine whether there is a need and the degree of effectiveness. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is Provide specific analysis results The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface for the survey based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We analyze the user's past research history and suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering survey data, the input is filtered based on the user's current work situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When users input survey data, the system prioritizes accepting input that is highly relevant, taking into account their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users input survey data, the system analyzes their social media activity and suggests relevant input content. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is We estimate the user's emotions and adjust the wording of the survey based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating the questionnaire, adjust the level of detail of the questions based on the importance of the survey content. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating questionnaires, different generation algorithms are applied depending on the category of the survey content. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is The system estimates the user's emotions and adjusts the length of the survey based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating the questionnaire, the priority of the questions is determined based on the submission date of the survey responses. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating the questionnaire, the order of the questions is adjusted based on the relevance of the survey content. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned implementation unit is We estimate the user's emotions and adjust the survey implementation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned implementation unit is When conducting a survey, optimize the implementation method by considering the attribute information of the hypothetical target audience. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned implementation unit is When conducting a survey, customize the implementation method by referring to the past response history of the target participants. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned implementation unit is The system estimates user sentiment and adjusts the order in which surveys are administered based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned implementation unit is When conducting a survey, the method of implementation should be selected considering the geographical distribution of the target audience. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned implementation unit is When conducting a survey, we analyze the social media activity of the target audience and propose implementation methods. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit is During analysis, past survey data is used to predict current survey results. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit is During the analysis, different analytical methods are applied to each category of survey content. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit is It estimates the user's emotions and adjusts the importance of the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit is During the analysis, we analyze how the analysis results change based on when the survey responses were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned analysis unit is During the analysis, we provide analysis results by referring to relevant market data related to the survey content. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk accepts input of survey details, A generation unit that creates a questionnaire based on the information received by the reception unit, An implementation unit that conducts the questionnaire created by the generation unit, The system includes an analysis unit that analyzes the questionnaire results collected by the implementation unit. A system characterized by the following features.

2. The generating unit is Automatically generates survey questions based on the survey content. The system according to feature 1.

3. The aforementioned implementation unit is Conduct a survey on a hypothetical target group and collect their responses. The system according to feature 1.

4. The aforementioned analysis unit is The collected response data is analyzed to determine whether there is a need and the degree of effectiveness. The system according to feature 1.

5. The aforementioned analysis unit is Provide specific analysis results The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface for the survey based on the estimated user emotions. The system according to feature 1.

7. The aforementioned reception unit is We analyze the user's past research history and suggest the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is When entering survey data, the input is filtered based on the user's current work situation and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is When users input survey data, the system prioritizes accepting input that is highly relevant, taking into account their geographical location. The system according to feature 1.

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