System

Abstract

An object of the system according to the embodiment is to dynamically generate the next question on the basis of the emotion of the answerer and the content of the answer, and to elicit the true sound and potential needs of the answerer.SOLUTION: A system includes a question generation part and a feeling analysis part. The question generation unit analyzes the answer content and emotion of the answerer, and generates the next question based on the analysis. The emotion analysis unit presents the question generated by the question generation unit to the answerer.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology was unable to dynamically generate the next question based on the respondent's emotions and answers, making it difficult to fully understand the respondent's true feelings and latent needs.

[0005] The system according to the embodiment aims to dynamically generate the next question based on the respondent's emotions and the content of their answer, and to draw out the respondent's true feelings and latent needs. [Means for solving the problem]

[0006] The system according to the embodiment includes a question generator and a sentiment analyzer. The question generator analyzes the content and sentiment of the respondent's answer and generates the next question based on the analysis. The sentiment analyzer presents the question generated by the question generator to the respondent. [Effects of the Invention]

[0007] The system according to the embodiment dynamically generates the next question based on the respondent's emotions and the content of the response, and can draw out the respondent's true feelings and latent needs. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A questionnaire system according to an embodiment of the present invention is a system that uses generative AI to streamline the questionnaire response process and uncover the true feelings and latent needs of respondents. This reduces the burden on respondents and enables the efficient collection of information that is useful to companies.

[0029] A questionnaire system according to an embodiment includes a question generation unit and a sentiment analysis unit. The question generation unit analyzes the content and sentiment of a respondent's answer and generates a subsequent question based on the analysis. For example, if a respondent answers, "I'm not satisfied with a recent service," the generation AI generates a follow-up question such as, "What specifically were you dissatisfied with?" The question generation unit generates appropriate questions based on prompts containing the respondent's answer and sentiment. For example, if a respondent answers, "This product is so-so," the generation AI reads the respondent's sentiment from the tone and speed of their voice and determines the level of satisfaction indicated by the rating "so-so." The sentiment analysis unit analyzes not only the content of the respondent's answer but also their sentiment using speech recognition technology. For example, the generation AI analyzes the respondent's tone and speed of their voice to determine their sentiment. The sentiment analysis unit generates individually customized questions based on the respondent's past answer history and profile information. For example, if a respondent has previously shown interest in a particular product, the generation AI may ask more questions related to that product. This allows respondents to be provided with questions tailored to their interests and concerns, improving the quality of their answers. As a result, the questionnaire system according to the embodiment reduces the burden on respondents and enables companies to efficiently collect information that is useful to them. For example, companies no longer need to think about questionnaire questions in advance, and the generation AI automatically generates questions, significantly reducing the cost and time required to create questionnaires. This allows companies to more efficiently understand customer needs.

[0030] The question generation unit can analyze the respondent's past behavioral history and purchase history and generate questions based on that. In the question generation unit, for example, the generation AI analyzes the respondent's past purchase history and generates detailed questions about specific products. For example, it asks about satisfaction with and areas for improvement for products purchased in the past. In addition, the question generation unit generates related questions based on the respondent's behavioral history. For example, it can analyze website browsing history and ask questions about products and services in which the respondent is interested. In addition, the question generation unit generates questions about products that the respondent may purchase next based on the respondent's purchase history. For example, it can ask questions about new products related to products purchased in the past. This makes it possible to generate more personalized questions by generating questions based on the respondent's past behavioral history and purchase history.

[0031] The question generation unit can analyze the content of respondents' social media posts and generate questions based on their interests and concerns. In the question generation unit, for example, the generation AI analyzes the content of respondents' social media posts and generates questions related to specific hobbies and concerns. For example, it poses questions about hobbies that are frequently posted about. In addition, the question generation unit identifies respondents' interests and concerns based on the content of their social media posts and generates questions based on them. For example, it can pose questions related to specific events or topics. In addition, the question generation unit analyzes the content of social media posts and generates questions that will attract the respondents' attention. For example, it can pose questions related to recent posts. In this way, by generating questions based on the content of social media posts, it is possible to ask questions that match the respondents' interests and concerns.

[0032] The question generation unit can generate questions in different languages ​​to realize a multilingual survey. For example, the question generation unit allows the generation AI to generate questions in multiple languages, such as English, Japanese, and French, to realize an international survey. For example, the same question can be asked in different languages. The question generation unit can also generate questions in different languages ​​to realize a multilingual survey. For example, questions can be generated according to the language settings of the respondent. The question generation unit can also generate questions in different languages, and provide the survey in the language selected by the respondent. For example, questions can be asked in English to a respondent who selected English. This makes it possible to generate questions in different languages ​​to realize a multilingual survey.

[0033] The question generation unit generates questions using images and videos to visually attract respondents. For example, the question generation unit uses a generation AI to generate questions using images to visually attract respondents. For example, an image of a product may be shown to ask about impressions of the product. The question generation unit also uses a generation AI to generate questions using videos to visually attract respondents. For example, a promotional video for the product may be shown to ask about impressions of the product. The question generation unit also uses a generation AI to generate questions using images and videos to attract respondents' interest. For example, questions can be posed using visually appealing content. In this way, the use of images and videos can visually attract respondents.

[0034] The data integration unit integrates the sentiment analysis results with purchase history and website browsing history to gain deeper insights. For example, the generation AI in the data integration unit integrates the sentiment analysis results of respondents with their purchase history to analyze the emotions behind their purchasing behavior. For example, it analyzes the emotions they felt when purchasing a specific product. The generation AI also integrates the sentiment analysis results with their website browsing history to gain a detailed understanding of the respondents' interests and concerns. For example, it can analyze the emotions they felt when viewing a specific page. The generation AI also integrates the sentiment analysis results with other data to gain deeper insights. For example, it can integrate them with social media data to understand the respondents' overall emotional trends. This allows for deeper insights to be gained by integrating the sentiment analysis results with other data.

[0035] The question generation unit can analyze the lifestyle and hobbies and preferences of the respondent and personalize questions based on that. In the question generation unit, for example, the generation AI analyzes the lifestyle data of the respondent and generates individually customized questions. For example, for a respondent who likes the outdoors, it will ask questions related to the outdoors. In addition, the question generation unit generates interesting questions based on the respondent's hobbies and preferences. For example, for a respondent who likes music, it can ask questions related to music. In addition, the question generation unit analyzes the lifestyle and hobbies and preferences of the respondent and personalizes questions based on that. For example, for a respondent who likes sports, it can ask questions related to sports. In this way, by personalizing questions based on lifestyle and hobbies and preferences, the quality of answers can be improved.

[0036] The question generation unit can generate region-specific questions by taking into account the geographical information of the respondent. In the question generation unit, for example, the generation AI generates region-specific questions based on the geographical information of the respondent. For example, it asks questions about events or culture in a particular region. In addition, the question generation unit generates region-related questions by taking into account the geographical information. For example, it can ask questions about local specialties or tourist spots. In addition, the question generation unit generates region-specific questions by taking into account the geographical information of the respondent. For example, it can ask questions about the climate and culture of the region. In this way, region-specific questions can be generated by taking into account the geographical information.

[0037] The question generation unit generates questions that are specific to the respondent's occupation or industry, allowing for specialized feedback. In the question generation unit, for example, the generation AI generates occupation-specific questions based on the respondent's occupation information. For example, medical-related questions are asked to medical professionals. In addition, the question generation unit generates industry-specific questions based on the respondent's industry information. For example, questions about the latest technological trends can be asked to respondents in the IT industry. In addition, the question generation unit generates questions that are specific to the respondent's occupation or industry, allowing for specialized feedback. For example, questions about the manufacturing process can be asked to respondents in the manufacturing industry. In this way, specialized questions can be generated to allow for specialized feedback.

[0038] The question generation unit can generate individually customized questions by taking into account the respondent's family structure and living environment. In the question generation unit, for example, the generation AI generates questions related to the family based on the respondent's family structure. For example, questions about child-rearing are asked to households with children. In addition, the question generation unit generates questions related to lifestyle based on the respondent's living environment. For example, questions about urban life can be asked to respondents who live in urban areas. In addition, the question generation unit generates individually customized questions by taking into account the respondent's family structure and living environment. For example, questions about pets can be asked to respondents who have pets. In this way, individually customized questions can be generated by taking into account the family structure and living environment.

[0039] The aggregation unit can automatically tally survey results and generate reports in real time. For example, the generation AI tally survey response data in real time and instantly generate reports. For example, the aggregation results are updated each time a response is completed. The aggregation unit also has the generation AI automatically tally survey results and generate reports in real time. For example, the aggregation results can be displayed as graphs or charts. The aggregation unit also has the generation AI generate detailed reports based on the survey results. For example, the aggregation results can be displayed separately for each respondent's attributes. This allows the company to save costs and time by automatically aggregating survey results and generating reports in real time.

[0040] The analysis unit can automatically analyze survey results and extract important insights. In the analysis unit, for example, the generation AI analyzes survey response data and automatically extracts important insights. For example, it can identify customer satisfaction and dissatisfaction. The analysis unit also analyzes customer behavior patterns based on the survey results. For example, it can analyze response trends to specific questions. The analysis unit also analyzes survey results and extracts important insights. For example, it can propose marketing strategies based on customer purchasing trends and sentiment analysis results. In this way, automatically analyzing survey results and extracting important insights can save companies time and costs.

[0041] The data integration unit can integrate the survey results with other marketing data to perform a comprehensive analysis. For example, the generation AI can integrate the survey results with other marketing data to perform a comprehensive analysis. For example, it can integrate them with purchase data and website browsing data. The data integration unit can also integrate the survey results with marketing data to perform a detailed analysis of customer behavior patterns. For example, it can analyze based on ad click rates and purchase history. The data integration unit can also integrate the survey results with other data to perform a comprehensive analysis. For example, it can integrate them with social media data to understand overall customer behavioral trends. This allows the survey results to be integrated with other marketing data to perform a comprehensive analysis.

[0042] The question generation unit can automatically generate questions for the next survey based on the survey results. For example, the generation AI automatically generates questions for the next survey based on the results of the previous survey. For example, it can ask follow-up questions about complaints from the previous survey. The question generation unit also generates questions for the next survey based on the survey results. For example, it can ask questions based on the interests and concerns of the respondents. The question generation unit also generates questions for the next survey based on the survey results. For example, it can ask detailed questions about a specific topic. This allows the company to save costs and time by automatically generating the next questions based on the survey results.

[0043] The profile generation unit can generate detailed profiles for each customer segment based on the survey results. For example, the generation AI in the profile generation unit generates detailed profiles for each customer segment based on the survey results. For example, profiles are created based on age, gender, and interests. The profile generation unit also analyzes customer behavior patterns based on the survey results and generates detailed profiles. For example, profiles can be created based on purchase history and website browsing history. The profile generation unit also generates detailed profiles for each customer segment based on the survey results. For example, profiles can be created to optimize marketing strategies for specific customer demographics. This makes it possible to optimize targeting strategies by generating detailed profiles for each customer segment based on the survey results.

[0044] The comparative analysis unit can perform a comparative analysis with competitors based on the survey results. For example, the generation AI performs a comparative analysis with competitors based on the survey results. For example, it compares customer satisfaction and points of dissatisfaction with competitors. The comparative analysis unit also compares performance with competitors based on the survey results. For example, it can compare evaluations of product quality and price. The comparative analysis unit also performs a comparative analysis with competitors based on the survey results. For example, it can compare customer purchasing trends and brand awareness. This makes it possible to optimize differentiation strategies by performing a comparative analysis with competitors based on the survey results.

[0045] The concept generation unit can automatically generate a concept for a new product based on the results of the survey. In the concept generation unit, for example, a generation AI automatically generates a concept for a new product based on the results of the survey. For example, it proposes a product concept that reflects customer needs and dissatisfaction points. In addition, the concept generation unit defines the characteristics of a new product based on the results of the survey. For example, it can generate concepts related to specific functions and designs. In addition, the concept generation unit automatically generates a concept for a new product based on the results of the survey. For example, it can propose a concept that reflects the target market and points of differentiation from competitors. In this way, the efficiency of product development can be improved by automatically generating a concept for a new product based on the results of the survey.

[0046] The effect prediction unit can predict the effect of an advertising campaign based on the survey results. For example, the generation AI predicts the effect of an advertising campaign based on the survey results. For example, it predicts how much of a response a particular message or visual will provoke. The effect prediction unit also simulates the effect of an advertising campaign based on the survey results. For example, it can compare the effectiveness of different advertising strategies. The effect prediction unit also predicts the effect of an advertising campaign based on the survey results. For example, it can evaluate the effectiveness of advertising for a specific target demographic. This makes it possible to improve the accuracy of marketing strategies by predicting the effect of an advertising campaign based on the survey results.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The questionnaire system can further include a health data acquisition unit that monitors the respondent's health condition. For example, data such as heart rate and blood pressure can be acquired from a wearable device, and questions can be adjusted based on the respondent's health condition. This makes it possible to prioritize questions that will help the respondent relax if the respondent is feeling stressed. The health data acquisition unit can also monitor the respondent's health condition in real time and provide feedback according to the health condition. For example, if the respondent's heart rate is high, advice on how to relax can be provided. This makes it possible to conduct a questionnaire that takes the respondent's health condition into consideration.

[0049] The survey system can further include a hobby analysis unit that analyzes the hobbies and interests of respondents. For example, it can identify hobbies and interests based on data on events the respondent has previously attended or products they have purchased, and generate questions based on those. This makes it possible to provide questions that interest the respondent and improve the quality of their answers. The hobby analysis unit can also suggest new events or products related to the respondent's hobbies. For example, it can provide information on new music events to a respondent who likes music. This makes it possible to provide personalized questions based on the respondent's interests.

[0050] The survey system may further include a geographic information acquisition unit that acquires the respondent's geographic information. For example, region-specific questions can be generated based on the respondent's place of residence or current location. This allows for providing questions related to local culture and events to attract the respondent's interest. The geographic information acquisition unit may also provide feedback related to the region based on the respondent's geographic information. For example, it may provide information about local specialties and tourist spots. This enables personalized questions based on geographic information.

[0051] The questionnaire system may further include an occupational information acquisition unit that acquires occupational information of the respondent. For example, specialized questions can be generated based on the respondent's occupation and industry. This allows for questions that utilize the respondent's specialized knowledge and improves the quality of the answers. The occupational information acquisition unit may also provide new information and trends related to the respondent's occupation. For example, a respondent in the IT industry may be provided with information on the latest technological trends. This enables personalized questions based on the occupational information.

[0052] The questionnaire system may further include a family information acquisition unit that acquires the respondent's family structure. For example, family-related questions can be generated based on the respondent's family structure. This allows for questions that take into consideration the respondent's living environment, improving the quality of the answers. The family information acquisition unit may also provide new information or advice related to the respondent's family structure. For example, advice on child-rearing can be provided to a respondent who is raising children. This allows for personalized questions based on family structure.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The question generator analyzes the respondent's response and emotions, and generates the next question based on those. For example, if a respondent answers, "I'm not satisfied with a recent service," the generation AI generates a follow-up question such as, "What specifically were you dissatisfied with?" The generation AI also generates appropriate questions based on prompts that include the respondent's response and emotions. For example, if a respondent answers, "This product is so-so," the generation AI reads the respondent's emotions from the tone and speed of their voice and determines the level of satisfaction that the rating "so-so" indicates. Step 2: In the sentiment analysis section, the generation AI not only analyzes the content of the respondent's answers, but also their emotions using voice recognition technology. For example, the generation AI analyzes the tone and speed of the respondent's voice to determine their emotions. The sentiment analysis section also allows the generation AI to generate individually customized questions based on the respondent's past answer history and profile information. For example, if a respondent has previously shown interest in a particular product, the generation AI will ask them more questions related to that product.

[0055] (Example 2) A questionnaire system according to an embodiment of the present invention is a system that uses generative AI to streamline the questionnaire response process and uncover the true feelings and latent needs of respondents. This reduces the burden on respondents and enables the efficient collection of information that is useful to companies.

[0056] A questionnaire system according to an embodiment includes a question generation unit and a sentiment analysis unit. The question generation unit analyzes the content and sentiment of a respondent's answer and generates a subsequent question based on the analysis. For example, if a respondent answers, "I'm not satisfied with a recent service," the generation AI generates a follow-up question such as, "What specifically were you dissatisfied with?" The question generation unit generates appropriate questions based on prompts containing the respondent's answer and sentiment. For example, if a respondent answers, "This product is so-so," the generation AI reads the respondent's sentiment from the tone and speed of their voice and determines the level of satisfaction indicated by the rating "so-so." The sentiment analysis unit analyzes not only the content of the respondent's answer but also their sentiment using speech recognition technology. For example, the generation AI analyzes the respondent's tone and speed of their voice to determine their sentiment. The sentiment analysis unit generates individually customized questions based on the respondent's past answer history and profile information. For example, if a respondent has previously shown interest in a particular product, the generation AI may ask more questions related to that product. This allows respondents to be provided with questions tailored to their interests and concerns, improving the quality of their answers. As a result, the questionnaire system according to the embodiment reduces the burden on respondents and enables companies to efficiently collect information that is useful to them. For example, companies no longer need to think about questionnaire questions in advance, and the generation AI automatically generates questions, significantly reducing the cost and time required to create questionnaires. This allows companies to more efficiently understand customer needs.

[0057] The question generation unit can analyze the respondent's past behavioral history and purchase history and generate questions based on that. In the question generation unit, for example, the generation AI analyzes the respondent's past purchase history and generates detailed questions about specific products. For example, it asks about satisfaction with and areas for improvement for products purchased in the past. In addition, the question generation unit generates related questions based on the respondent's behavioral history. For example, it can analyze website browsing history and ask questions about products and services in which the respondent is interested. In addition, the question generation unit generates questions about products that the respondent may purchase next based on the respondent's purchase history. For example, it can ask questions about new products related to products purchased in the past. This makes it possible to generate more personalized questions by generating questions based on the respondent's past behavioral history and purchase history.

[0058] The question generation unit can analyze the content of respondents' social media posts and generate questions based on their interests and concerns. In the question generation unit, for example, the generation AI analyzes the content of respondents' social media posts and generates questions related to specific hobbies and concerns. For example, it poses questions about hobbies that are frequently posted about. In addition, the question generation unit identifies respondents' interests and concerns based on the content of their social media posts and generates questions based on them. For example, it can pose questions related to specific events or topics. In addition, the question generation unit analyzes the content of social media posts and generates questions that will attract the respondents' attention. For example, it can pose questions related to recent posts. In this way, by generating questions based on the content of social media posts, it is possible to ask questions that match the respondents' interests and concerns.

[0059] The question generation unit uses the emotion estimation function to generate questions according to the respondent's emotional state and prioritize questions that elicit positive emotions. For example, the question generation unit uses the emotion estimation function to generate positive questions when the respondent is relaxed. For example, questions about enjoyable experiences or successful experiences are posed. The question generation unit also uses the emotion estimation function to analyze the respondent's emotional state and prioritize questions that elicit positive emotions. For example, when the respondent is feeling stressed, questions that will help the respondent to relax can be posed. The question generation unit also uses the emotion estimation function to generate questions according to the respondent's emotional state. For example, when the respondent is feeling positive emotions, even more positive questions can be posed. In this way, by using the emotion estimation function, questions according to the respondent's emotional state can be generated and positive emotions can be elicited.

[0060] The question generation unit can generate questions in different languages ​​to realize a multilingual survey. For example, the question generation unit allows the generation AI to generate questions in multiple languages, such as English, Japanese, and French, to realize an international survey. For example, the same question can be asked in different languages. The question generation unit can also generate questions in different languages ​​to realize a multilingual survey. For example, questions can be generated according to the language settings of the respondent. The question generation unit can also generate questions in different languages, and provide the survey in the language selected by the respondent. For example, questions can be asked in English to a respondent who selected English. This makes it possible to generate questions in different languages ​​to realize a multilingual survey.

[0061] The question generation unit generates questions using images and videos to visually attract respondents. For example, the question generation unit uses a generation AI to generate questions using images to visually attract respondents. For example, an image of a product may be shown to ask about impressions of the product. The question generation unit also uses a generation AI to generate questions using videos to visually attract respondents. For example, a promotional video for the product may be shown to ask about impressions of the product. The question generation unit also uses a generation AI to generate questions using images and videos to attract respondents' interest. For example, questions can be posed using visually appealing content. In this way, the use of images and videos can visually attract respondents.

[0062] The question generation unit can use the emotion estimation function to optimize the order of questions based on the respondent's emotions and improve the quality of answers. For example, the question generation unit uses the emotion estimation function to ask important questions first when the respondent is relaxed. For example, detailed questions are asked when the respondent is emotionally stable. The question generation unit also uses the emotion estimation function to analyze the respondent's emotional state and optimize the order of questions. For example, simple questions can be asked first when the respondent is feeling stressed. The question generation unit also uses the emotion estimation function to optimize the order of questions based on the respondent's emotions. For example, important questions can be asked first when the respondent is feeling positive. In this way, by using the emotion estimation function, the order of questions can be optimized based on the respondent's emotions and the quality of answers can be improved.

[0063] The emotion analysis unit can use facial expression recognition technology to analyze emotions from the respondent's facial expression when answering a question. In the emotion analysis unit, for example, the generation AI uses a camera to analyze the respondent's facial expression in real time to analyze emotions. For example, it analyzes facial expressions such as smiles and wrinkles between the eyebrows. In addition, the emotion analysis unit uses facial expression recognition technology to analyze the respondent's emotions. For example, it can determine emotions based on changes in the respondent's facial expression. In addition, the generation AI uses facial expression recognition technology to analyze the respondent's emotions in detail. For example, it can analyze emotions by capturing subtle changes in facial expression. As a result, by using facial expression recognition technology, it is possible to analyze emotions from the facial expression when answering a question.

[0064] The emotion analysis unit analyzes biometric information to more accurately grasp the respondent's emotional state. For example, the generation AI uses a heart rate sensor to monitor the respondent's heart rate in real time and analyze their emotional state. For example, it can determine stress levels based on heart rate fluctuations. The emotion analysis unit also analyzes the respondent's emotional state using a galvanic skin response sensor. For example, it can determine emotions based on skin galvanic response. The emotion analysis unit also analyzes biometric information to more accurately grasp the respondent's emotional state. For example, it can analyze emotions by combining heart rate and galvanic skin response data. This makes it possible to more accurately grasp emotional states by analyzing biometric information.

[0065] The emotion analysis unit can use the emotion estimation function to monitor changes in the respondent's emotions in real time and generate follow-up questions at appropriate timing. The emotion analysis unit can, for example, use the emotion estimation function to monitor changes in the respondent's emotions in real time and generate follow-up questions when the emotions have stabilized. For example, detailed questions can be asked when the emotions have calmed down. The emotion analysis unit can also use the emotion estimation function to analyze changes in the respondent's emotions and generate follow-up questions at appropriate timing. For example, more detailed questions can be asked when the respondent has positive emotions. The emotion analysis unit can also use the emotion estimation function to monitor changes in the respondent's emotions in real time and generate follow-up questions. For example, appropriate questions can be asked when the emotions have changed. As a result, by using the emotion estimation function, changes in emotions can be monitored in real time and follow-up questions can be generated at appropriate timing.

[0066] The feedback generation unit can provide personalized feedback after the survey is completed based on the emotion analysis results. For example, the feedback generation unit provides individual feedback after the survey is completed based on the emotion analysis results of the respondent by the generation AI. For example, if there are many positive emotions, an encouraging message can be sent. The feedback generation unit also provides feedback according to the respondent's emotions based on the emotion analysis results by the generation AI. For example, if there are many negative emotions, a message pointing out areas for improvement can be sent. The feedback generation unit also provides feedback tailored to the respondent's emotions based on the emotion analysis results by the generation AI. For example, if the emotions are stable, detailed feedback can be provided. This makes it possible to provide personalized feedback based on the emotion analysis results, thereby responding to the respondent individually.

[0067] The data integration unit integrates the sentiment analysis results with purchase history and website browsing history to gain deeper insights. For example, the generation AI in the data integration unit integrates the sentiment analysis results of respondents with their purchase history to analyze the emotions behind their purchasing behavior. For example, it analyzes the emotions they felt when purchasing a specific product. The generation AI also integrates the sentiment analysis results with their website browsing history to gain a detailed understanding of the respondents' interests and concerns. For example, it can analyze the emotions they felt when viewing a specific page. The generation AI also integrates the sentiment analysis results with other data to gain deeper insights. For example, it can integrate them with social media data to understand the respondents' overall emotional trends. This allows for deeper insights to be gained by integrating the sentiment analysis results with other data.

[0068] The incentive providing unit uses the emotion estimation function to provide incentives based on the respondent's emotions, thereby increasing their motivation to answer. For example, the incentive providing unit uses the emotion estimation function to provide coupons when positive emotions increase. For example, a coupon is sent when the respondent smiles more frequently. The incentive providing unit also uses the emotion estimation function to provide incentives based on the respondent's emotions. For example, points can be provided when the respondent is relaxed. The incentive providing unit also uses the emotion estimation function to provide incentives according to the respondent's emotions. For example, a special gift can be provided when there are many positive emotions. In this way, by using the emotion estimation function, incentives based on the respondent's emotions can be provided, increasing their motivation to answer.

[0069] The question generation unit can analyze the lifestyle and hobbies and preferences of the respondent and personalize questions based on that. In the question generation unit, for example, the generation AI analyzes the lifestyle data of the respondent and generates individually customized questions. For example, for a respondent who likes the outdoors, it will ask questions related to the outdoors. In addition, the question generation unit generates interesting questions based on the respondent's hobbies and preferences. For example, for a respondent who likes music, it can ask questions related to music. In addition, the question generation unit analyzes the lifestyle and hobbies and preferences of the respondent and personalizes questions based on that. For example, for a respondent who likes sports, it can ask questions related to sports. In this way, by personalizing questions based on lifestyle and hobbies and preferences, the quality of answers can be improved.

[0070] The question generation unit can generate region-specific questions by taking into account the geographical information of the respondent. In the question generation unit, for example, the generation AI generates region-specific questions based on the geographical information of the respondent. For example, it asks questions about events or culture in a particular region. In addition, the question generation unit generates region-related questions by taking into account the geographical information. For example, it can ask questions about local specialties or tourist spots. In addition, the question generation unit generates region-specific questions by taking into account the geographical information of the respondent. For example, it can ask questions about the climate and culture of the region. In this way, region-specific questions can be generated by taking into account the geographical information.

[0071] The question generation unit uses the emotion estimation function to generate personalized questions according to the respondent's emotional state, thereby improving the quality of the answers. The question generation unit, for example, uses the emotion estimation function to generate personalized questions when the respondent is relaxed. For example, questions that are easy to answer in a relaxed state are posed. The question generation unit also uses the emotion estimation function to analyze the respondent's emotional state and generate personalized questions. For example, simple questions can be posed when the respondent is feeling stressed. The question generation unit also uses the emotion estimation function to generate questions according to the respondent's emotional state. For example, detailed questions can be posed when the respondent has positive emotions. In this way, by using the emotion estimation function, personalized questions can be generated according to the respondent's emotional state, thereby improving the quality of the answers.

[0072] The question generation unit generates questions that are specific to the respondent's occupation or industry, allowing for specialized feedback. In the question generation unit, for example, the generation AI generates occupation-specific questions based on the respondent's occupation information. For example, medical-related questions are asked to medical professionals. In addition, the question generation unit generates industry-specific questions based on the respondent's industry information. For example, questions about the latest technological trends can be asked to respondents in the IT industry. In addition, the question generation unit generates questions that are specific to the respondent's occupation or industry, allowing for specialized feedback. For example, questions about the manufacturing process can be asked to respondents in the manufacturing industry. In this way, specialized questions can be generated to allow for specialized feedback.

[0073] The question generation unit can generate individually customized questions by taking into account the respondent's family structure and living environment. In the question generation unit, for example, the generation AI generates questions related to the family based on the respondent's family structure. For example, questions about child-rearing are asked to households with children. In addition, the question generation unit generates questions related to lifestyle based on the respondent's living environment. For example, questions about urban life can be asked to respondents who live in urban areas. In addition, the question generation unit generates individually customized questions by taking into account the respondent's family structure and living environment. For example, questions about pets can be asked to respondents who have pets. In this way, individually customized questions can be generated by taking into account the family structure and living environment.

[0074] The question generation unit uses the emotion estimation function to adjust the difficulty of questions based on the respondent's emotions, thereby reducing the burden of answering. For example, the question generation unit uses the emotion estimation function to ask easy questions when the respondent is feeling stressed. For example, it continues to ask easy questions until the respondent's emotions stabilize. The question generation unit also uses the emotion estimation function to analyze the respondent's emotional state and adjust the difficulty of questions. For example, it can ask more detailed questions when the respondent is relaxed. The question generation unit also uses the emotion estimation function to adjust the difficulty of questions based on the respondent's emotions. For example, it can ask more difficult questions when the respondent is feeling positive. In this way, by using the emotion estimation function, it is possible to adjust the difficulty of questions based on the respondent's emotions and reduce the burden of answering.

[0075] The aggregation unit can automatically tally survey results and generate reports in real time. For example, the generation AI tally survey response data in real time and instantly generate reports. For example, the aggregation results are updated each time a response is completed. The aggregation unit also has the generation AI automatically tally survey results and generate reports in real time. For example, the aggregation results can be displayed as graphs or charts. The aggregation unit also has the generation AI generate detailed reports based on the survey results. For example, the aggregation results can be displayed separately for each respondent's attributes. This allows the company to save costs and time by automatically aggregating survey results and generating reports in real time.

[0076] The analysis unit can automatically analyze survey results and extract important insights. In the analysis unit, for example, the generation AI analyzes survey response data and automatically extracts important insights. For example, it can identify customer satisfaction and dissatisfaction. The analysis unit also analyzes customer behavior patterns based on the survey results. For example, it can analyze response trends to specific questions. The analysis unit also analyzes survey results and extracts important insights. For example, it can propose marketing strategies based on customer purchasing trends and sentiment analysis results. In this way, automatically analyzing survey results and extracting important insights can save companies time and costs.

[0077] The improvement suggestion unit can use the emotion estimation function to automatically suggest improvements to the questionnaire based on the emotions of the respondent. For example, the improvement suggestion unit uses the emotion estimation function to analyze the emotion data of the respondent and automatically suggest improvements to the questionnaire. For example, questions that evoke a lot of negative emotions can be improved. The improvement suggestion unit also uses the emotion estimation function to suggest improvements to the questionnaire based on the emotions of the respondent. For example, questions that cause the respondent to feel stressed can be improved. The improvement suggestion unit also uses the emotion estimation function to suggest improvements to the questionnaire based on the emotion data of the respondent. For example, questions that elicit positive emotions can be increased. In this way, by using the emotion estimation function, it is possible to automatically suggest improvements to the questionnaire based on the emotions of the respondent.

[0078] The data integration unit can integrate the survey results with other marketing data to perform a comprehensive analysis. For example, the generation AI can integrate the survey results with other marketing data to perform a comprehensive analysis. For example, it can integrate them with purchase data and website browsing data. The data integration unit can also integrate the survey results with marketing data to perform a detailed analysis of customer behavior patterns. For example, it can analyze based on ad click rates and purchase history. The data integration unit can also integrate the survey results with other data to perform a comprehensive analysis. For example, it can integrate them with social media data to understand overall customer behavioral trends. This allows the survey results to be integrated with other marketing data to perform a comprehensive analysis.

[0079] The question generation unit can automatically generate questions for the next survey based on the survey results. For example, the generation AI automatically generates questions for the next survey based on the results of the previous survey. For example, it can ask follow-up questions about complaints from the previous survey. The question generation unit also generates questions for the next survey based on the survey results. For example, it can ask questions based on the interests and concerns of the respondents. The question generation unit also generates questions for the next survey based on the survey results. For example, it can ask detailed questions about a specific topic. This allows the company to save costs and time by automatically generating the next questions based on the survey results.

[0080] The design optimization unit uses the emotion estimation function to optimize the design of the questionnaire based on the respondent's emotions, thereby improving the response rate. The design optimization unit, for example, uses the emotion estimation function to optimize the design of the questionnaire based on the respondent's emotion data. For example, it adopts a design that elicits positive emotions. The design optimization unit also uses the emotion estimation function to suggest a design based on the respondent's emotions. For example, it can adopt colors and layouts that make the respondent feel relaxed. The design optimization unit also uses the emotion estimation function to optimize the design according to the respondent's emotions. For example, it can use fonts and images that elicit positive emotions. In this way, by using the emotion estimation function, the design of the questionnaire based on the respondent's emotions can be optimized, thereby improving the response rate.

[0081] The profile generation unit can generate detailed profiles for each customer segment based on the survey results. For example, the generation AI in the profile generation unit generates detailed profiles for each customer segment based on the survey results. For example, profiles are created based on age, gender, and interests. The profile generation unit also analyzes customer behavior patterns based on the survey results and generates detailed profiles. For example, profiles can be created based on purchase history and website browsing history. The profile generation unit also generates detailed profiles for each customer segment based on the survey results. For example, profiles can be created to optimize marketing strategies for specific customer demographics. This makes it possible to optimize targeting strategies by generating detailed profiles for each customer segment based on the survey results.

[0082] The comparative analysis unit can perform a comparative analysis with competitors based on the survey results. For example, the generation AI performs a comparative analysis with competitors based on the survey results. For example, it compares customer satisfaction and points of dissatisfaction with competitors. The comparative analysis unit also compares performance with competitors based on the survey results. For example, it can compare evaluations of product quality and price. The comparative analysis unit also performs a comparative analysis with competitors based on the survey results. For example, it can compare customer purchasing trends and brand awareness. This makes it possible to optimize differentiation strategies by performing a comparative analysis with competitors based on the survey results.

[0083] The marketing strategy proposal unit can use the emotion estimation function to propose a marketing strategy based on the customer's emotions. The marketing strategy proposal unit, for example, uses the emotion estimation function to propose a marketing strategy based on the customer's emotion data. For example, it can propose a campaign that elicits positive emotions. The marketing strategy proposal unit also uses the emotion estimation function to formulate a marketing strategy based on the customer's emotions. For example, it can propose an advertising message according to the customer's emotions. The marketing strategy proposal unit also uses the emotion estimation function to propose a marketing strategy based on the customer's emotion data. For example, it can propose a promotion strategy that elicits positive emotions. In this way, by using the emotion estimation function, it is possible to propose a marketing strategy based on the customer's emotions.

[0084] The concept generation unit can automatically generate a concept for a new product based on the results of the survey. In the concept generation unit, for example, a generation AI automatically generates a concept for a new product based on the results of the survey. For example, it proposes a product concept that reflects customer needs and dissatisfaction points. In addition, the concept generation unit defines the characteristics of a new product based on the results of the survey. For example, it can generate concepts related to specific functions and designs. In addition, the concept generation unit automatically generates a concept for a new product based on the results of the survey. For example, it can propose a concept that reflects the target market and points of differentiation from competitors. In this way, the efficiency of product development can be improved by automatically generating a concept for a new product based on the results of the survey.

[0085] The effect prediction unit can predict the effect of an advertising campaign based on the survey results. For example, the generation AI predicts the effect of an advertising campaign based on the survey results. For example, it predicts how much of a response a particular message or visual will provoke. The effect prediction unit also simulates the effect of an advertising campaign based on the survey results. For example, it can compare the effectiveness of different advertising strategies. The effect prediction unit also predicts the effect of an advertising campaign based on the survey results. For example, it can evaluate the effectiveness of advertising for a specific target demographic. This makes it possible to improve the accuracy of marketing strategies by predicting the effect of an advertising campaign based on the survey results.

[0086] The brand strategy formulation unit can use the emotion estimation function to formulate a brand strategy based on customer emotions. The brand strategy formulation unit, for example, uses the emotion estimation function to formulate a brand strategy based on customer emotion data. For example, it can propose a brand message that elicits positive emotions. The brand strategy formulation unit also uses the emotion estimation function to formulate a brand strategy based on customer emotions. For example, it can propose brand positioning according to customer emotions. The brand strategy formulation unit also uses the emotion estimation function to formulate a brand strategy based on customer emotion data. For example, it can propose a visual identity that elicits positive emotions. In this way, by using the emotion estimation function, it is possible to formulate a brand strategy based on customer emotions.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The questionnaire system can further include a health data acquisition unit that monitors the respondent's health condition. For example, data such as heart rate and blood pressure can be acquired from a wearable device, and questions can be adjusted based on the respondent's health condition. This makes it possible to prioritize questions that will help the respondent relax if the respondent is feeling stressed. The health data acquisition unit can also monitor the respondent's health condition in real time and provide feedback according to the health condition. For example, if the respondent's heart rate is high, advice on how to relax can be provided. This makes it possible to conduct a questionnaire that takes the respondent's health condition into consideration.

[0089] The survey system can further include a hobby analysis unit that analyzes the hobbies and interests of respondents. For example, it can identify hobbies and interests based on data on events the respondent has previously attended or products they have purchased, and generate questions based on those. This makes it possible to provide questions that interest the respondent and improve the quality of their answers. The hobby analysis unit can also suggest new events or products related to the respondent's hobbies. For example, it can provide information on new music events to a respondent who likes music. This makes it possible to provide personalized questions based on the respondent's interests.

[0090] The survey system may further include a geographic information acquisition unit that acquires the respondent's geographic information. For example, region-specific questions can be generated based on the respondent's place of residence or current location. This allows for providing questions related to local culture and events to attract the respondent's interest. The geographic information acquisition unit may also provide feedback related to the region based on the respondent's geographic information. For example, it may provide information about local specialties and tourist spots. This enables personalized questions based on geographic information.

[0091] The questionnaire system may further include an occupational information acquisition unit that acquires occupational information of the respondent. For example, specialized questions can be generated based on the respondent's occupation and industry. This allows for questions that utilize the respondent's specialized knowledge and improves the quality of the answers. The occupational information acquisition unit may also provide new information and trends related to the respondent's occupation. For example, a respondent in the IT industry may be provided with information on the latest technological trends. This enables personalized questions based on the occupational information.

[0092] The questionnaire system may further include a family information acquisition unit that acquires the respondent's family structure. For example, family-related questions can be generated based on the respondent's family structure. This allows for questions that take into consideration the respondent's living environment, improving the quality of the answers. The family information acquisition unit may also provide new information or advice related to the respondent's family structure. For example, advice on child-rearing can be provided to a respondent who is raising children. This allows for personalized questions based on family structure.

[0093] The questionnaire system may further include an incentive providing unit that estimates the respondent's emotions and provides incentives based on the estimated emotions. For example, when a respondent has positive emotions, a special coupon or points may be provided. This increases the respondent's motivation and improves the quality of their responses. The incentive providing unit may also adjust the content of the incentive depending on the respondent's emotional state. For example, when a respondent is feeling stressed, an incentive to help them relax may be provided. This makes it possible to provide incentives based on emotions.

[0094] The questionnaire system may further include a question order optimization unit that estimates the respondent's emotions and optimizes the order of questions based on the estimated emotions. For example, when the respondent is relaxed, important questions can be asked first. This allows for providing a question order that corresponds to the respondent's emotional state and improving the quality of the answers. The question order optimization unit may also adjust the difficulty of questions according to the respondent's emotional state. For example, when the respondent is feeling stressed, easy questions can be asked first. This makes it possible to optimize the question order based on emotions.

[0095] The questionnaire system may further include a feedback providing unit that estimates the respondent's emotions and provides feedback based on the estimated emotions. For example, when the respondent has positive emotions, an encouraging message can be provided. This allows the respondent to be provided with feedback that corresponds to their emotional state, thereby improving their satisfaction. The feedback providing unit may also adjust the content of the feedback depending on the respondent's emotional state. For example, when the respondent has negative emotions, feedback that points out areas for improvement can be provided. This makes it possible to provide feedback based on emotions.

[0096] The questionnaire system can further include a design optimization unit that estimates the respondent's emotions and optimizes the questionnaire design based on the estimated emotions. For example, it can adopt colors and layouts that make the respondent feel relaxed. This can provide a design that matches the respondent's emotional state and improve the response rate. The design optimization unit can also adjust design elements according to the respondent's emotional state. For example, when the respondent has positive emotions, it can use bright colors and fun illustrations. This enables design optimization based on emotions.

[0097] The questionnaire system can further include an improvement suggestion unit that estimates the respondent's emotions and suggests improvements to the questionnaire based on the estimated emotions. For example, it can identify questions that the respondent has negative emotions about and make suggestions to improve those questions. This makes it possible to improve the questionnaire according to the respondent's emotional state. The improvement suggestion unit can also adjust the content of the questionnaire according to the respondent's emotional state. For example, when the respondent is feeling stressed, it can make a suggestion to increase questions that will help them relax. This makes it possible to suggest improvements to the questionnaire based on emotions.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The question generator analyzes the respondent's response and emotions, and generates the next question based on those. For example, if a respondent answers, "I'm not satisfied with a recent service," the generation AI generates a follow-up question such as, "What specifically were you dissatisfied with?" The generation AI also generates appropriate questions based on prompts that include the respondent's response and emotions. For example, if a respondent answers, "This product is so-so," the generation AI reads the respondent's emotions from the tone and speed of their voice and determines the level of satisfaction that the rating "so-so" indicates. Step 2: In the sentiment analysis section, the generation AI not only analyzes the content of the respondent's answers, but also their emotions using voice recognition technology. For example, the generation AI analyzes the tone and speed of the respondent's voice to determine their emotions. The sentiment analysis section also allows the generation AI to generate individually customized questions based on the respondent's past answer history and profile information. For example, if a respondent has previously shown interest in a particular product, the generation AI will ask them more questions related to that product.

[0100] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, 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. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0120] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. 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. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 7, a 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.

[0135] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0141] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0144] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0145] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0147] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0150] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0151] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0152] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0153] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0154] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0156] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0157] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0158] 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.

[0159] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0160] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0161] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0162] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0163] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0164] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0165] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0166] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a question generation unit that analyzes the content and emotions of the respondent and generates the next question based on the analysis; a sentiment analysis unit that presents the question generated by the question generation unit to the respondent; A system characterized by:

2. The question generation unit Analyzing the respondent's social media posts and generating questions based on their interests 2. The system of claim 1.

3. The question generation unit Generate questions in different languages ​​to enable multilingual surveys 2. The system of claim 1.

4. The emotion analysis unit Using facial expression recognition technology, the emotions of the respondent are analyzed from their facial expressions when answering.

2. The system of claim 1.

5. The question generation unit Analyzing the respondent's lifestyle and hobbies and preferences and personalizing the questions accordingly 2. The system of claim 1.

6. The aggregation section is Automatically compile survey results and generate reports in real time 2. The system of claim 1.

7. The profile generation unit Generate detailed profiles for each customer segment based on survey results 2. The system of claim 1.

8. The question generation unit Generate questions according to the respondent's emotional state and prioritize questions that elicit positive emotions 2. The system of claim 1.

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