system

Abstract

The system according to this embodiment aims to accurately capture the essential needs, emotions, and intentions of the respondents. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives the user's response. The analysis unit analyzes the response received by the reception unit and extracts the respondent's needs, emotions, and intentions. The generation unit generates follow-up questions based on the information extracted by the analysis unit. The provision unit provides the follow-up questions generated by the generation unit to the user.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to accurately capture the essential needs, feelings, and intentions of respondents from the answers to questionnaires.

[0005] The system according to the embodiment aims to accurately capture the essential needs, feelings, and intentions of respondents.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives responses from the user. The analysis unit analyzes the responses received by the reception unit and extracts the respondent's needs, emotions, and intentions. The generation unit generates follow-up questions based on the information extracted by the analysis unit. The provision unit provides the follow-up questions generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can accurately capture the essential needs, emotions, and intentions of the respondent. [Brief explanation of the drawing]

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

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The questionnaire system according to an embodiment of the present invention is a questionnaire method that utilizes generative AI to elicit the respondent's thoughts from multiple perspectives in a conversational format. In this questionnaire system, when a user answers a questionnaire, the generative AI asks questions in a conversational format. For example, if a user answers a question such as "What kind of drinks do you usually drink?", the generative AI will ask more detailed questions based on that answer. Next, the generative AI analyzes the user's answers and extracts the respondent's essential needs, feelings, and intentions. Furthermore, based on the insights obtained, the generative AI will ask the user more in-depth questions. This allows for the collection of detailed information about the user's behavior and preferences. In this way, the conversational questionnaire method utilizing generative AI can collect deeper insights that cannot be obtained with conventional questionnaires. This enables more accurate decision-making in formulating marketing strategies and product development. For example, if a user answers, "I usually drink coffee, but I drink tea on special occasions," the generative AI can obtain the insight from that answer that "the user prefers to drink tea on special occasions." Furthermore, based on the insights obtained, the generative AI will ask the user more in-depth questions. For example, questions such as "Why do you drink tea on special occasions?" or "In what situations do you most often drink tea?" can be asked. This allows for the collection of detailed information about user behavior and preferences. In this way, conversational survey methods utilizing generative AI can gather deeper insights that cannot be obtained with traditional surveys. This enables more accurate decision-making in formulating marketing strategies and product development. The survey system efficiently analyzes user responses and generates and provides follow-up questions, thereby gaining deeper insights.

[0029] The survey system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives user responses. User responses include, but are not limited to, text format, multiple-choice format, etc. The reception unit receives, for example, text data entered by the user. The reception unit can also receive responses in multiple-choice format. For example, the reception unit receives the response selected by the user from the multiple-choice options as data. Furthermore, the reception unit can also receive voice input. For example, the reception unit converts the content of the user's voice response into text data using voice recognition technology and receives it. The analysis unit uses a generation AI to analyze the responses received by the reception unit and extract the respondent's essential needs, emotions, and intentions. The analysis unit uses, for example, a generation AI to analyze the user's responses and extract the respondent's needs. The analysis unit can also use a generation AI to analyze the user's responses and estimate the respondent's emotions. Furthermore, the analysis unit can use a generation AI to analyze the user's responses and extract the respondent's intentions. For example, the generative AI extracts purchasing intent and information needs from the user's responses. The generative AI estimates emotions such as joy, sadness, and anger from the user's responses. The generative AI extracts the purpose of the action and the expected outcome from the user's responses. The generation unit uses the generative AI to generate follow-up questions based on the insights extracted by the analysis unit. For example, the generation unit generates questions that the generative AI asks for additional information based on the user's responses. The generation unit can also have the generative AI generate confirmation questions based on the user's responses. Furthermore, the generation unit can have the generative AI generate more detailed questions based on the user's responses. For example, the generative AI generates follow-up questions such as "Could you tell me more about that?" based on the user's responses. The generative AI generates confirmation questions such as "Could you explain this in more detail?" based on the user's responses. The generative AI generates more detailed questions such as "What exactly do you mean?" based on the user's responses. The delivery unit provides the follow-up questions generated by the generation unit to the user. The delivery unit displays the generated follow-up questions to the user, for example.Furthermore, the service provider can provide the generated follow-up questions to the user via voice. Additionally, the service provider can provide the generated follow-up questions to the user as a notification. For example, the service provider can display the generated follow-up questions to the user through a web application or mobile application. The service provider can provide the generated follow-up questions to the user via voice using speech synthesis technology. The service provider can also provide the generated follow-up questions to the user as a push notification. As a result, the survey system according to this embodiment can efficiently analyze user responses and generate and provide follow-up questions to gain deeper insights.

[0030] The reception unit receives user responses. User responses include, but are not limited to, text format, multiple-choice format, etc. The reception unit can, for example, receive text data entered by the user. The reception unit can also receive responses in multiple-choice format. For example, the reception unit can receive the answer selected by the user from the multiple-choice options as data. Furthermore, the reception unit can also receive voice input. For example, the reception unit can convert the content of a user's voice response into text data using speech recognition technology and receive it. The reception unit is designed to provide a user-friendly interface and allow for smooth input of responses. For example, users can easily input responses through web forms or mobile applications. In the case of voice input, the speech recognition technology is optimized to allow users to respond in a natural conversational format and measures are taken to minimize misrecognition. Furthermore, the reception unit has the function to encrypt and securely store the entered data in order to protect user privacy. This allows users to provide responses with peace of mind. The reception unit receives user responses in real time and immediately sends them to the analysis unit, enabling rapid data processing. This improves the overall efficiency of the survey system and allows for a quicker response to user needs.

[0031] The analysis unit uses generative AI to analyze responses received by the reception unit and extract the respondent's essential needs, emotions, and intentions. For example, the analysis unit uses generative AI to analyze user responses and extract the respondent's needs. The analysis unit can also use generative AI to analyze user responses and estimate the respondent's emotions. Furthermore, the analysis unit can use generative AI to analyze user responses and extract the respondent's intentions. For example, the generative AI can extract purchasing intent and the need for information from user responses. The generative AI can estimate emotions such as joy, sadness, and anger from user responses. The generative AI can extract the purpose of actions and expected results from user responses. The analysis unit utilizes natural language processing technology to analyze user responses in detail and read latent meanings and emotions from text data. For example, it uses sentiment analysis algorithms to classify positive, negative, and neutral emotions contained in user responses and understand the respondent's psychological state. It also uses intention analysis algorithms to clarify what the user wants and what actions they expect. Furthermore, the analysis unit refers to past response data and user behavior history to analyze respondent trends and patterns, thereby extracting needs and intentions with greater accuracy. This allows the analysis unit to gain a deeper understanding of user responses and respond to individual needs.

[0032] The generation unit uses a generation AI to generate follow-up questions based on insights extracted by the analysis unit. For example, the generation unit generates questions that ask for additional information based on the user's answers. The generation unit can also generate confirmation questions based on the user's answers. Furthermore, the generation unit can also generate detailed questions based on the user's answers. For example, the generation AI generates follow-up questions such as "Could you tell me more about that?" based on the user's answers. The generation AI generates confirmation questions such as "Could you explain this in more detail?" based on the user's answers. The generation AI generates detailed questions such as "What exactly do you mean?" based on the user's answers. The generation unit is equipped with an advanced algorithm to automatically generate appropriate follow-up questions according to the content of the user's answers. For example, if a user gives an opinion about a particular product, it generates questions that ask for more detailed information related to that opinion. Also, if a user shows an emotional reaction, it generates questions to explore the background and reasons for that emotion. By providing follow-up questions at the appropriate time in response to the user's answers, the generation unit can keep the user engaged and gain deeper insights. Furthermore, the generation unit can refer to the user's response history and generate consistent questions based on past responses. This allows users to have a consistent experience and improves the quality of the survey.

[0033] The service provider provides users with follow-up questions generated by the generation unit. For example, the service provider displays the generated follow-up questions to the user. The service provider can also provide the generated follow-up questions to the user via voice. Furthermore, the service provider can provide the generated follow-up questions to the user as notifications. For example, the service provider displays the generated follow-up questions to the user through a web application or mobile application. The service provider provides the generated follow-up questions to the user via voice using speech synthesis technology. The service provider provides the generated follow-up questions to the user as push notifications. The service provider provides an intuitive and user-friendly interface to enable users to answer follow-up questions quickly and easily. For example, web and mobile applications employ a simple and clear design to allow users to easily input their answers. In the case of voice delivery, speech recognition technology is integrated to enable users to answer via voice input. Furthermore, providing follow-up questions via push notifications ensures that users do not miss important questions and can answer them in a timely manner. The service provider also has the functionality to monitor the user's response status in real time and send reminders as needed. This can improve user response rates and maximize the effectiveness of surveys.

[0034] The analysis unit can analyze user responses and extract information about the respondent's behavior and preferences. For example, the analysis unit can extract purchasing behavior from user responses. The analysis unit can also extract browsing behavior from user responses. The analysis unit can also extract favorite products from user responses. The analysis unit can also extract topics of interest from user responses. This allows for deeper insights by extracting detailed information about the user's behavior and preferences. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input user responses into a generative AI and have the generative AI perform the extraction of information about behavior and preferences.

[0035] The reception department can analyze the user's past response history and select an appropriate reception method. For example, the reception department may prioritize suggesting response methods that the user has preferred in the past. For example, the reception department may analyze the user's past response times and prompt them to respond at the optimal time. For example, the reception department may consider the user's past response frequency and prompt them to respond at an appropriate frequency. In this way, the reception department can select the optimal reception method by analyzing the user's past response history. Some or all of the above processing in the reception department may be performed using AI or not. For example, the reception department may input the user's past response history data into a generating AI and have the generating AI select an appropriate reception method.

[0036] The reception unit can filter responses based on the user's current situation and areas of interest. For example, the reception unit prioritizes questions related to topics the user is currently interested in. For example, the reception unit selects appropriate questions based on the user's current situation (e.g., working, on break). For example, the reception unit filters questions based on the user's past responses to ensure they are relevant. This allows the reception unit to receive highly relevant questions by filtering based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's current situation data and areas of interest data into a generating AI and have the generating AI perform the filtering.

[0037] The reception desk can prioritize receiving responses that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize questions related to that region. For example, if the user is traveling, the reception desk will prioritize questions related to their travel destination. For example, if the user is at home, the reception desk will prioritize questions related to their home. In this way, by considering the user's geographical location, the reception desk can prioritize receiving questions that are highly relevant. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI select highly relevant questions.

[0038] The reception unit can analyze the user's social media activity when receiving responses and accept relevant responses. For example, the reception unit can prioritize questions related to topics the user has shown interest in on social media. For example, the reception unit can consider the user's social media activity times and prompt responses at the most optimal time. For example, the reception unit can analyze the user's social media friendships and accept relevant questions. In this way, by analyzing the user's social media activity, it is possible to accept highly relevant questions. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI select relevant questions.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the responses during the analysis. For example, the analysis unit performs a detailed analysis for important responses. For example, the analysis unit performs a concise analysis for general responses. For example, the analysis unit performs a theme-specific analysis for responses related to a particular theme. In this way, by adjusting the level of detail of the analysis based on the importance of the responses, a detailed analysis can be performed for important responses. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input response importance data into a generative AI and have the generative AI perform the adjustment of the level of detail.

[0040] The analysis unit can apply different analysis algorithms depending on the category of the response during analysis. For example, the analysis unit applies an emotion analysis algorithm to responses related to emotions. For example, the analysis unit applies a behavior analysis algorithm to responses related to behavior. For example, the analysis unit applies a preference analysis algorithm to responses related to preferences. By applying different analysis algorithms depending on the category of the response, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input response category data into a generative AI and have the generative AI execute the application of an appropriate analysis algorithm.

[0041] The analysis unit can determine the priority of analysis based on the submission date of the responses during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent responses. For example, the analysis unit may prioritize the analysis of responses submitted within a specific period. For example, the analysis unit may analyze current responses while referring to past responses. This allows the analysis unit to prioritize the analysis of the most recent responses by determining the priority of analysis based on the submission date of the responses. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input response submission date data into a generation AI and have the generation AI perform the priority determination.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the answers during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant answers. For example, the analysis unit may prioritize the analysis of answers related to a specific theme. For example, the analysis unit may prioritize the analysis of answers that are relevant to the user's past answers. In this way, by adjusting the order of analysis based on the relevance of the answers, highly relevant answers can be prioritized. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the relevance data of the answers into a generative AI and have the generative AI perform the order adjustment.

[0043] The generation unit can adjust the level of detail of follow-up questions based on the importance of the answers when generating follow-up questions. For example, the generation unit can ask detailed follow-up questions for important answers. For example, the generation unit can ask concise follow-up questions for general answers. For example, the generation unit can ask theme-specific follow-up questions for answers related to a particular theme. In this way, by adjusting the level of detail of questions based on the importance of the answers, detailed follow-up questions can be asked for important answers. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input answer importance data into a generation AI and have the generation AI perform the level of detail adjustment.

[0044] The generation unit can apply different question generation algorithms depending on the category of the answer when generating follow-up questions. For example, the generation unit can create follow-up questions using an emotion analysis algorithm for answers related to emotions, for example, for answers related to behavior, and for answers related to preferences, for example, for answers related to preferences. By applying different question generation algorithms depending on the category of the answer, more appropriate follow-up questions can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the answer category data into a generation AI and cause the generation AI to execute the application of an appropriate question generation algorithm.

[0045] The generation unit can determine the priority of follow-up questions based on the submission date of the answers when generating follow-up questions. For example, the generation unit can prioritize follow-up questions for the most recent answers. For example, the generation unit can prioritize follow-up questions for answers submitted within a specific period. For example, the generation unit can ask follow-up questions for current answers while referring to past answers. In this way, by determining the priority of questions based on the submission date of the answers, follow-up questions can be prioritized for the most recent answers. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the answer submission date data into the generation AI and have the generation AI perform the priority determination.

[0046] The generation unit can adjust the order of follow-up questions based on the relevance of the answers when generating follow-up questions. For example, the generation unit can prioritize asking follow-up questions to highly relevant answers. For example, the generation unit can prioritize asking follow-up questions to answers related to a specific theme. For example, the generation unit can prioritize asking follow-up questions to answers that are relevant to the user's past answers. In this way, by adjusting the order of questions based on the relevance of the answers, follow-up questions can be prioritized for highly relevant answers. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the relevance data of the answers into a generation AI and have the generation AI perform the order adjustment.

[0047] The service provider can select the optimal delivery method when providing follow-up questions by referring to the user's past response history. For example, the service provider may prioritize suggesting delivery methods that the user has preferred in the past. For example, the service provider may analyze the user's past response times and provide follow-up questions at the optimal time. For example, the service provider may consider the user's past response frequency and provide follow-up questions at an appropriate frequency. This allows the service provider to select the optimal delivery method by referring to the user's past response history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider may input the user's past response history data into a generating AI and have the generating AI select an appropriate delivery method.

[0048] The service provider can select the optimal method of providing follow-up questions by considering the user's device information. For example, if the user is using a smartphone, the service provider will provide follow-up questions tailored to the screen size. For example, if the user is using a tablet, the service provider will provide follow-up questions optimized for a larger screen. For example, if the user is using a smartwatch, the service provider will provide concise and highly visible follow-up questions. This allows the service provider to select the optimal method of providing questions by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user device information data into a generating AI and have the generating AI select an appropriate method of providing questions.

[0049] The service provider can provide multilingual follow-up questions according to the user's language settings when providing follow-up questions. For example, the service provider can automatically set the language of the follow-up questions based on the language settings of the user's device. For example, the service provider can provide a language switching function if the user uses multiple languages. For example, if the user selects a specific language, the service provider can provide follow-up questions in that language. This allows for the provision of more appropriate follow-up questions by providing multilingual follow-up questions according to the user's language settings. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's language setting data into a generating AI and have the generating AI generate multilingual follow-up questions.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The survey system can also collect information about users' lifestyles and health status based on their responses. For example, if a user answers a question such as, "What kind of exercise do you usually do?", the generating AI will then ask more detailed questions based on that answer. Next, the generating AI will analyze the user's responses to gain insights into the user's exercise habits and health status. Furthermore, based on the insights gained, the generating AI will ask the user even more in-depth questions. For example, it may ask questions such as, "How many times a week do you exercise?" or "What kind of exercise do you prefer?" This allows for the collection of detailed information about the user's lifestyle and health status. This enables more accurate decision-making in health management and fitness plan development.

[0052] The survey system can also collect information about users' hobbies and interests based on their responses. For example, if a user answers a question like, "What are your hobbies?", the generating AI will ask more detailed questions based on that answer. Next, the generating AI analyzes the user's responses to gain insights into their hobbies and interests. Furthermore, based on the insights gained, the generating AI will ask the user more in-depth questions. For example, it might ask, "What kind of books do you usually read?" or "What kind of movies do you like to watch?" This allows for the collection of detailed information about the user's hobbies and interests. This enables more accurate decision-making in formulating marketing strategies and providing content.

[0053] The survey system can also collect information about users' purchasing behavior and consumption patterns based on their responses. For example, when a user answers a question such as, "What kind of products do you usually buy?", the generating AI will ask more detailed questions based on that answer. Next, the generating AI analyzes the user's responses to gain insights into the user's purchasing behavior and consumption patterns. Furthermore, based on the insights gained, the generating AI will ask the user even more in-depth questions. For example, it may ask questions such as, "What brands do you prefer to buy?" or "How often do you buy products?" This allows for the collection of detailed information about users' purchasing behavior and consumption patterns. This enables more accurate decision-making in formulating marketing strategies and product development.

[0054] The survey system can also collect information about users' travel preferences and destinations based on their responses. For example, if a user answers a question like, "What kind of trips do you usually take?", the generating AI will ask more detailed questions based on that answer. Next, the generating AI analyzes the user's responses to gain insights into their travel preferences and destinations. Furthermore, based on the insights gained, the generating AI will ask the user more in-depth questions, such as, "What kind of travel destinations do you like?" or "What kind of activities do you enjoy?". This allows for the collection of detailed information about the user's travel preferences and destinations. This enables more accurate decision-making in suggesting travel plans and promoting tourist destinations.

[0055] The survey system can also collect information about users' food preferences and eating habits based on their responses. For example, if a user answers a question such as, "What kind of meals do you usually eat?", the generating AI will then ask more detailed questions based on that answer. Next, the generating AI analyzes the user's responses to gain insights into the user's food preferences and eating habits. Furthermore, based on the insights gained, the generating AI will ask the user even more in-depth questions. For example, it may ask questions such as, "What kind of dishes do you like to eat?" or "What ingredients do you often use?" This allows for the collection of detailed information about the user's food preferences and eating habits. This enables more accurate decision-making in suggesting meal plans and developing food products.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception desk receives user responses. User responses can be in text format, multiple-choice format, or voice input. For example, it can accept text data entered by the user or responses selected from multiple-choice options. It can also convert voice responses using speech recognition technology into text data and accept that as well. Step 2: The analysis unit uses a generation AI to analyze the responses received by the reception unit and extract the respondent's needs, emotions, and intentions. For example, the generation AI analyzes the user's responses and extracts their willingness to purchase, need for information, emotions such as joy, sadness, and anger, the purpose of their actions, and the results they expect. Step 3: The generation unit uses the generation AI to generate follow-up questions based on the insights extracted by the analysis unit. For example, the generation AI generates questions that ask for additional information, confirmation questions, and follow-up questions based on the user's answers. Step 4: The providing unit provides the follow-up questions generated by the generating unit to the user. For example, the generated follow-up questions may be displayed through a web application or mobile application, provided as voice using speech synthesis technology, or provided as a push notification.

[0058] (Example of form 2) The questionnaire system according to an embodiment of the present invention is a questionnaire method that utilizes generative AI to elicit the respondent's thoughts from multiple perspectives in a conversational format. In this questionnaire system, when a user answers a questionnaire, the generative AI asks questions in a conversational format. For example, if a user answers a question such as "What kind of drinks do you usually drink?", the generative AI will ask more detailed questions based on that answer. Next, the generative AI analyzes the user's answers and extracts the respondent's essential needs, feelings, and intentions. Furthermore, based on the insights obtained, the generative AI will ask the user more in-depth questions. This allows for the collection of detailed information about the user's behavior and preferences. In this way, the conversational questionnaire method utilizing generative AI can collect deeper insights that cannot be obtained with conventional questionnaires. This enables more accurate decision-making in formulating marketing strategies and product development. For example, if a user answers, "I usually drink coffee, but I drink tea on special occasions," the generative AI can obtain the insight from that answer that "the user prefers to drink tea on special occasions." Furthermore, based on the insights obtained, the generative AI will ask the user more in-depth questions. For example, questions such as "Why do you drink tea on special occasions?" or "In what situations do you most often drink tea?" can be asked. This allows for the collection of detailed information about user behavior and preferences. In this way, conversational survey methods utilizing generative AI can gather deeper insights that cannot be obtained with traditional surveys. This enables more accurate decision-making in formulating marketing strategies and product development. The survey system efficiently analyzes user responses and generates and provides follow-up questions, thereby gaining deeper insights.

[0059] The survey system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives user responses. User responses include, but are not limited to, text format, multiple-choice format, etc. The reception unit receives, for example, text data entered by the user. The reception unit can also receive responses in multiple-choice format. For example, the reception unit receives the response selected by the user from the multiple-choice options as data. Furthermore, the reception unit can also receive voice input. For example, the reception unit converts the content of the user's voice response into text data using voice recognition technology and receives it. The analysis unit uses a generation AI to analyze the responses received by the reception unit and extract the respondent's essential needs, emotions, and intentions. The analysis unit uses, for example, a generation AI to analyze the user's responses and extract the respondent's needs. The analysis unit can also use a generation AI to analyze the user's responses and estimate the respondent's emotions. Furthermore, the analysis unit can use a generation AI to analyze the user's responses and extract the respondent's intentions. For example, the generative AI extracts purchasing intent and information needs from the user's responses. The generative AI estimates emotions such as joy, sadness, and anger from the user's responses. The generative AI extracts the purpose of the action and the expected outcome from the user's responses. The generation unit uses the generative AI to generate follow-up questions based on the insights extracted by the analysis unit. For example, the generation unit generates questions that the generative AI asks for additional information based on the user's responses. The generation unit can also have the generative AI generate confirmation questions based on the user's responses. Furthermore, the generation unit can have the generative AI generate more detailed questions based on the user's responses. For example, the generative AI generates follow-up questions such as "Could you tell me more about that?" based on the user's responses. The generative AI generates confirmation questions such as "Could you explain this in more detail?" based on the user's responses. The generative AI generates more detailed questions such as "What exactly do you mean?" based on the user's responses. The delivery unit provides the follow-up questions generated by the generation unit to the user. The delivery unit displays the generated follow-up questions to the user, for example.Furthermore, the service provider can provide the generated follow-up questions to the user via voice. Additionally, the service provider can provide the generated follow-up questions to the user as a notification. For example, the service provider can display the generated follow-up questions to the user through a web application or mobile application. The service provider can provide the generated follow-up questions to the user via voice using speech synthesis technology. The service provider can also provide the generated follow-up questions to the user as a push notification. As a result, the survey system according to this embodiment can efficiently analyze user responses and generate and provide follow-up questions to gain deeper insights.

[0060] The reception unit receives user responses. User responses include, but are not limited to, text format, multiple-choice format, etc. The reception unit can, for example, receive text data entered by the user. The reception unit can also receive responses in multiple-choice format. For example, the reception unit can receive the answer selected by the user from the multiple-choice options as data. Furthermore, the reception unit can also receive voice input. For example, the reception unit can convert the content of a user's voice response into text data using speech recognition technology and receive it. The reception unit is designed to provide a user-friendly interface and allow for smooth input of responses. For example, users can easily input responses through web forms or mobile applications. In the case of voice input, the speech recognition technology is optimized to allow users to respond in a natural conversational format and measures are taken to minimize misrecognition. Furthermore, the reception unit has the function to encrypt and securely store the entered data in order to protect user privacy. This allows users to provide responses with peace of mind. The reception unit receives user responses in real time and immediately sends them to the analysis unit, enabling rapid data processing. This improves the overall efficiency of the survey system and allows for a quicker response to user needs.

[0061] The analysis unit uses generative AI to analyze responses received by the reception unit and extract the respondent's essential needs, emotions, and intentions. For example, the analysis unit uses generative AI to analyze user responses and extract the respondent's needs. The analysis unit can also use generative AI to analyze user responses and estimate the respondent's emotions. Furthermore, the analysis unit can use generative AI to analyze user responses and extract the respondent's intentions. For example, the generative AI can extract purchasing intent and the need for information from user responses. The generative AI can estimate emotions such as joy, sadness, and anger from user responses. The generative AI can extract the purpose of actions and expected results from user responses. The analysis unit utilizes natural language processing technology to analyze user responses in detail and read latent meanings and emotions from text data. For example, it uses sentiment analysis algorithms to classify positive, negative, and neutral emotions contained in user responses and understand the respondent's psychological state. It also uses intention analysis algorithms to clarify what the user wants and what actions they expect. Furthermore, the analysis unit refers to past response data and user behavior history to analyze respondent trends and patterns, thereby extracting needs and intentions with greater accuracy. This allows the analysis unit to gain a deeper understanding of user responses and respond to individual needs.

[0062] The generation unit uses a generation AI to generate follow-up questions based on insights extracted by the analysis unit. For example, the generation unit generates questions that ask for additional information based on the user's answers. The generation unit can also generate confirmation questions based on the user's answers. Furthermore, the generation unit can also generate detailed questions based on the user's answers. For example, the generation AI generates follow-up questions such as "Could you tell me more about that?" based on the user's answers. The generation AI generates confirmation questions such as "Could you explain this in more detail?" based on the user's answers. The generation AI generates detailed questions such as "What exactly do you mean?" based on the user's answers. The generation unit is equipped with an advanced algorithm to automatically generate appropriate follow-up questions according to the content of the user's answers. For example, if a user gives an opinion about a particular product, it generates questions that ask for more detailed information related to that opinion. Also, if a user shows an emotional reaction, it generates questions to explore the background and reasons for that emotion. By providing follow-up questions at the appropriate time in response to the user's answers, the generation unit can keep the user engaged and gain deeper insights. Furthermore, the generation unit can refer to the user's response history and generate consistent questions based on past responses. This allows users to have a consistent experience and improves the quality of the survey.

[0063] The service provider provides users with follow-up questions generated by the generation unit. For example, the service provider displays the generated follow-up questions to the user. The service provider can also provide the generated follow-up questions to the user via voice. Furthermore, the service provider can provide the generated follow-up questions to the user as notifications. For example, the service provider displays the generated follow-up questions to the user through a web application or mobile application. The service provider provides the generated follow-up questions to the user via voice using speech synthesis technology. The service provider provides the generated follow-up questions to the user as push notifications. The service provider provides an intuitive and user-friendly interface to enable users to answer follow-up questions quickly and easily. For example, web and mobile applications employ a simple and clear design to allow users to easily input their answers. In the case of voice delivery, speech recognition technology is integrated to enable users to answer via voice input. Furthermore, providing follow-up questions via push notifications ensures that users do not miss important questions and can answer them in a timely manner. The service provider also has the functionality to monitor the user's response status in real time and send reminders as needed. This can improve user response rates and maximize the effectiveness of surveys.

[0064] The analysis unit can analyze user responses and extract information about the respondent's behavior and preferences. For example, the analysis unit can extract purchasing behavior from user responses. The analysis unit can also extract browsing behavior from user responses. The analysis unit can also extract favorite products from user responses. The analysis unit can also extract topics of interest from user responses. This allows for deeper insights by extracting detailed information about the user's behavior and preferences. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input user responses into a generative AI and have the generative AI perform the extraction of information about behavior and preferences.

[0065] The reception unit can estimate the user's emotions and adjust the timing of response reception based on the estimated emotions. For example, if the user is stressed, the reception unit may prompt them to respond at a time when they can relax. For example, if the user is focused, the reception unit may accept the response immediately. For example, if the user is tired, the reception unit may prompt them to respond after a break. By adjusting the timing of response reception according to the user's emotions, responses can be received at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0066] The reception department can analyze the user's past response history and select an appropriate reception method. For example, the reception department may prioritize suggesting response methods that the user has preferred in the past. For example, the reception department may analyze the user's past response times and prompt them to respond at the optimal time. For example, the reception department may consider the user's past response frequency and prompt them to respond at an appropriate frequency. In this way, the reception department can select the optimal reception method by analyzing the user's past response history. Some or all of the above processing in the reception department may be performed using AI or not. For example, the reception department may input the user's past response history data into a generating AI and have the generating AI select an appropriate reception method.

[0067] The reception unit can filter responses based on the user's current situation and areas of interest. For example, the reception unit prioritizes questions related to topics the user is currently interested in. For example, the reception unit selects appropriate questions based on the user's current situation (e.g., working, on break). For example, the reception unit filters questions based on the user's past responses to ensure they are relevant. This allows the reception unit to receive highly relevant questions by filtering based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's current situation data and areas of interest data into a generating AI and have the generating AI perform the filtering.

[0068] The reception desk can estimate the user's emotions and determine the priority of the answers to be received based on the estimated emotions. For example, if the user is excited, the reception desk will prioritize important questions. For example, if the user is relaxed, the reception desk will prioritize detailed questions. For example, if the user is tired, the reception desk will prioritize simple questions. In this way, important questions can be prioritized by determining the priority of answers according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion-based priority determination.

[0069] The reception desk can prioritize receiving responses that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize questions related to that region. For example, if the user is traveling, the reception desk will prioritize questions related to their travel destination. For example, if the user is at home, the reception desk will prioritize questions related to their home. In this way, by considering the user's geographical location, the reception desk can prioritize receiving questions that are highly relevant. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI select highly relevant questions.

[0070] The reception unit can analyze the user's social media activity when receiving responses and accept relevant responses. For example, the reception unit can prioritize questions related to topics the user has shown interest in on social media. For example, the reception unit can consider the user's social media activity times and prompt responses at the most optimal time. For example, the reception unit can analyze the user's social media friendships and accept relevant questions. In this way, by analyzing the user's social media activity, it is possible to accept highly relevant questions. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI select relevant questions.

[0071] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is in a hurry, the analysis unit provides concise analysis results that get straight to the point. For example, if the user is excited, the analysis unit provides visually stimulating analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform adjustments to the presentation based on the emotions.

[0072] The analysis unit can adjust the level of detail of the analysis based on the importance of the responses during the analysis. For example, the analysis unit performs a detailed analysis for important responses. For example, the analysis unit performs a concise analysis for general responses. For example, the analysis unit performs a theme-specific analysis for responses related to a particular theme. In this way, by adjusting the level of detail of the analysis based on the importance of the responses, a detailed analysis can be performed for important responses. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input response importance data into a generative AI and have the generative AI perform the adjustment of the level of detail.

[0073] The analysis unit can apply different analysis algorithms depending on the category of the response during analysis. For example, the analysis unit applies an emotion analysis algorithm to responses related to emotions. For example, the analysis unit applies a behavior analysis algorithm to responses related to behavior. For example, the analysis unit applies a preference analysis algorithm to responses related to preferences. By applying different analysis algorithms depending on the category of the response, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input response category data into a generative AI and have the generative AI execute the application of an appropriate analysis algorithm.

[0074] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will perform a short, concise analysis. If the user is relaxed, the analysis unit will perform a detailed analysis. If the user is excited, the analysis unit will perform a visually stimulating analysis. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis based on the emotions.

[0075] The analysis unit can determine the priority of analysis based on the submission date of the responses during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent responses. For example, the analysis unit may prioritize the analysis of responses submitted within a specific period. For example, the analysis unit may analyze current responses while referring to past responses. This allows the analysis unit to prioritize the analysis of the most recent responses by determining the priority of analysis based on the submission date of the responses. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input response submission date data into a generation AI and have the generation AI perform the priority determination.

[0076] The analysis unit can adjust the order of analysis based on the relevance of the answers during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant answers. For example, the analysis unit may prioritize the analysis of answers related to a specific theme. For example, the analysis unit may prioritize the analysis of answers that are relevant to the user's past answers. In this way, by adjusting the order of analysis based on the relevance of the answers, highly relevant answers can be prioritized. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the relevance data of the answers into a generative AI and have the generative AI perform the order adjustment.

[0077] The generation unit can estimate the user's emotions and adjust the wording of follow-up questions based on the estimated emotions. For example, if the user is relaxed, the generation unit will ask detailed follow-up questions. If the user is in a hurry, the generation unit will ask concise follow-up questions. If the user is excited, the generation unit will ask visually stimulating follow-up questions. By adjusting the wording of follow-up questions according to the user's emotions, more appropriate follow-up questions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using or without a generative AI. For example, the generation unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of wording based on emotions.

[0078] The generation unit can adjust the level of detail of follow-up questions based on the importance of the answers when generating follow-up questions. For example, the generation unit can ask detailed follow-up questions for important answers. For example, the generation unit can ask concise follow-up questions for general answers. For example, the generation unit can ask theme-specific follow-up questions for answers related to a particular theme. In this way, by adjusting the level of detail of questions based on the importance of the answers, detailed follow-up questions can be asked for important answers. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input answer importance data into a generation AI and have the generation AI perform the level of detail adjustment.

[0079] The generation unit can apply different question generation algorithms depending on the category of the answer when generating follow-up questions. For example, the generation unit can create follow-up questions using an emotion analysis algorithm for answers related to emotions, for example, for answers related to behavior, and for answers related to preferences, for example, for answers related to preferences. By applying different question generation algorithms depending on the category of the answer, more appropriate follow-up questions can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the answer category data into a generation AI and cause the generation AI to execute the application of an appropriate question generation algorithm.

[0080] The generation unit can estimate the user's emotions and adjust the length of follow-up questions based on the estimated emotions. For example, if the user is in a hurry, the generation unit will ask short, to the point. If the user is relaxed, the generation unit will ask detailed follow-up questions. If the user is excited, the generation unit will ask visually stimulating follow-up questions. By adjusting the length of follow-up questions according to the user's emotions, more appropriate follow-up questions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using or without a generative AI. For example, the generation unit can input user emotion data into a generative AI and have the generative AI adjust the length of questions based on emotions.

[0081] The generation unit can determine the priority of follow-up questions based on the submission date of the answers when generating follow-up questions. For example, the generation unit can prioritize follow-up questions for the most recent answers. For example, the generation unit can prioritize follow-up questions for answers submitted within a specific period. For example, the generation unit can ask follow-up questions for current answers while referring to past answers. In this way, by determining the priority of questions based on the submission date of the answers, follow-up questions can be prioritized for the most recent answers. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the answer submission date data into the generation AI and have the generation AI perform the priority determination.

[0082] The generation unit can adjust the order of follow-up questions based on the relevance of the answers when generating follow-up questions. For example, the generation unit can prioritize asking follow-up questions to highly relevant answers. For example, the generation unit can prioritize asking follow-up questions to answers related to a specific theme. For example, the generation unit can prioritize asking follow-up questions to answers that are relevant to the user's past answers. In this way, by adjusting the order of questions based on the relevance of the answers, follow-up questions can be prioritized for highly relevant answers. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the relevance data of the answers into a generation AI and have the generation AI perform the order adjustment.

[0083] The service provider can estimate the user's emotions and adjust the way follow-up questions are presented based on the estimated emotions. For example, if the user is relaxed, the service provider may provide detailed follow-up questions. If the user is in a hurry, the service provider may provide concise follow-up questions. If the user is excited, the service provider may provide visually stimulating follow-up questions. By adjusting the way follow-up questions are presented according to the user's emotions, more appropriate follow-up questions can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion-based adjustments to the service provider's presentation.

[0084] The service provider can select the optimal delivery method when providing follow-up questions by referring to the user's past response history. For example, the service provider may prioritize suggesting delivery methods that the user has preferred in the past. For example, the service provider may analyze the user's past response times and provide follow-up questions at the optimal time. For example, the service provider may consider the user's past response frequency and provide follow-up questions at an appropriate frequency. This allows the service provider to select the optimal delivery method by referring to the user's past response history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider may input the user's past response history data into a generating AI and have the generating AI select an appropriate delivery method.

[0085] The service provider can estimate the user's emotions and adjust the procedure for providing follow-up questions based on the estimated emotions. For example, if the user is nervous, the service provider will provide follow-up questions in a calm voice. For example, if the user is relaxed, the service provider will provide follow-up questions in a cheerful voice. For example, if the user is in a hurry, the service provider will provide quick and concise follow-up questions. This allows for the provision of more appropriate follow-up questions by adjusting the procedure for providing follow-up questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform adjustments to the service procedure based on emotions.

[0086] The service provider can select the optimal method of providing follow-up questions by considering the user's device information. For example, if the user is using a smartphone, the service provider will provide follow-up questions tailored to the screen size. For example, if the user is using a tablet, the service provider will provide follow-up questions optimized for a larger screen. For example, if the user is using a smartwatch, the service provider will provide concise and highly visible follow-up questions. This allows the service provider to select the optimal method of providing questions by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user device information data into a generating AI and have the generating AI select an appropriate method of providing questions.

[0087] The service provider can provide multilingual follow-up questions according to the user's language settings when providing follow-up questions. For example, the service provider can automatically set the language of the follow-up questions based on the language settings of the user's device. For example, the service provider can provide a language switching function if the user uses multiple languages. For example, if the user selects a specific language, the service provider can provide follow-up questions in that language. This allows for the provision of more appropriate follow-up questions by providing multilingual follow-up questions according to the user's language settings. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's language setting data into a generating AI and have the generating AI generate multilingual follow-up questions.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The survey system can also collect information about users' lifestyles and health status based on their responses. For example, if a user answers a question such as, "What kind of exercise do you usually do?", the generating AI will then ask more detailed questions based on that answer. Next, the generating AI will analyze the user's responses to gain insights into the user's exercise habits and health status. Furthermore, based on the insights gained, the generating AI will ask the user even more in-depth questions. For example, it may ask questions such as, "How many times a week do you exercise?" or "What kind of exercise do you prefer?" This allows for the collection of detailed information about the user's lifestyle and health status. This enables more accurate decision-making in health management and fitness plan development.

[0090] The survey system can also collect information about users' hobbies and interests based on their responses. For example, if a user answers a question like, "What are your hobbies?", the generating AI will ask more detailed questions based on that answer. Next, the generating AI analyzes the user's responses to gain insights into their hobbies and interests. Furthermore, based on the insights gained, the generating AI will ask the user more in-depth questions. For example, it might ask, "What kind of books do you usually read?" or "What kind of movies do you like to watch?" This allows for the collection of detailed information about the user's hobbies and interests. This enables more accurate decision-making in formulating marketing strategies and providing content.

[0091] The survey system can also collect information about users' purchasing behavior and consumption patterns based on their responses. For example, when a user answers a question such as, "What kind of products do you usually buy?", the generating AI will ask more detailed questions based on that answer. Next, the generating AI analyzes the user's responses to gain insights into the user's purchasing behavior and consumption patterns. Furthermore, based on the insights gained, the generating AI will ask the user even more in-depth questions. For example, it may ask questions such as, "What brands do you prefer to buy?" or "How often do you buy products?" This allows for the collection of detailed information about users' purchasing behavior and consumption patterns. This enables more accurate decision-making in formulating marketing strategies and product development.

[0092] The survey system can also collect information about users' travel preferences and destinations based on their responses. For example, if a user answers a question like, "What kind of trips do you usually take?", the generating AI will ask more detailed questions based on that answer. Next, the generating AI analyzes the user's responses to gain insights into their travel preferences and destinations. Furthermore, based on the insights gained, the generating AI will ask the user more in-depth questions, such as, "What kind of travel destinations do you like?" or "What kind of activities do you enjoy?". This allows for the collection of detailed information about the user's travel preferences and destinations. This enables more accurate decision-making in suggesting travel plans and promoting tourist destinations.

[0093] The survey system can also collect information about users' food preferences and eating habits based on their responses. For example, if a user answers a question such as, "What kind of meals do you usually eat?", the generating AI will then ask more detailed questions based on that answer. Next, the generating AI analyzes the user's responses to gain insights into the user's food preferences and eating habits. Furthermore, based on the insights gained, the generating AI will ask the user even more in-depth questions. For example, it may ask questions such as, "What kind of dishes do you like to eat?" or "What ingredients do you often use?" This allows for the collection of detailed information about the user's food preferences and eating habits. This enables more accurate decision-making in suggesting meal plans and developing food products.

[0094] The survey system can estimate a user's emotions and assess their stress level based on those emotions. For example, when a user answers a question such as, "How has work been going lately?", the generating AI estimates the user's emotions from the answer and assesses their stress level. Next, the generating AI can provide advice for stress reduction based on the user's stress level. For example, it might ask questions such as, "What methods do you try to relax?" or "How do you cope when you feel stressed?" This allows the system to assess the user's stress level and provide appropriate advice, thereby contributing to the improvement of the user's mental health.

[0095] The survey system can estimate a user's emotions and evaluate their motivation based on those emotions. For example, when a user answers a question such as, "How is your recent progress towards achieving your goals?", the generating AI estimates the user's emotions from the answer and evaluates their motivation. Next, the generating AI can provide advice to improve the user's motivation based on that evaluation. For example, it might ask questions such as, "What methods are you trying to achieve your goals?" or "What are you doing to maintain your motivation?" This allows the system to evaluate the user's motivation and provide appropriate advice, thereby supporting the user in achieving their goals.

[0096] The survey system can estimate a user's emotions and evaluate their happiness level based on those emotions. For example, when a user answers a question such as, "How satisfied are you with your life lately?", the generating AI estimates the user's emotions from the answer and evaluates their happiness level. Next, the generating AI can provide advice to improve the user's happiness level based on that evaluation. For example, it might ask questions such as, "What are you doing to improve your life satisfaction?" or "What moments make you feel happy?". This allows the system to evaluate the user's happiness level and provide appropriate advice, thereby contributing to improving the user's quality of life.

[0097] The survey system can estimate a user's emotions and evaluate their sociability based on those emotions. For example, when a user answers a question such as, "How are your relationships lately?", the generative AI estimates the user's emotions from the answer and evaluates their sociability. Next, the generative AI can provide advice to improve the user's sociability based on that evaluation. For example, it might ask questions such as, "What do you do to maintain good relationships?" or "What activities do you engage in to make new friends?" This allows the system to evaluate the user's sociability and provide appropriate advice, thereby contributing to the improvement of the user's relationships.

[0098] The survey system can estimate a user's emotions and evaluate their creativity based on those emotions. For example, when a user answers a question such as, "How has your creative work been going lately?", the generative AI estimates the user's emotions from the answer and evaluates their creativity. Next, the generative AI can provide advice to improve the user's creativity based on that evaluation. For example, it might ask questions such as, "What strategies do you use to continue your creative work?" or "What methods do you try to generate new ideas?" This allows the system to evaluate the user's creativity and provide appropriate advice, thereby contributing to supporting the user's creative activities.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The reception desk receives user responses. User responses can be in text format, multiple-choice format, or voice input. For example, it can accept text data entered by the user or responses selected from multiple-choice options. It can also convert voice responses using speech recognition technology into text data and accept that as well. Step 2: The analysis unit uses a generation AI to analyze the responses received by the reception unit and extract the respondent's needs, emotions, and intentions. For example, the generation AI analyzes the user's responses and extracts their willingness to purchase, need for information, emotions such as joy, sadness, and anger, the purpose of their actions, and the results they expect. Step 3: The generation unit uses the generation AI to generate follow-up questions based on the insights extracted by the analysis unit. For example, the generation AI generates questions that ask for additional information, confirmation questions, and follow-up questions based on the user's answers. Step 4: The providing unit provides the follow-up questions generated by the generating unit to the user. For example, the generated follow-up questions may be displayed through a web application or mobile application, provided as voice using speech synthesis technology, or provided as a push notification.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0103] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives text or voice input from the user. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes the user's response using generation AI to extract needs, emotions, and intentions. The generation unit is implemented by the identification processing unit 290 of the data processing device 12 and generates follow-up questions based on the analysis results. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated follow-up questions to the user by display or voice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0111] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0112] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0113] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0114] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0115] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives text or voice input from the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the user's responses using generation AI to extract needs, emotions, and intentions. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates follow-up questions based on the analysis results. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated follow-up questions to the user by display or voice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives text or voice input from the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the user's responses using generation AI to extract needs, emotions, and intentions. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates follow-up questions based on the analysis results. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated follow-up questions to the user by display or voice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0145] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives text or voice input from the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the user's response using generation AI to extract needs, emotions, and intentions. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates follow-up questions based on the analysis results. The provision unit is implemented by the control unit 46A of the robot 414 and provides the generated follow-up questions to the user by display or voice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0154] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0156] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0157] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0158] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0162] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0163] 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.

[0164] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0165] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0166] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0167] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0169] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0170] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0171] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0172] (Note 1) A reception desk that accepts user responses, An analysis unit analyzes the responses received by the reception unit and extracts the respondents' needs, emotions, and intentions. A generation unit generates follow-up questions based on the information extracted by the analysis unit, The system includes a providing unit that provides the user with follow-up questions generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze user responses and extract information about respondents' behavior and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of response submissions based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is Analyze the user's past response history and select the appropriate method of receiving their response. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is When receiving responses, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of responses to accept based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving responses, responses that are highly relevant will be prioritized based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving responses, the system analyzes the user's social media activity and accepts relevant responses. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the responses. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the response. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on when the responses were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the order of analysis will be adjusted based on the relevance of the responses. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is The system estimates the user's emotions and adjusts the wording of follow-up questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating follow-up questions, adjust the level of detail based on the importance of the answers. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating follow-up questions, apply different question generation algorithms depending on the category of the answer. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is The system estimates the user's emotions and adjusts the length of follow-up questions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating follow-up questions, prioritize the questions based on when the responses were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating follow-up questions, adjust the order of questions based on the relevance of the answers. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the way follow-up questions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing follow-up questions, the appropriate method of delivery is selected based on the user's past response history. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the follow-up questioning procedure based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing follow-up questions, the appropriate method of delivery will be selected based on the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing follow-up questions, multilingual follow-up questions will be provided according to the user's language settings. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts user responses, An analysis unit analyzes the responses received by the reception unit and extracts the respondents' needs, emotions, and intentions. A generation unit generates follow-up questions based on the information extracted by the analysis unit, The system includes a providing unit that provides the user with follow-up questions generated by the generation unit. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze user responses and extract information about respondents' behavior and preferences. The system according to feature 1.

3. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of response submissions based on the estimated emotions. The system according to feature 1.

4. The aforementioned reception unit is Analyze the user's past response history and select the appropriate method of receiving their response. The system according to feature 1.

5. The aforementioned reception unit is When receiving responses, filtering is performed based on the user's current situation and areas of interest. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the user's emotions and determines the priority of responses to accept based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is When receiving responses, responses that are highly relevant will be prioritized based on the user's geographical location. The system according to feature 1.

8. The aforementioned reception unit is When receiving responses, the system analyzes the user's social media activity and accepts relevant responses. The system according to feature 1.

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