system

Abstract

The system according to this embodiment aims to dynamically change the content of questions in response to user answers and to collect and analyze detailed information. [Solution] The system according to the embodiment comprises a reception unit, a question generation unit, a collection unit, an analysis unit, and a display unit. The reception unit receives the user's response. The question generation unit dynamically changes the question content based on the response received by the reception unit and asks a more detailed question. The collection unit collects the user's response based on the question generated by the question generation unit. The analysis unit classifies and analyzes the response collected by the collection unit. The display unit visually displays the analysis results obtained by the analysis unit.

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, the method including the 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 the content of hearings and questionnaires varies depending on the designer or the person conducting the hearing, and sufficient answers necessary for service improvement may not be obtained.

[0005] The system according to the embodiment aims to dynamically change the content of questions according to the user's answers and collect and analyze detailed information.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a question generation unit, a collection unit, an analysis unit, and a display unit. The reception unit receives user responses. The question generation unit dynamically modifies the question content based on the responses received by the reception unit and asks more detailed questions. The collection unit collects user responses based on the questions generated by the question generation unit. The analysis unit classifies and analyzes the responses collected by the collection unit. The display unit visually displays the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can dynamically change the content of questions in response to the user's answers and collect and analyze detailed information. [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 such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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 solution for automating VOC collection and analysis according to the embodiment of the present invention is a system for automating VOC (Voice of Customer) collection and analysis. This system consists of three functions: dynamic questionnaires, automatic classification and analysis, and an administrator dashboard. First, the dynamic questionnaire function allows an AI agent to ask follow-up questions in response to user answers and collect information. Next, the automatic classification and analysis function allows the AI ​​to automatically classify and analyze the collected data. Finally, the administrator dashboard function allows for centralized management and visual display of sales, contract numbers, and VOC. This solves the problems of inconsistency in content depending on the designer or interviewer in conventional interviews and questionnaires, and the inability to obtain sufficient answers necessary for service improvement. For example, let's explain the dynamic questionnaire function. The AI ​​agent dynamically changes the content of the questions in response to user answers and asks follow-up questions. For example, if a user answers "3" to the question "On a scale of 1 to 10, how satisfied would you be?", the AI ​​agent will ask follow-up questions such as "What aspects could have been improved?". This makes it possible to elicit customer opinions that cannot be obtained with simple questionnaires. Next, we will explain the automatic classification and analysis function. In-depth information collected through dynamic surveys is difficult to classify, but the AI ​​agent automatically aggregates the survey content and performs classification and analysis. For example, based on user responses, it can identify factors contributing to low satisfaction and extract areas for improvement. Finally, we will explain the administrator dashboard function. By centrally managing and visually displaying sales, contract numbers, and Voice of the Customer (VoC), it is possible to quickly grasp the information necessary for service improvement. For example, by displaying sales data and customer satisfaction data together, it is possible to analyze which products or services are influencing customer satisfaction. In this way, the present invention combines three functions—dynamic surveys, automatic classification and analysis, and an administrator dashboard—to automate VOC collection and analysis, making a significant contribution to service improvement. As a result, the system that automates VOC collection and analysis can efficiently collect, classify, analyze, and visually display user responses.

[0029] The system for automating VOC collection and analysis according to the embodiment comprises a reception unit, a question generation unit, a collection unit, an analysis unit, and a display unit. 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, accept responses in text format. The reception unit can also accept responses in multiple-choice format. Furthermore, the reception unit can also accept responses using voice input. For example, the reception unit allows users to input responses by voice. The question generation unit dynamically changes the question content based on the responses received by the reception unit and asks more detailed questions. The question generation unit uses, for example, an algorithm that changes the question content according to the user's response. The question generation unit can also adjust the timing of questions based on the user's response. Furthermore, the question generation unit can also adjust the depth of questions based on the user's response. For example, if a user answers "3" to the question "On a scale of 1 to 10, how satisfied were you?", the question generation unit will ask a follow-up question such as "What aspects could have been improved?" The collection unit collects user responses based on questions generated by the question generation unit. The collection unit can, for example, collect user responses in a chat format. It can also collect user responses in a survey format. Furthermore, the collection unit can collect user responses in an interview format. For example, the collection unit can collect user responses using a chatbot. The analysis unit classifies and analyzes the responses collected by the collection unit. The analysis unit can, for example, use an algorithm to automatically classify the collected responses. It can also use a method to automatically analyze the collected responses. Furthermore, the analysis unit can manually classify and analyze the collected responses. For example, the analysis unit can classify and analyze responses using natural language processing techniques. The display unit visually displays the analysis results obtained by the analysis unit. The display unit can, for example, display the analysis results using graphs or charts. It can also display the analysis results in a dashboard format. Furthermore, it can display the analysis results in a report format.For example, the display unit combines sales data and customer satisfaction data to display them. This allows the VOC collection and analysis system according to the embodiment to efficiently collect, classify, analyze, and visually display user responses.

[0030] The reception desk receives user responses. User responses may include, but are not limited to, text format or multiple-choice format. The reception desk can, for example, accept text-format responses. It can also accept multiple-choice responses. Furthermore, the reception desk can accept responses using voice input. For example, the reception desk can allow users to input responses by voice. The reception desk is designed to allow users to easily input responses through a user interface. For example, users can use web forms or mobile applications to input text, select from options, or record their responses by voice. In the case of voice input, speech recognition technology is used to convert the voice data into text data for subsequent processing. The reception desk has the ability to receive user responses in real time and immediately save them to a database. This ensures that user responses are collected reliably without being lost. The reception desk can also provide immediate feedback on user responses. For example, it can increase user engagement by displaying a confirmation message or the next question after the user submits their response. Furthermore, the reception desk implements security measures such as data encryption and access control to protect user privacy. This allows users to provide responses with confidence. The reception desk is equipped with various functions to efficiently receive user responses, improving the overall reliability of the system and the user experience.

[0031] The question generation unit dynamically modifies the question content based on the answers received by the reception unit, asking more detailed questions. For example, the question generation unit uses an algorithm that changes the question content according to the user's answers. The question generation unit can also adjust the timing of questions based on the user's answers. Furthermore, the question generation unit can adjust the depth of questions based on the user's answers. For example, if the user answers "3" to the question "On a scale of 1 to 10, how satisfied were you?", the question generation unit will ask a follow-up question such as "What aspects were lacking?". The question generation unit utilizes natural language processing technology to analyze the user's answers and generate appropriate follow-up questions. For example, if a user expresses dissatisfaction with a particular product, it automatically generates questions to explore the specific content and causes of that dissatisfaction. The question generation unit can refer to the user's answer history and past interaction data to provide questions optimized for each individual user. This improves the quality of user answers and allows for the collection of more detailed information. The question generation unit also provides real-time feedback on user answers, making it easier for users to continue answering. For example, if a user is unsure of an answer, hints and examples can be provided to facilitate their response. The question generation unit can efficiently and effectively collect information and improve overall system performance by dynamically changing the question content based on the user's answers.

[0032] The data collection unit collects user responses based on questions generated by the question generation unit. The data collection unit can collect user responses in a chat format, for example. It can also collect user responses in a survey format. Furthermore, it can collect user responses in an interview format. For example, the data collection unit can collect user responses using a chatbot. The data collection unit is designed to allow users to easily input responses through a user interface. For example, users can input text, select options, or record their responses via voice using web forms or mobile applications. In the case of voice input, speech recognition technology is used to convert the voice data into text data for subsequent processing. The data collection unit has the ability to receive user responses in real time and immediately save them to a database. This ensures that user responses are collected reliably without loss. The data collection unit can also provide immediate feedback on user responses. For example, it can increase user engagement by displaying confirmation messages or the next question after the user submits their response. Furthermore, the data collection unit implements security measures such as data encryption and access control to protect user privacy. This allows users to provide answers with confidence. The data collection unit is equipped with various functions for efficiently collecting user responses, improving the overall reliability of the system and the user experience.

[0033] The analysis department classifies and analyzes the responses collected by the collection department. The analysis department can, for example, use algorithms to automatically classify the collected responses. It can also use methods to automatically analyze the collected responses. Furthermore, the analysis department can manually classify and analyze the collected responses. For example, the analysis department can classify and analyze responses using natural language processing techniques. The analysis department analyzes the collected responses using text mining techniques to extract response trends and patterns. For example, it can extract common keywords and phrases from user responses and classify the content of the responses into categories. It can also use sentiment analysis techniques to analyze the emotions contained in user responses and identify positive, negative, and neutral emotions. Based on these analysis results, the analysis department can identify user satisfaction and dissatisfaction and propose areas for improvement. Furthermore, the analysis department statistically analyzes the collected responses and visualizes the distribution and trends of the responses. For example, it can perform frequency distribution analysis and cross-tabulation of responses to reveal differences in responses based on specific attributes or conditions. Based on these analysis results, the analysis department can understand user needs and requests and use this information to improve products and services. The analysis department possesses diverse technologies for efficiently classifying and analyzing collected responses, thereby improving the overall system performance and accuracy.

[0034] The display unit visually displays the analysis results obtained by the analysis unit. For example, the display unit displays the analysis results using graphs and charts. The display unit can also display the analysis results in a dashboard format. Furthermore, the display unit can display the analysis results in a report format. For example, the display unit can display sales data and customer satisfaction data in combination. The display unit is designed to allow users to intuitively understand the analysis results through its user interface. For example, it allows users to view the analysis results in real time using a web dashboard or mobile application. The display unit provides a variety of graphs and charts for visually displaying the analysis results. For example, it can clearly show data trends and patterns using bar graphs, line graphs, pie charts, heatmaps, etc. It also provides an interactive dashboard, allowing users to filter data and view detailed information. The display unit also has the function to output analysis results in report format. For example, it can automatically generate periodic reports and send them to stakeholders via email. The reports include summaries of the analysis results, key metrics, and recommendations, providing stakeholders with information to make quick decisions. The display unit is equipped with various functions for visually displaying analysis results, allowing users to intuitively understand the data and make quick decisions. As a result, the system for automating VOC collection and analysis according to this embodiment can efficiently collect, classify, analyze, and visually display user responses.

[0035] The question generation unit can dynamically change the content of questions in response to user answers and ask more detailed questions. For example, the question generation unit can use an algorithm that changes the content of questions in response to user answers. For example, if a user answers "3" to the question "On a scale of 1 to 10, how satisfied were you?", the question generation unit will ask a follow-up question such as "What aspects were lacking?". The question generation unit can also adjust the timing of questions based on user answers. For example, it can ask concise questions during busy times and more detailed questions during less busy times. The question generation unit can also adjust the depth of questions based on user answers. For example, if a user answers "10" to the question "On a scale of 1 to 10, how satisfied were you?", the question generation unit will ask a more detailed question such as "What aspects were particularly good?". This allows for the collection of more detailed information by dynamically changing the content of questions in response to user answers. Some or all of the above processing in the question generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the question generation unit can input the user's answer into the generation AI, which can then dynamically change the content of the question.

[0036] The analysis unit can automatically classify and analyze the collected responses. For example, the analysis unit may use an algorithm to automatically classify the collected responses. For example, the analysis unit may classify responses using natural language processing technology. The analysis unit can also use methods to automatically analyze the collected responses. For example, the analysis unit may analyze responses using machine learning algorithms. Furthermore, the analysis unit can manually classify and analyze the collected responses. For example, the analysis unit may have experts manually classify and analyze the responses. This allows for efficient data processing by automatically classifying and analyzing the collected responses. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input the collected responses into a generative AI, which can then classify and analyze the responses.

[0037] The display unit can display a combination of sales data and customer satisfaction data. For example, the display unit can visually display a combination of sales data and customer satisfaction data. For example, the display unit can display fluctuations in customer satisfaction based on sales data. The display unit can also display the impact on sales based on customer satisfaction data. For example, the display unit can analyze which products or services are influencing customer satisfaction by combining sales data and customer satisfaction data. By displaying a combination of sales data and customer satisfaction data, information necessary for service improvement can be quickly grasped. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit can input sales data and customer satisfaction data into a generation AI, and the generation AI can combine and display the data.

[0038] The collection unit can collect user responses in a chat format. The collection unit can collect user responses using, for example, a chatbot. For example, the collection unit can enable users to input their responses in a chat format. The collection unit can also collect user responses in a survey format. For example, the collection unit can enable users to input their responses in a survey format. Furthermore, the collection unit can also collect user responses in an interview format. For example, the collection unit can enable users to input their responses in an interview format. By collecting user responses in a chat format, the burden on users is reduced and the response rate is improved. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the collection unit can input user responses into a generative AI, and the generative AI can collect the responses.

[0039] The display unit can function as an administrator dashboard. For example, the display unit can provide an administrator with a dashboard that centrally manages sales data and customer satisfaction data. For example, the display unit can provide a dashboard that visually displays the information the administrator needs. The display unit can also provide a dashboard that allows the administrator to check data in real time. For example, the display unit can provide a dashboard that allows the administrator to check sales data and customer satisfaction data in real time. By functioning as an administrator dashboard, the display unit can centrally manage and visually grasp the information the administrator needs. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the display unit can input sales data and customer satisfaction data into a generation AI, which can centrally manage and visually display the data.

[0040] 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 select the most efficient reception method from the user's past response history. The reception department can also analyze the user's response patterns and prompt for a response at the optimal time. For example, the reception department analyzes the user's response patterns and prompts for a response at the optimal time. This allows the reception department to 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, for example, a generative AI, or without a generative AI. For example, the reception department can input the user's past response history into a generative AI, which can then select the optimal reception method.

[0041] The reception unit can filter responses based on the user's current situation and areas of interest. For example, the reception unit prioritizes receiving questions related to topics the user is currently interested in. For example, the reception unit selects appropriate questions according to the user's current situation. The reception unit can also filter questions based on the user's areas of interest to ensure they are relevant. For example, the reception unit filters questions based on the user's areas of interest to ensure they are relevant. This allows the reception unit to receive questions that are relevant 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, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's current situation and areas of interest into a generative AI, which can then perform the filtering.

[0042] The reception unit can prioritize receiving responses that are highly relevant, taking into account the user's geographical location information. For example, if the user is in a specific region, the reception unit will prioritize receiving questions related to that region. For example, the reception unit will select highly relevant questions based on the user's current location. The reception unit can also suggest the most appropriate questions, taking into account the user's geographical location information. For example, the reception unit will suggest the most appropriate questions, taking into account the user's geographical location information. This allows for the priority of receiving highly relevant questions by considering the user's geographical location information. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's geographical location information into a generative AI, which can then select highly relevant questions.

[0043] The reception unit can analyze the user's social media activity when receiving responses and accept relevant responses. For example, the reception unit can accept questions related to topics of interest based on the user's social media activity. For example, the reception unit can analyze the user's social media activity and suggest the most relevant questions. The reception unit can also select highly relevant questions based on the user's social media activity. For example, the reception unit selects highly relevant questions based on the user's social media activity. This allows the reception unit to accept highly relevant questions by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input the user's social media activity into a generative AI, which can then select relevant questions.

[0044] The question generation unit can adjust the level of detail of a question based on the importance of the answer during question generation. For example, the question generation unit generates a detailed question for important answers. For example, the question generation unit generates a concise question for general answers. The question generation unit can also generate a question with an appropriate level of detail for a specific answer. For example, the question generation unit generates a question with an appropriate level of detail for a specific answer. In this way, appropriate questions can be generated by adjusting the level of detail of a question based on the importance of the answer. Some or all of the above processing in the question generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the question generation unit can input the importance of the answer to the generation AI, and the generation AI can adjust the level of detail of the question.

[0045] The question generation unit can apply different question algorithms depending on the category of the answer when generating a question. For example, the question generation unit can apply a specialized question algorithm to technical answers. For example, the question generation unit can apply a standard question algorithm to general answers. The question generation unit can also apply an appropriate question algorithm to a specific category. For example, the question generation unit can apply an appropriate question algorithm to a specific category. In this way, appropriate questions can be generated by applying different question algorithms depending on the category of the answer. Some or all of the above processing in the question generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the question generation unit can input the category of the answer to the generation AI, and the generation AI can apply a question algorithm.

[0046] The question generation unit can determine the priority of questions based on the submission timing of the answers when generating them. For example, the question generation unit can prioritize questions for answers submitted early. For example, it can postpone questions for answers submitted late. The question generation unit can also generate questions with an appropriate priority according to the submission timing. For example, the question generation unit can generate questions with an appropriate priority according to the submission timing. This allows questions to be generated in the appropriate order by determining the priority of questions based on the submission timing of the answers. Some or all of the above processing in the question generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the question generation unit can input the submission timing of the answers into the generation AI, and the generation AI can determine the priority of the questions.

[0047] The question generation unit can adjust the order of questions based on the relevance of the answers when generating questions. For example, the question generation unit can prioritize generating questions for highly relevant answers. For example, the question generation unit can postpone generating questions for less relevant answers. The question generation unit can also generate questions in an appropriate order according to their relevance. For example, the question generation unit can generate questions in an appropriate order according to their relevance. In this way, by adjusting the order of questions based on the relevance of the answers, questions can be generated in an appropriate order. Some or all of the above processing in the question generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the question generation unit can input the relevance of the answers into a generation AI, and the generation AI can adjust the order of the questions.

[0048] The data collection unit can analyze the user's past response history to select an appropriate collection method when collecting responses. For example, the data collection unit may prioritize suggesting collection methods that the user has preferred to use in the past. For example, the data collection unit may select the most efficient collection method from the user's past response history. The data collection unit can also analyze the user's response patterns and collect responses at the optimal time. For example, the data collection unit analyzes the user's response patterns and collects responses at the optimal time. This allows the optimal collection method to be selected by analyzing the user's past response history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's past response history into a generative AI, which can then select the optimal collection method.

[0049] The data collection unit can customize the means of collection based on the user's current situation when collecting responses. For example, the data collection unit may prioritize collecting responses related to topics the user is currently interested in. For example, the data collection unit may select an appropriate means of collection according to the user's current situation. The data collection unit can also collect highly relevant responses based on the user's areas of interest. For example, the data collection unit may collect highly relevant responses based on the user's areas of interest. This allows for the collection of more appropriate responses by customizing the means of collection based on the user's current situation. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit may input the user's current situation into a generative AI, which can then customize the means of collection.

[0050] The data collection unit can select the optimal data collection method by considering the user's geographical location information when collecting responses. For example, if the user is in a specific region, the data collection unit will prioritize collecting responses related to that region. For example, the data collection unit will select highly relevant responses based on the user's current location. The data collection unit can also propose the optimal data collection method by considering the user's geographical location information. For example, the data collection unit will propose the optimal data collection method by considering the user's geographical location information. This allows the optimal data collection method to be selected by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI, which can then select the optimal data collection method.

[0051] The data collection unit can analyze the user's social media activity and propose a method of data collection when collecting responses. For example, the data collection unit can collect responses related to topics of interest from the user's social media activity. For example, the data collection unit analyzes the user's social media activity and proposes the optimal method of data collection. The data collection unit can also collect highly relevant responses based on the user's social media activity. For example, the data collection unit collects highly relevant responses based on the user's social media activity. This allows the optimal method of data collection to be proposed by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media activity into a generative AI, which can then propose a method of data collection.

[0052] The analysis unit can optimize its analysis algorithm based on past analysis data during analysis. For example, the analysis unit can select the optimal analysis algorithm based on past analysis data. For example, the analysis unit can adjust the analysis algorithm by referring to past analysis results. The analysis unit can also perform efficient analysis by utilizing past data. For example, the analysis unit can perform efficient analysis by utilizing past data. This allows the optimal analysis algorithm to be selected by referring to past analysis data. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input past analysis data into a generative AI, and the generative AI can optimize the analysis algorithm.

[0053] The analysis unit can apply different analytical methods to each category of responses during the analysis. For example, the analysis unit can apply specialized analytical methods to technical responses. For example, the analysis unit can apply standard analytical methods to general responses. The analysis unit can also apply appropriate analytical methods to specific categories. For example, the analysis unit can apply appropriate analytical methods to specific categories. This allows for appropriate analysis by applying different analytical methods to each category of responses. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the response categories into a generative AI, and the generative AI can apply analytical methods.

[0054] The analysis unit can weight the analysis based on the submission date of the responses. For example, the analysis unit may give higher weight to responses submitted early, and lower weight to responses submitted late. The analysis unit can also apply appropriate weighting based on the submission date. For example, the analysis unit can apply appropriate weighting based on the submission date. This allows for appropriate weighting of the analysis based on the submission date of the responses. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the submission dates of the responses into a generative AI, which can then perform the weighting of the analysis.

[0055] The analysis department can perform analysis based on relevant market data for the responses. For example, the analysis department can analyze the responses based on relevant market data. For example, the analysis department can refer to market data to evaluate the relevance of the responses. The analysis department can also utilize market data to perform efficient analysis. For example, the analysis department can utilize market data to perform efficient analysis. This allows for efficient analysis by referring to relevant market data for the responses. Some or all of the above processing in the analysis department may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis department can input relevant market data into a generative AI, and the generative AI can perform the analysis.

[0056] The display unit can select an appropriate display method by referring to the user's past operation history when displaying information. For example, the display unit may prioritize suggesting display methods that the user has previously preferred. For example, the display unit may select the most efficient display method from the user's past operation history. The display unit can also analyze the user's operation patterns and suggest the optimal display method. For example, the display unit analyzes the user's operation patterns and suggests the optimal display method. This allows the display unit to select the optimal display method by referring to the user's past operation history. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit can input the user's past operation history into a generation AI, which can then select an appropriate display method.

[0057] The display unit can combine and display sales data and customer satisfaction data at the time of display. For example, the display unit can visually display the combined sales data and customer satisfaction data. For example, the display unit can display fluctuations in customer satisfaction based on sales data. The display unit can also display the impact on sales based on customer satisfaction data. For example, the display unit can combine sales data and customer satisfaction data to analyze which products or services are influencing customer satisfaction. By displaying the combined sales data and customer satisfaction data, information necessary for service improvement can be quickly grasped. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit can input sales data and customer satisfaction data into a generation AI, and the generation AI can combine and display the data.

[0058] The display unit can select an appropriate display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. For example, if the user is using a tablet, the display unit provides a display method optimized for a large screen. The display unit can also provide a simple and highly visible display method if the user is using a smartwatch. For example, if the user is using a smartwatch, the display unit provides a simple and highly visible display method. This allows the optimal display method to be selected by taking into account the user's device information. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit can input the user's device information into a generation AI, and the generation AI can select an appropriate display method.

[0059] The display unit can function as an administrator dashboard when displaying data. For example, the display unit can provide an administrator with a dashboard for centrally managing sales data and customer satisfaction data. For example, the display unit can provide a dashboard that visually displays the information the administrator needs. The display unit can also provide a dashboard that allows the administrator to check data in real time. For example, the display unit can provide a dashboard that allows the administrator to check sales data and customer satisfaction data in real time. By functioning as an administrator dashboard, the display unit can centrally manage and visually grasp the information the administrator needs. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit can input sales data and customer satisfaction data into a generation AI, which can then centrally manage and visually display the data.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The reception department can analyze the user's past response history and select an appropriate reception method. For example, it can prioritize suggesting response methods that the user has preferred in the past. It can also select the most efficient reception method based on the user's past response history. Furthermore, it can analyze the user's response patterns and prompt them to respond at the optimal time. In this way, the optimal reception method can be selected by analyzing the user's past response history. Some or all of the above processing in the reception department may be performed using a generation AI, or it may be performed without a generation AI. For example, the reception department can input the user's past response history into a generation AI, which can then select the optimal reception method.

[0062] The question generation unit can adjust the level of detail of a question based on the importance of the answer during question generation. For example, it can generate detailed questions for important answers, and concise questions for general answers. Furthermore, it can generate questions with an appropriate level of detail for specific answers. In this way, appropriate questions can be generated by adjusting the level of detail of questions based on the importance of the answer. Some or all of the above processing in the question generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the question generation unit can input the importance of the answer to the generation AI, and the generation AI can adjust the level of detail of the question.

[0063] The data collection unit can customize the means of data collection based on the user's current situation when collecting responses. For example, it can prioritize collecting responses related to topics the user is currently interested in. It can also select appropriate data collection methods according to the user's current situation. Furthermore, it can collect highly relevant responses based on the user's areas of interest. By customizing the data collection methods based on the user's current situation, more appropriate responses can be collected. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the user's current situation into a generative AI, which can then customize the data collection methods.

[0064] The analysis unit can optimize its analysis algorithm based on past analysis data during analysis. For example, it can select the optimal analysis algorithm based on past analysis data. It can also adjust the analysis algorithm by referring to past analysis results. Furthermore, it can perform efficient analysis by utilizing past data. This allows for the selection of the optimal analysis algorithm by referring to past analysis data. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input past analysis data into a generative AI, which can then optimize the analysis algorithm.

[0065] The display unit can select an appropriate display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, it can provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible display method. In this way, the optimal display method can be selected by considering the user's device information. Some or all of the above processing in the display unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the display unit can input the user's device information into a generation AI, and the generation AI can select an appropriate display method.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk receives user responses. User responses can be in text format, multiple-choice format, or voice input. For example, users can input their responses by voice. Step 2: The question generation unit dynamically modifies the question content based on the answers received by the reception unit and asks more detailed questions. For example, an algorithm that changes the question content according to the user's answers can be used. The timing and depth of the questions can also be adjusted. Step 3: The collection unit collects user responses based on the questions generated by the question generation unit. For example, user responses can be collected in the form of a chat, survey, or interview. Step 4: The analysis unit classifies and analyzes the responses collected by the collection unit. For example, the responses can be automatically classified and analyzed using natural language processing technology. Step 5: The display unit visually displays the analysis results obtained by the analysis unit. For example, the analysis results can be displayed in the form of graphs, charts, dashboards, reports, etc.

[0068] (Example of form 2) The solution for automating VOC collection and analysis according to the embodiment of the present invention is a system for automating VOC (Voice of Customer) collection and analysis. This system consists of three functions: dynamic questionnaires, automatic classification and analysis, and an administrator dashboard. First, the dynamic questionnaire function allows an AI agent to ask follow-up questions in response to user answers and collect information. Next, the automatic classification and analysis function allows the AI ​​to automatically classify and analyze the collected data. Finally, the administrator dashboard function allows for centralized management and visual display of sales, contract numbers, and VOC. This solves the problems of inconsistency in content depending on the designer or interviewer in conventional interviews and questionnaires, and the inability to obtain sufficient answers necessary for service improvement. For example, let's explain the dynamic questionnaire function. The AI ​​agent dynamically changes the content of the questions in response to user answers and asks follow-up questions. For example, if a user answers "3" to the question "On a scale of 1 to 10, how satisfied would you be?", the AI ​​agent will ask follow-up questions such as "What aspects could have been improved?". This makes it possible to elicit customer opinions that cannot be obtained with simple questionnaires. Next, we will explain the automatic classification and analysis function. In-depth information collected through dynamic surveys is difficult to classify, but the AI ​​agent automatically aggregates the survey content and performs classification and analysis. For example, based on user responses, it can identify factors contributing to low satisfaction and extract areas for improvement. Finally, we will explain the administrator dashboard function. By centrally managing and visually displaying sales, contract numbers, and Voice of the Customer (VoC), it is possible to quickly grasp the information necessary for service improvement. For example, by displaying sales data and customer satisfaction data together, it is possible to analyze which products or services are influencing customer satisfaction. In this way, the present invention combines three functions—dynamic surveys, automatic classification and analysis, and an administrator dashboard—to automate VOC collection and analysis, making a significant contribution to service improvement. As a result, the system that automates VOC collection and analysis can efficiently collect, classify, analyze, and visually display user responses.

[0069] The system for automating VOC collection and analysis according to the embodiment comprises a reception unit, a question generation unit, a collection unit, an analysis unit, and a display unit. 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, accept responses in text format. The reception unit can also accept responses in multiple-choice format. Furthermore, the reception unit can also accept responses using voice input. For example, the reception unit allows users to input responses by voice. The question generation unit dynamically changes the question content based on the responses received by the reception unit and asks more detailed questions. The question generation unit uses, for example, an algorithm that changes the question content according to the user's response. The question generation unit can also adjust the timing of questions based on the user's response. Furthermore, the question generation unit can also adjust the depth of questions based on the user's response. For example, if a user answers "3" to the question "On a scale of 1 to 10, how satisfied were you?", the question generation unit will ask a follow-up question such as "What aspects could have been improved?" The collection unit collects user responses based on questions generated by the question generation unit. The collection unit can, for example, collect user responses in a chat format. It can also collect user responses in a survey format. Furthermore, the collection unit can collect user responses in an interview format. For example, the collection unit can collect user responses using a chatbot. The analysis unit classifies and analyzes the responses collected by the collection unit. The analysis unit can, for example, use an algorithm to automatically classify the collected responses. It can also use a method to automatically analyze the collected responses. Furthermore, the analysis unit can manually classify and analyze the collected responses. For example, the analysis unit can classify and analyze responses using natural language processing techniques. The display unit visually displays the analysis results obtained by the analysis unit. The display unit can, for example, display the analysis results using graphs or charts. It can also display the analysis results in a dashboard format. Furthermore, it can display the analysis results in a report format.For example, the display unit combines sales data and customer satisfaction data to display them. This allows the VOC collection and analysis system according to the embodiment to efficiently collect, classify, analyze, and visually display user responses.

[0070] The reception desk receives user responses. User responses may include, but are not limited to, text format or multiple-choice format. The reception desk can, for example, accept text-format responses. It can also accept multiple-choice responses. Furthermore, the reception desk can accept responses using voice input. For example, the reception desk can allow users to input responses by voice. The reception desk is designed to allow users to easily input responses through a user interface. For example, users can use web forms or mobile applications to input text, select from options, or record their responses by voice. In the case of voice input, speech recognition technology is used to convert the voice data into text data for subsequent processing. The reception desk has the ability to receive user responses in real time and immediately save them to a database. This ensures that user responses are collected reliably without being lost. The reception desk can also provide immediate feedback on user responses. For example, it can increase user engagement by displaying a confirmation message or the next question after the user submits their response. Furthermore, the reception desk implements security measures such as data encryption and access control to protect user privacy. This allows users to provide responses with confidence. The reception desk is equipped with various functions to efficiently receive user responses, improving the overall reliability of the system and the user experience.

[0071] The question generation unit dynamically modifies the question content based on the answers received by the reception unit, asking more detailed questions. For example, the question generation unit uses an algorithm that changes the question content according to the user's answers. The question generation unit can also adjust the timing of questions based on the user's answers. Furthermore, the question generation unit can adjust the depth of questions based on the user's answers. For example, if the user answers "3" to the question "On a scale of 1 to 10, how satisfied were you?", the question generation unit will ask a follow-up question such as "What aspects were lacking?". The question generation unit utilizes natural language processing technology to analyze the user's answers and generate appropriate follow-up questions. For example, if a user expresses dissatisfaction with a particular product, it automatically generates questions to explore the specific content and causes of that dissatisfaction. The question generation unit can refer to the user's answer history and past interaction data to provide questions optimized for each individual user. This improves the quality of user answers and allows for the collection of more detailed information. The question generation unit also provides real-time feedback on user answers, making it easier for users to continue answering. For example, if a user is unsure of an answer, hints and examples can be provided to facilitate their response. The question generation unit can efficiently and effectively collect information and improve overall system performance by dynamically changing the question content based on the user's answers.

[0072] The data collection unit collects user responses based on questions generated by the question generation unit. The data collection unit can collect user responses in a chat format, for example. It can also collect user responses in a survey format. Furthermore, it can collect user responses in an interview format. For example, the data collection unit can collect user responses using a chatbot. The data collection unit is designed to allow users to easily input responses through a user interface. For example, users can input text, select options, or record their responses via voice using web forms or mobile applications. In the case of voice input, speech recognition technology is used to convert the voice data into text data for subsequent processing. The data collection unit has the ability to receive user responses in real time and immediately save them to a database. This ensures that user responses are collected reliably without loss. The data collection unit can also provide immediate feedback on user responses. For example, it can increase user engagement by displaying confirmation messages or the next question after the user submits their response. Furthermore, the data collection unit implements security measures such as data encryption and access control to protect user privacy. This allows users to provide answers with confidence. The data collection unit is equipped with various functions for efficiently collecting user responses, improving the overall reliability of the system and the user experience.

[0073] The analysis department classifies and analyzes the responses collected by the collection department. The analysis department can, for example, use algorithms to automatically classify the collected responses. It can also use methods to automatically analyze the collected responses. Furthermore, the analysis department can manually classify and analyze the collected responses. For example, the analysis department can classify and analyze responses using natural language processing techniques. The analysis department analyzes the collected responses using text mining techniques to extract response trends and patterns. For example, it can extract common keywords and phrases from user responses and classify the content of the responses into categories. It can also use sentiment analysis techniques to analyze the emotions contained in user responses and identify positive, negative, and neutral emotions. Based on these analysis results, the analysis department can identify user satisfaction and dissatisfaction and propose areas for improvement. Furthermore, the analysis department statistically analyzes the collected responses and visualizes the distribution and trends of the responses. For example, it can perform frequency distribution analysis and cross-tabulation of responses to reveal differences in responses based on specific attributes or conditions. Based on these analysis results, the analysis department can understand user needs and requests and use this information to improve products and services. The analysis department possesses diverse technologies for efficiently classifying and analyzing collected responses, thereby improving the overall system performance and accuracy.

[0074] The display unit visually displays the analysis results obtained by the analysis unit. For example, the display unit displays the analysis results using graphs and charts. The display unit can also display the analysis results in a dashboard format. Furthermore, the display unit can display the analysis results in a report format. For example, the display unit can display sales data and customer satisfaction data in combination. The display unit is designed to allow users to intuitively understand the analysis results through its user interface. For example, it allows users to view the analysis results in real time using a web dashboard or mobile application. The display unit provides a variety of graphs and charts for visually displaying the analysis results. For example, it can clearly show data trends and patterns using bar graphs, line graphs, pie charts, heatmaps, etc. It also provides an interactive dashboard, allowing users to filter data and view detailed information. The display unit also has the function to output analysis results in report format. For example, it can automatically generate periodic reports and send them to stakeholders via email. The reports include summaries of the analysis results, key metrics, and recommendations, providing stakeholders with information to make quick decisions. The display unit is equipped with various functions for visually displaying analysis results, allowing users to intuitively understand the data and make quick decisions. As a result, the system for automating VOC collection and analysis according to this embodiment can efficiently collect, classify, analyze, and visually display user responses.

[0075] The question generation unit can dynamically change the content of questions in response to user answers and ask more detailed questions. For example, the question generation unit can use an algorithm that changes the content of questions in response to user answers. For example, if a user answers "3" to the question "On a scale of 1 to 10, how satisfied were you?", the question generation unit will ask a follow-up question such as "What aspects were lacking?". The question generation unit can also adjust the timing of questions based on user answers. For example, it can ask concise questions during busy times and more detailed questions during less busy times. The question generation unit can also adjust the depth of questions based on user answers. For example, if a user answers "10" to the question "On a scale of 1 to 10, how satisfied were you?", the question generation unit will ask a more detailed question such as "What aspects were particularly good?". This allows for the collection of more detailed information by dynamically changing the content of questions in response to user answers. Some or all of the above processing in the question generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the question generation unit can input the user's answer into the generation AI, which can then dynamically change the content of the question.

[0076] The analysis unit can automatically classify and analyze the collected responses. For example, the analysis unit may use an algorithm to automatically classify the collected responses. For example, the analysis unit may classify responses using natural language processing technology. The analysis unit can also use methods to automatically analyze the collected responses. For example, the analysis unit may analyze responses using machine learning algorithms. Furthermore, the analysis unit can manually classify and analyze the collected responses. For example, the analysis unit may have experts manually classify and analyze the responses. This allows for efficient data processing by automatically classifying and analyzing the collected responses. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input the collected responses into a generative AI, which can then classify and analyze the responses.

[0077] The display unit can display a combination of sales data and customer satisfaction data. For example, the display unit can visually display a combination of sales data and customer satisfaction data. For example, the display unit can display fluctuations in customer satisfaction based on sales data. The display unit can also display the impact on sales based on customer satisfaction data. For example, the display unit can analyze which products or services are influencing customer satisfaction by combining sales data and customer satisfaction data. By displaying a combination of sales data and customer satisfaction data, information necessary for service improvement can be quickly grasped. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit can input sales data and customer satisfaction data into a generation AI, and the generation AI can combine and display the data.

[0078] The collection unit can collect user responses in a chat format. The collection unit can collect user responses using, for example, a chatbot. For example, the collection unit can enable users to input their responses in a chat format. The collection unit can also collect user responses in a survey format. For example, the collection unit can enable users to input their responses in a survey format. Furthermore, the collection unit can also collect user responses in an interview format. For example, the collection unit can enable users to input their responses in an interview format. By collecting user responses in a chat format, the burden on users is reduced and the response rate is improved. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the collection unit can input user responses into a generative AI, and the generative AI can collect the responses.

[0079] The display unit can function as an administrator dashboard. For example, the display unit can provide an administrator with a dashboard that centrally manages sales data and customer satisfaction data. For example, the display unit can provide a dashboard that visually displays the information the administrator needs. The display unit can also provide a dashboard that allows the administrator to check data in real time. For example, the display unit can provide a dashboard that allows the administrator to check sales data and customer satisfaction data in real time. By functioning as an administrator dashboard, the display unit can centrally manage and visually grasp the information the administrator needs. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the display unit can input sales data and customer satisfaction data into a generation AI, which can centrally manage and visually display the data.

[0080] The reception unit can estimate the user's emotions and adjust the timing of response requests based on the estimated emotions. For example, if the user is stressed, the reception unit may prompt for a response at a time when the user can relax. For example, if the user is relaxed, the reception unit may request a response immediately. The reception unit can also prompt for a response after a break if the user is tired. For example, if the reception unit is tired, it may request a response after a break. By adjusting the timing of response requests according to the user's emotions, responses can be prompted 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 a generative AI, or not using a generative AI. For example, the reception unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the timing of response requests.

[0081] 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 select the most efficient reception method from the user's past response history. The reception department can also analyze the user's response patterns and prompt for a response at the optimal time. For example, the reception department analyzes the user's response patterns and prompts for a response at the optimal time. This allows the reception department to 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, for example, a generative AI, or without a generative AI. For example, the reception department can input the user's past response history into a generative AI, which can then select the optimal reception method.

[0082] The reception unit can filter responses based on the user's current situation and areas of interest. For example, the reception unit prioritizes receiving questions related to topics the user is currently interested in. For example, the reception unit selects appropriate questions according to the user's current situation. The reception unit can also filter questions based on the user's areas of interest to ensure they are relevant. For example, the reception unit filters questions based on the user's areas of interest to ensure they are relevant. This allows the reception unit to receive questions that are relevant 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, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's current situation and areas of interest into a generative AI, which can then perform the filtering.

[0083] 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. Also, if the user is tired, the reception desk may prioritize simple questions. For example, if the user is tired, the reception desk will prioritize simple questions. This allows important questions to be prioritized by determining the priority of answers to be received 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 a generative AI, or not using a generative AI. For example, the reception desk can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of the answers.

[0084] The reception unit can prioritize receiving responses that are highly relevant, taking into account the user's geographical location information. For example, if the user is in a specific region, the reception unit will prioritize receiving questions related to that region. For example, the reception unit will select highly relevant questions based on the user's current location. The reception unit can also suggest the most appropriate questions, taking into account the user's geographical location information. For example, the reception unit will suggest the most appropriate questions, taking into account the user's geographical location information. This allows for the priority of receiving highly relevant questions by considering the user's geographical location information. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's geographical location information into a generative AI, which can then select highly relevant questions.

[0085] The reception unit can analyze the user's social media activity when receiving responses and accept relevant responses. For example, the reception unit can accept questions related to topics of interest based on the user's social media activity. For example, the reception unit can analyze the user's social media activity and suggest the most relevant questions. The reception unit can also select highly relevant questions based on the user's social media activity. For example, the reception unit selects highly relevant questions based on the user's social media activity. This allows the reception unit to accept highly relevant questions by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input the user's social media activity into a generative AI, which can then select relevant questions.

[0086] The question generation unit can estimate the user's emotions and adjust the wording of the question based on the estimated emotions. For example, if the user is nervous, the question generation unit can ask the question in a gentle manner. For example, if the user is relaxed, the question generation unit can ask the question in a detailed manner. Also, if the user is excited, the question generation unit can ask the question in a concise manner. For example, if the user is excited, the question generation unit can ask the question in a concise manner. In this way, by adjusting the wording of the question according to the user's emotions, more appropriate questions can be asked. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the question generation unit may be performed using a generative AI, or not using a generative AI. For example, the question generation unit can input user emotion data into a generative AI, and the generative AI can adjust the wording of the question.

[0087] The question generation unit can adjust the level of detail of a question based on the importance of the answer during question generation. For example, the question generation unit generates a detailed question for important answers. For example, the question generation unit generates a concise question for general answers. The question generation unit can also generate a question with an appropriate level of detail for a specific answer. For example, the question generation unit generates a question with an appropriate level of detail for a specific answer. In this way, appropriate questions can be generated by adjusting the level of detail of a question based on the importance of the answer. Some or all of the above processing in the question generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the question generation unit can input the importance of the answer to the generation AI, and the generation AI can adjust the level of detail of the question.

[0088] The question generation unit can apply different question algorithms depending on the category of the answer when generating a question. For example, the question generation unit can apply a specialized question algorithm to technical answers. For example, the question generation unit can apply a standard question algorithm to general answers. The question generation unit can also apply an appropriate question algorithm to a specific category. For example, the question generation unit can apply an appropriate question algorithm to a specific category. In this way, appropriate questions can be generated by applying different question algorithms depending on the category of the answer. Some or all of the above processing in the question generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the question generation unit can input the category of the answer to the generation AI, and the generation AI can apply a question algorithm.

[0089] The question generation unit can estimate the user's emotions and adjust the length of the question based on the estimated emotions. For example, if the user is in a hurry, the question generation unit will generate a short question. For example, if the user is relaxed, the question generation unit will generate a longer question. The question generation unit can also generate a concise question if the user is excited. For example, if the question generation unit is excited, the question generation unit will generate a concise question. By adjusting the length of the question according to the user's emotions, more appropriate questions can be asked. 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 question generation unit may be performed using a generative AI, or not using a generative AI. For example, the question generation unit can input user emotion data into a generative AI, which can then adjust the length of the question.

[0090] The question generation unit can determine the priority of questions based on the submission timing of the answers when generating them. For example, the question generation unit can prioritize questions for answers submitted early. For example, it can postpone questions for answers submitted late. The question generation unit can also generate questions with an appropriate priority according to the submission timing. For example, the question generation unit can generate questions with an appropriate priority according to the submission timing. This allows questions to be generated in the appropriate order by determining the priority of questions based on the submission timing of the answers. Some or all of the above processing in the question generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the question generation unit can input the submission timing of the answers into the generation AI, and the generation AI can determine the priority of the questions.

[0091] The question generation unit can adjust the order of questions based on the relevance of the answers when generating questions. For example, the question generation unit can prioritize generating questions for highly relevant answers. For example, the question generation unit can postpone generating questions for less relevant answers. The question generation unit can also generate questions in an appropriate order according to their relevance. For example, the question generation unit can generate questions in an appropriate order according to their relevance. In this way, by adjusting the order of questions based on the relevance of the answers, questions can be generated in an appropriate order. Some or all of the above processing in the question generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the question generation unit can input the relevance of the answers into a generation AI, and the generation AI can adjust the order of the questions.

[0092] The data collection unit can estimate the user's emotions and adjust the method of collecting responses based on the estimated emotions. For example, if the user is relaxed, the data collection unit may request detailed responses. For example, if the user is in a hurry, the data collection unit may request concise responses. The data collection unit may also request visually stimulating responses if the user is excited. For example, if the data collection unit is excited, the data collection unit may request visually stimulating responses. By adjusting the method of collecting responses according to the user's emotions, responses can be collected in a more appropriate manner. 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 data collection unit may be performed using a generative AI, or not using a generative AI. For example, the data collection unit can input the user's emotion data into a generative AI, which can then adjust the method of collecting responses.

[0093] The data collection unit can analyze the user's past response history to select an appropriate collection method when collecting responses. For example, the data collection unit may prioritize suggesting collection methods that the user has preferred to use in the past. For example, the data collection unit may select the most efficient collection method from the user's past response history. The data collection unit can also analyze the user's response patterns and collect responses at the optimal time. For example, the data collection unit analyzes the user's response patterns and collects responses at the optimal time. This allows the optimal collection method to be selected by analyzing the user's past response history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's past response history into a generative AI, which can then select the optimal collection method.

[0094] The data collection unit can customize the means of collection based on the user's current situation when collecting responses. For example, the data collection unit may prioritize collecting responses related to topics the user is currently interested in. For example, the data collection unit may select an appropriate means of collection according to the user's current situation. The data collection unit can also collect highly relevant responses based on the user's areas of interest. For example, the data collection unit may collect highly relevant responses based on the user's areas of interest. This allows for the collection of more appropriate responses by customizing the means of collection based on the user's current situation. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit may input the user's current situation into a generative AI, which can then customize the means of collection.

[0095] The data collection unit can estimate the user's emotions and determine the priority of the answers to collect based on the estimated emotions. For example, if the user is excited, the data collection unit will prioritize collecting important answers. For example, if the user is relaxed, the data collection unit will prioritize collecting detailed answers. The data collection unit can also prioritize collecting simple answers if the user is tired. For example, if the user is tired, the data collection unit will prioritize collecting simple answers. This allows for the priority of collecting important answers by determining the priority of the answers to collect 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 data collection unit may be performed using a generative AI, or not using a generative AI. For example, the data collection unit can input the user's emotion data into a generative AI, which can then determine the priority of the answers.

[0096] The data collection unit can select the optimal data collection method by considering the user's geographical location information when collecting responses. For example, if the user is in a specific region, the data collection unit will prioritize collecting responses related to that region. For example, the data collection unit will select highly relevant responses based on the user's current location. The data collection unit can also propose the optimal data collection method by considering the user's geographical location information. For example, the data collection unit will propose the optimal data collection method by considering the user's geographical location information. This allows the optimal data collection method to be selected by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI, which can then select the optimal data collection method.

[0097] The data collection unit can analyze the user's social media activity and propose a method of data collection when collecting responses. For example, the data collection unit can collect responses related to topics of interest from the user's social media activity. For example, the data collection unit analyzes the user's social media activity and proposes the optimal method of data collection. The data collection unit can also collect highly relevant responses based on the user's social media activity. For example, the data collection unit collects highly relevant responses based on the user's social media activity. This allows the optimal method of data collection to be proposed by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media activity into a generative AI, which can then propose a method of data collection.

[0098] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is relaxed, the analysis unit performs a detailed analysis. For example, if the user is in a hurry, the analysis unit performs a concise analysis. The analysis unit can also perform a visually stimulating analysis if the user is excited. For example, if the analysis unit is excited, the analysis unit performs a visually stimulating analysis. This allows for a more appropriate analysis by adjusting the analysis method 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 analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, which can then adjust the analysis method.

[0099] The analysis unit can optimize its analysis algorithm based on past analysis data during analysis. For example, the analysis unit can select the optimal analysis algorithm based on past analysis data. For example, the analysis unit can adjust the analysis algorithm by referring to past analysis results. The analysis unit can also perform efficient analysis by utilizing past data. For example, the analysis unit can perform efficient analysis by utilizing past data. This allows the optimal analysis algorithm to be selected by referring to past analysis data. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input past analysis data into a generative AI, and the generative AI can optimize the analysis algorithm.

[0100] The analysis unit can apply different analytical methods to each category of responses during the analysis. For example, the analysis unit can apply specialized analytical methods to technical responses. For example, the analysis unit can apply standard analytical methods to general responses. The analysis unit can also apply appropriate analytical methods to specific categories. For example, the analysis unit can apply appropriate analytical methods to specific categories. This allows for appropriate analysis by applying different analytical methods to each category of responses. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the response categories into a generative AI, and the generative AI can apply analytical methods.

[0101] The analysis unit can estimate the user's emotions and determine the priority of the analysis based on the estimated emotions. For example, if the user is excited, the analysis unit will prioritize important analyses. For example, if the user is relaxed, the analysis unit will prioritize detailed analyses. The analysis unit can also prioritize simple analyses if the user is tired. For example, if the user is tired, the analysis unit will prioritize simple analyses. This allows important analyses to be prioritized by determining the priority of the analysis 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 analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, which can then determine the priority of the analysis.

[0102] The analysis unit can weight the analysis based on the submission date of the responses. For example, the analysis unit may give higher weight to responses submitted early, and lower weight to responses submitted late. The analysis unit can also apply appropriate weighting based on the submission date. For example, the analysis unit can apply appropriate weighting based on the submission date. This allows for appropriate weighting of the analysis based on the submission date of the responses. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the submission dates of the responses into a generative AI, which can then perform the weighting of the analysis.

[0103] The analysis department can perform analysis based on relevant market data for the responses. For example, the analysis department can analyze the responses based on relevant market data. For example, the analysis department can refer to market data to evaluate the relevance of the responses. The analysis department can also utilize market data to perform efficient analysis. For example, the analysis department can utilize market data to perform efficient analysis. This allows for efficient analysis by referring to relevant market data for the responses. Some or all of the above processing in the analysis department may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis department can input relevant market data into a generative AI, and the generative AI can perform the analysis.

[0104] The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, if the user is tense, the display unit provides a simple and highly visible display method. For example, if the user is relaxed, the display unit provides a display method that includes detailed information. The display unit can also provide a concise display method if the user is in a hurry. For example, if the display unit provides a concise display method if the user is in a hurry, the display unit provides a concise display method. This allows for more appropriate display by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 display unit may be performed using a generative AI, or not using a generative AI. For example, the display unit can input user emotion data into a generative AI, which can then adjust the display method.

[0105] The display unit can select an appropriate display method by referring to the user's past operation history when displaying information. For example, the display unit may prioritize suggesting display methods that the user has previously preferred. For example, the display unit may select the most efficient display method from the user's past operation history. The display unit can also analyze the user's operation patterns and suggest the optimal display method. For example, the display unit analyzes the user's operation patterns and suggests the optimal display method. This allows the display unit to select the optimal display method by referring to the user's past operation history. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit can input the user's past operation history into a generation AI, which can then select an appropriate display method.

[0106] The display unit can combine and display sales data and customer satisfaction data at the time of display. For example, the display unit can visually display the combined sales data and customer satisfaction data. For example, the display unit can display fluctuations in customer satisfaction based on sales data. The display unit can also display the impact on sales based on customer satisfaction data. For example, the display unit can combine sales data and customer satisfaction data to analyze which products or services are influencing customer satisfaction. By displaying the combined sales data and customer satisfaction data, information necessary for service improvement can be quickly grasped. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit can input sales data and customer satisfaction data into a generation AI, and the generation AI can combine and display the data.

[0107] The display unit can estimate the user's emotions and determine the display priority based on the estimated emotions. For example, if the user is excited, the display unit will prioritize displaying important information. For example, if the user is relaxed, the display unit will prioritize displaying detailed information. The display unit can also prioritize displaying simple information if the user is tired. For example, if the user is tired, the display unit will prioritize displaying simple information. In this way, by determining the display priority according to the user's emotions, important information can be displayed preferentially. 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 display unit may be performed using a generative AI, or not using a generative AI. For example, the display unit can input user emotion data into a generative AI, and the generative AI can determine the display priority.

[0108] The display unit can select an appropriate display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. For example, if the user is using a tablet, the display unit provides a display method optimized for a large screen. The display unit can also provide a simple and highly visible display method if the user is using a smartwatch. For example, if the user is using a smartwatch, the display unit provides a simple and highly visible display method. This allows the optimal display method to be selected by taking into account the user's device information. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit can input the user's device information into a generation AI, and the generation AI can select an appropriate display method.

[0109] The display unit can function as an administrator dashboard when displaying data. For example, the display unit can provide an administrator with a dashboard for centrally managing sales data and customer satisfaction data. For example, the display unit can provide a dashboard that visually displays the information the administrator needs. The display unit can also provide a dashboard that allows the administrator to check data in real time. For example, the display unit can provide a dashboard that allows the administrator to check sales data and customer satisfaction data in real time. By functioning as an administrator dashboard, the display unit can centrally manage and visually grasp the information the administrator needs. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit can input sales data and customer satisfaction data into a generation AI, which can then centrally manage and visually display the data.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The reception unit can estimate the user's emotions and adjust the timing of response requests based on the estimated emotions. For example, if the user is stressed, the reception unit can prompt them to respond at a time when they can relax. If the user is relaxed, the reception unit can request a response immediately. Furthermore, if the user is tired, the reception unit can prompt them to respond after a break. By adjusting the timing of response requests according to the user's emotions, responses can be prompted at a more appropriate time. Emotion estimation is achieved 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 reception unit may be performed using generative AI or not. For example, the reception unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the timing of response requests.

[0112] The question generation unit can estimate the user's emotions and adjust the wording of the question based on the estimated emotions. For example, if the user is nervous, the question can be phrased gently. If the user is relaxed, the question can be phrased in detail. Furthermore, if the user is excited, the question can be phrased concisely. In this way, by adjusting the wording of the question according to the user's emotions, more appropriate questions can be asked. Emotion estimation is achieved using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the question generation unit may be performed using a generative AI or not. For example, the question generation unit can input user emotion data into a generative AI, and the generative AI can adjust the wording of the question.

[0113] The data collection unit can estimate the user's emotions and adjust the method of collecting responses based on the estimated emotions. For example, if the user is relaxed, detailed responses may be requested. If the user is in a hurry, concise responses may be requested. Furthermore, if the user is excited, visually stimulating responses may be requested. By adjusting the method of collecting responses according to the user's emotions, responses can be collected in a more appropriate manner. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input user emotion data into a generative AI, which can then adjust the method of collecting responses.

[0114] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is relaxed, a detailed analysis can be performed. If the user is in a hurry, a concise analysis can be performed. Furthermore, if the user is excited, a visually stimulating analysis can be performed. In this way, by adjusting the analysis method according to the user's emotions, a more appropriate analysis can be performed. Emotion estimation is achieved using an emotion engine or generative AI, etc. 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 generative AI or not. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can adjust the analysis method.

[0115] The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method containing detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. In this way, by adjusting the display method according to the user's emotions, a more appropriate display can be provided. Emotion estimation is achieved 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 display unit may be performed using a generative AI or not. For example, the display unit can input user emotion data into a generative AI, and the generative AI can adjust the display method.

[0116] The reception department can analyze the user's past response history and select an appropriate reception method. For example, it can prioritize suggesting response methods that the user has preferred in the past. It can also select the most efficient reception method based on the user's past response history. Furthermore, it can analyze the user's response patterns and prompt them to respond at the optimal time. In this way, the optimal reception method can be selected by analyzing the user's past response history. Some or all of the above processing in the reception department may be performed using a generation AI, or it may be performed without a generation AI. For example, the reception department can input the user's past response history into a generation AI, which can then select the optimal reception method.

[0117] The question generation unit can adjust the level of detail of a question based on the importance of the answer during question generation. For example, it can generate detailed questions for important answers, and concise questions for general answers. Furthermore, it can generate questions with an appropriate level of detail for specific answers. In this way, appropriate questions can be generated by adjusting the level of detail of questions based on the importance of the answer. Some or all of the above processing in the question generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the question generation unit can input the importance of the answer to the generation AI, and the generation AI can adjust the level of detail of the question.

[0118] The data collection unit can customize the means of data collection based on the user's current situation when collecting responses. For example, it can prioritize collecting responses related to topics the user is currently interested in. It can also select appropriate data collection methods according to the user's current situation. Furthermore, it can collect highly relevant responses based on the user's areas of interest. By customizing the data collection methods based on the user's current situation, more appropriate responses can be collected. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the user's current situation into a generative AI, which can then customize the data collection methods.

[0119] The analysis unit can optimize its analysis algorithm based on past analysis data during analysis. For example, it can select the optimal analysis algorithm based on past analysis data. It can also adjust the analysis algorithm by referring to past analysis results. Furthermore, it can perform efficient analysis by utilizing past data. This allows for the selection of the optimal analysis algorithm by referring to past analysis data. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input past analysis data into a generative AI, which can then optimize the analysis algorithm.

[0120] The display unit can select an appropriate display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, it can provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible display method. In this way, the optimal display method can be selected by considering the user's device information. Some or all of the above processing in the display unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the display unit can input the user's device information into a generation AI, and the generation AI can select an appropriate display method.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The reception desk receives user responses. User responses can be in text format, multiple-choice format, or voice input. For example, users can input their responses by voice. Step 2: The question generation unit dynamically modifies the question content based on the answers received by the reception unit and asks more detailed questions. For example, an algorithm that changes the question content according to the user's answers can be used. The timing and depth of the questions can also be adjusted. Step 3: The collection unit collects user responses based on the questions generated by the question generation unit. For example, user responses can be collected in the form of a chat, survey, or interview. Step 4: The analysis unit classifies and analyzes the responses collected by the collection unit. For example, the responses can be automatically classified and analyzed using natural language processing technology. Step 5: The display unit visually displays the analysis results obtained by the analysis unit. For example, the analysis results can be displayed in the form of graphs, charts, dashboards, reports, etc.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the reception unit, question generation unit, collection unit, analysis unit, and display 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 reception device 38 of the smart device 14 and receives the user's response. The question generation unit is implemented by the identification processing unit 290 of the data processing device 12 and dynamically changes the question content based on the user's response. The collection unit is implemented by the control unit 46A of the smart device 14 and collects the user's response based on the question generated by the question generation unit. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12 and classifies and analyzes the collected responses. The display unit is implemented by the output device 40 of the smart device 14 and visually displays the analysis results. 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.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the reception unit, question generation unit, collection unit, analysis unit, and display 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 microphone 238 of the smart glasses 214 and receives the user's response. The question generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and dynamically changes the question content based on the user's response. The collection unit is implemented by the control unit 46A of the smart glasses 214 and collects the user's response based on the question generated by the question generation unit. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and classifies and analyzes the collected responses. The display unit is implemented by the speaker 240 of the smart glasses 214 and visually displays the analysis results. 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.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the reception unit, question generation unit, collection unit, analysis unit, and display 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 microphone 238 of the headset terminal 314 and receives the user's response. The question generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and dynamically changes the question content based on the user's response. The collection unit is implemented by the control unit 46A of the headset terminal 314 and collects the user's response based on the question generated by the question generation unit. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and classifies and analyzes the collected responses. The display unit is implemented by the display 343 of the headset terminal 314 and visually displays the analysis results. 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.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the reception unit, question generation unit, collection unit, analysis unit, and display unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives the user's response. The question generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and dynamically changes the question content based on the user's response. The collection unit is implemented by the control unit 46A of the robot 414 and collects the user's response based on the question generated by the question generation unit. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and classifies and analyzes the collected responses. The display unit is implemented by the speaker 240 of the robot 414 and visually displays the analysis results. 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A reception desk that accepts user responses, A question generation unit dynamically modifies the question content based on the response received by the reception unit and asks more detailed questions. A collection unit collects user responses based on questions generated by the aforementioned question generation unit, An analysis unit that classifies and analyzes the responses collected by the collection unit, The system includes a display unit that visually displays the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned question generation unit, The questions are dynamically changed based on the user's responses, and more detailed questions are asked. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The collected responses are automatically categorized and analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned display unit is Display a combination of sales data and customer satisfaction data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Collect user responses in a chat format. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned display unit is Functions as an administrator dashboard The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of response requests based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) 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 9) 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 10) 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 11) The aforementioned reception unit is When receiving responses, responses that are highly relevant based on the user's geographical location will be given priority. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving responses, relevant responses will be accepted based on the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned question generation unit, The system estimates the user's emotions and adjusts the wording of questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned question generation unit, When generating 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 15) The aforementioned question generation unit, When generating questions, different question algorithms are applied depending on the category of the answer. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned question generation unit, The system estimates the user's emotions and adjusts the length of the questions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned question generation unit, When generating questions, prioritize them based on when the answers are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned question generation unit, When generating questions, the order of questions is adjusted based on the relevance of the answers. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned collection unit is We estimate the user's emotions and adjust the method of collecting responses based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned collection unit is When collecting responses, the system analyzes the user's past response history to select the appropriate collection method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned collection unit is When collecting responses, customize the collection method based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned collection unit is It estimates the user's emotions and determines the priority of the responses to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned collection unit is When collecting responses, the appropriate collection method is selected based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned collection unit is When collecting responses, we propose collection methods based on users' social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit is During analysis, the analysis algorithm is optimized based on past analysis data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit is During the analysis, different analytical methods are applied to each category of responses. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit is We estimate the user's emotions and prioritize the analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned analysis unit is During the analysis, the analysis will be weighted based on when the responses were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned analysis unit is During the analysis, the analysis will be based on relevant market data for the responses. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned display unit is It estimates the user's emotions and adjusts the display method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned display unit is When displaying information, the system selects the appropriate display method based on the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned display unit is When displaying data, sales data and customer satisfaction data are combined and displayed. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned display unit is It estimates the user's emotions and determines the display priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned display unit is When displaying content, the system selects the appropriate display method based on the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned display unit is When displayed, it functions as an administrator dashboard. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 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, A question generation unit dynamically modifies the question content based on the response received by the reception unit and asks more detailed questions. A collection unit collects user responses based on questions generated by the aforementioned question generation unit, An analysis unit that classifies and analyzes the responses collected by the collection unit, The system includes a display unit that visually displays the analysis results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned analysis unit is The collected responses are automatically classified and analyzed. The system according to feature 1.

3. The aforementioned display unit is Display a combination of sales data and customer satisfaction data. The system according to feature 1.

4. The aforementioned collection unit is Collect user responses in a chat format. The system according to feature 1.

5. The aforementioned display unit is Functions as an administrator dashboard The system according to feature 1.

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

7. 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.

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