system

Abstract

The system according to this embodiment aims to uncover users' latent needs and contribute to improving the quality of services. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, an analysis unit, and a proposal unit. The collection unit collects user attribute information. The generation unit generates questions based on the information collected by the collection unit. The analysis unit analyzes the answers obtained from the user based on the questions generated by the generation unit. The proposal unit makes specific improvement suggestions based on 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: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to appropriately elicit the potential needs of users and obtain data contributing to the improvement of service quality.

[0005] The system according to the embodiment aims to elicit the potential needs of users and contribute to the improvement of service quality.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a generation unit, an analysis unit, and a proposal unit. The collection unit collects user attribute information. The generation unit generates questions based on the information collected by the collection unit. The analysis unit analyzes the answers obtained from the user based on the questions generated by the generation unit. The proposal unit makes specific improvement suggestions based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can uncover users' latent needs and contribute to improving the quality of services. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages 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, 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 system according to the embodiment of the present invention is a system that extracts essential latent needs between service providers and users and contributes to improving service quality based on objective data. This system is designed to solve the problems of current surveys and needs assessments. For example, there are problems such as service providers not asking users appropriate questions and service providers not understanding the significance of the answers obtained from users. To solve these problems, this system consists of the following steps. First, user attribute information (age, gender, occupation, etc.) is collected. Next, based on the collected attribute information, a generating AI generates appropriate questions for the user. The generated questions are from the user's perspective and can extract the opinions that the user truly needs. Next, the generating AI analyzes the answers obtained from the user. The generating AI understands the nuances and context of the answers and accurately grasps the user's intentions. This allows service providers to correctly analyze and reflect user opinions. Finally, based on the analysis results, specific improvement suggestions are made to the service provider. This contributes to improving service quality. For example, if a user responds with "I want a more user-friendly interface," the generating AI accurately grasps that intent and suggests specific improvements (such as changing the placement or color of buttons). In this way, the system extracts users' latent needs and provides resources to contribute to improving service quality. As a result, the system can extract the essential latent needs between service providers and users and contribute to improving service quality based on objective data.

[0029] The service quality improvement system according to the embodiment comprises a collection unit, a generation unit, an analysis unit, and a proposal unit. The collection unit collects user attribute information. User attribute information includes, but is not limited to, age, gender, and occupation. For example, to collect the user's age, the collection unit calculates the user's age based on the date of birth entered by the user. The collection unit can also obtain gender information selected by the user to collect the user's gender. Furthermore, the collection unit can also obtain occupation information entered by the user to collect the user's occupation. For example, the collection unit stores the information entered by the user in a database for later analysis. The generation unit generates questions based on the information collected by the collection unit using a generation AI. The generated questions will differ depending on, for example, the user's age and gender, but is not limited to these examples. For example, if the user is a male in his 20s, the generation unit will generate questions suitable for a male in his 20s. The generation unit can also generate questions suitable for a female in her 30s if the user is a female in her 30s. Furthermore, the generation unit can adjust the content of the questions according to the user's occupation. For example, the generation unit generates questions suitable for a student if the user is a student. The analysis unit uses generational AI to analyze the answers obtained from the user based on the questions generated by the generation unit. Analysis is performed to understand the nuances and context of the answers, but is not limited to such examples. For example, the analysis unit uses natural language processing technology to analyze the content of the user's answers and accurately grasp the user's intent. The analysis unit can also analyze the emotional nuances of the answers. For example, the analysis unit calculates an emotional score from the content of the user's answers to understand the user's emotions. The suggestion unit uses generational AI to make specific improvement suggestions based on the analysis results obtained by the analysis unit. Suggestions can be, for example, suggestions for improvements to the service interface, but are not limited to such examples. For example, if the user answers, "I want a more user-friendly interface," the suggestion unit will suggest specific improvements such as changing the placement or color of buttons. The suggestion unit can also suggest adding or changing the functions of the service.For example, if the user responds that they "want a new feature," the proposal department will propose specific details of that feature. In this way, the service quality improvement system according to the embodiment can contribute to improving service quality by generating appropriate questions based on the user's attribute information, analyzing the responses, and making specific improvement suggestions.

[0030] The data collection unit collects user attribute information. This attribute information includes, but is not limited to, age, gender, and occupation. For example, to collect a user's age, the unit calculates the age based on the date of birth entered by the user. Specifically, it stores the date of birth entered by the user during registration in a database and uses an algorithm to calculate the current age based on that data. The data collection unit can also obtain gender information selected by the user to collect their gender. Gender information is entered by the user through a selection of options and stored in the database. Furthermore, the data collection unit can obtain occupation information entered by the user to collect their occupation. Occupation information is entered by the user through free-form input or by selecting from pre-defined options. For example, the data collection unit stores the information entered by the user in a database for later analysis. The data collection unit centrally manages this information and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible by the generation and analysis units. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The generation unit uses a generation AI to generate questions based on information collected by the collection unit. The generated questions will differ depending on, for example, the user's age and gender, but are not limited to these examples. Specifically, the generation AI takes user attribute information as input and uses an algorithm to generate the most appropriate questions based on that information. For example, if the user is a man in his 20s, the generation unit will generate questions suitable for a man in his 20s. The generation AI selects topics and question formats that are likely to interest men in their 20s based on past data and statistical information. The generation unit can also generate questions suitable for a woman in her 30s if the user is a woman in her 30s. The generation AI selects topics and question formats that are likely to interest women in their 30s and generates appropriate questions. Furthermore, the generation unit can adjust the content of the questions according to the user's occupation. For example, if the user is a student, the generation unit will generate questions suitable for a student. The generation AI selects topics and question formats that are likely to interest students and generates appropriate questions. In this way, the generation unit can generate the most appropriate questions based on the user's attribute information and provide questions that meet the user's interests and needs. The generation unit stores the generated questions in a database, making them accessible to the analysis unit. Furthermore, the generation unit can adjust the frequency and content of question generation, enabling flexible responses to specific situations and conditions. This allows the generation unit to generate questions efficiently and effectively, improving the overall system performance.

[0032] The analysis unit uses generative AI to analyze the answers obtained from the user based on the questions generated by the generation unit. Analysis is performed, for example, to understand the nuances and context of the answers, but is not limited to these examples. Specifically, the analysis unit uses natural language processing techniques to analyze the user's answers and accurately grasp the user's intent. Natural language processing techniques include morphological analysis, contextual analysis, and sentiment analysis. For example, the analysis unit performs morphological analysis on the user's answers, analyzing the meaning and relationships of each word. It also uses contextual analysis to understand the context of the answers and accurately grasp the user's intent. Furthermore, the analysis unit can also analyze the emotional nuances of the answers. For example, the analysis unit calculates an emotion score from the user's answers to understand the user's emotions. The emotion score is categorized into positive, negative, neutral, etc., and indicates the user's emotional state. The analysis unit can also analyze the tone and expression of the answers to more accurately grasp the user's emotions and intentions. This allows the analysis unit to quickly and accurately analyze user answers and grasp the user's intentions and emotions in real time. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term trend analysis and risk assessment. For example, it can predict user responses to specific topics or questions based on past response data and formulate future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0033] The proposal department uses generative AI to make specific improvement suggestions based on the analysis results obtained by the analysis department. These suggestions could, for example, propose improvements to the service interface, but are not limited to such examples. Specifically, if a user responds that they "want a more user-friendly interface," the proposal department will suggest specific improvements such as changing the placement or color of buttons. The generative AI uses an algorithm that proposes the optimal interface design based on past data and user feedback. The proposal department can also propose additions or changes to service functions. For example, if a user responds that they "want a new function," the proposal department will propose specific details of that function. The generative AI uses an algorithm that analyzes user needs and market trends to propose the optimal function. Furthermore, the proposal department can make individually customized suggestions based on user attribute information and responses. For example, if the user is a male in his 20s, the proposal department will make improvement suggestions suitable for men in their 20s. The generative AI selects improvements and functions that are likely to interest men in their 20s and makes appropriate suggestions. The proposal department can also make improvement suggestions suitable for women in their 30s if the user is a female in her 30s. The AI-generating system selects improvements and features that are likely to interest women in their 30s and makes appropriate suggestions. This allows the suggestion department to make optimal improvement suggestions based on user attribute information and responses, contributing to improved service quality. The suggestion department stores the suggestions in a database, making them accessible to other departments and systems. Furthermore, the suggestion department can adjust the frequency and content of suggestions, enabling flexible responses tailored to specific situations and conditions. This allows the suggestion department to make improvement suggestions efficiently and effectively, improving the overall performance of the system.

[0034] The data collection unit can collect attribute information such as the user's age, gender, and occupation. For example, to collect a user's age, the data collection unit calculates the user's age based on the date of birth entered by the user. For example, if a user was born on January 1, 1990, the data collection unit calculates their age as 33 in 2023. The data collection unit can also obtain gender information selected by the user to collect the user's gender. For example, the data collection unit provides a form for the user to select their gender from options such as male, female, or other, and collects the selected information. Furthermore, the data collection unit can obtain occupation information entered by the user to collect the user's occupation. For example, the data collection unit provides a form for the user to select their occupation from options such as student, company employee, or freelancer, and collects the selected information. This allows the data collection unit to generate more appropriate questions by collecting detailed attribute information about the user. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the information entered by the user into an AI, which can then analyze and collect age, gender, and occupation information.

[0035] The generation unit can generate appropriate questions for the user based on collected attribute information. For example, the generation unit generates different questions depending on the user's age and gender. For instance, if the user is a male in his 20s, the generation unit will generate questions suitable for a male in his 20s. Similarly, if the user is a female in her 30s, the generation unit can generate questions suitable for a female in her 30s. Furthermore, the generation unit can adjust the content of the questions according to the user's occupation. For example, if the user is a student, the generation unit will generate questions suitable for a student. The generation unit generates questions using a generation AI. The generation AI, for example, takes user attribute information as input and outputs appropriate questions. The generation AI uses natural language processing technology to generate the content of questions based on the user's attribute information. For example, if the user is a male in his 20s, the generation AI will generate the question, "What are important factors for a male in his 20s?" Similarly, if the user is a female in her 30s, the generation AI can generate the question, "What kind of interface is easy for a female in her 30s to use?" In this way, the generation unit can elicit the user's essential needs by generating appropriate questions based on the user's attribute information. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user attribute information into the AI, which can then generate appropriate questions.

[0036] The analysis unit can understand the detailed meaning and background of the responses obtained from users and accurately grasp the user's intent. For example, the analysis unit analyzes the user's responses using natural language processing technology to accurately grasp the user's intent. For instance, if a user responds, "I want a more user-friendly interface," the analysis unit accurately grasps that intent and proposes specific improvements. The analysis unit can also analyze the emotional nuances of the responses. For example, the analysis unit calculates an emotional score from the user's responses to understand the user's emotions. The analysis unit analyzes responses using generative AI. For example, the generative AI takes a user's response as input and outputs analysis results to understand the nuances and context of the response. The generative AI uses natural language processing technology to analyze the detailed meaning and background of the user's responses. For example, if a user responds, "I want a more user-friendly interface," the generative AI understands the context of that response and proposes specific improvements. In this way, the analysis unit can accurately grasp the user's intent by understanding the nuances and context of the user's responses. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's response into the AI, which can then analyze the nuances and context of the response.

[0037] The proposal department can propose specific improvements based on the analysis results. For example, the proposal department can propose improvements to the service interface. For instance, if a user responds that they "want a more user-friendly interface," the proposal department can propose specific improvements such as changing the placement or color of buttons. The proposal department can also propose the addition or modification of service functions. For example, if a user responds that they "want a new function," the proposal department can propose the specific content of that function. The proposal department makes improvement suggestions using generative AI. For example, the generative AI takes the analysis results as input and outputs specific improvements. The generative AI uses natural language processing technology to propose specific improvements based on the analysis results. For example, if a user responds that they "want a more user-friendly interface," the generative AI accurately grasps their intent and proposes specific improvements. In this way, the proposal department can contribute to improving the quality of the service by proposing specific improvements based on the analysis results. Some or all of the above processing in the proposal department may be performed using AI, or not using AI. For example, the proposal department can input the analysis results into AI, and the AI ​​can propose specific improvements.

[0038] The data collection unit can analyze the user's past behavior history and select an appropriate data collection method. For example, the data collection unit may prioritize data collection methods that the user has frequently used in the past (e.g., questionnaire format). For example, the data collection unit may analyze the user's past responses to questionnaires and collect information again in the questionnaire format. The data collection unit can also select the data collection method that yielded the best response based on the user's behavior history. For example, if the data collection unit has shown a good response to interviews in the past, it will collect information in the interview format. Furthermore, the data collection unit can analyze the user's behavior patterns and collect data at the optimal timing. For example, the data collection unit may analyze that the user is most active during a specific time period and collect information during that time period. This allows the data collection unit to efficiently collect attribute information by selecting the optimal data collection method based on the user's past behavior history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input user behavior history data into AI, which can then select the optimal data collection method.

[0039] The data collection unit can filter attribute information based on the user's current living situation and areas of interest. For example, if the user is currently raising children, the data collection unit will prioritize collecting attribute information related to childcare. For instance, the data collection unit will confirm that the user is raising children and generate questions related to childcare. The data collection unit can also collect attribute information related to a user's hobby if the user has one. For example, the data collection unit will confirm that the user is interested in music and generate questions related to music. Furthermore, the data collection unit can filter appropriate attribute information according to the user's living situation (e.g., student, working adult). For example, the data collection unit will confirm that the user is a student and generate questions related to student life. This allows the data collection unit to collect more relevant attribute information by filtering based on the user's living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's living situation and areas of interest into an AI, which can then perform appropriate filtering.

[0040] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting attribute information. For example, if the user lives in a specific region, the data collection unit will prioritize the collection of attribute information related to that region. For example, if the user lives in an urban area, the data collection unit will generate questions related to urban areas and prompt the user to answer. The data collection unit can also collect attribute information related to the travel destination if the user is traveling. For example, the data collection unit will generate questions about the user's experiences at the travel destination and prompt the user to answer. Furthermore, the data collection unit can also collect region-specific attribute information based on the user's geographical location. For example, if the user lives in a specific country, the data collection unit will generate questions related to that country and prompt the user to answer. In this way, the data collection unit can collect region-specific attribute information by prioritizing the collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, which can then prioritize the collection of highly relevant information.

[0041] The data collection unit can collect relevant information by analyzing the user's social media activity when collecting attribute information. For example, the data collection unit can collect relevant attribute information based on information shared by the user on social media. For example, the data collection unit can analyze the content posted by the user on social media and generate questions based on their interests. The data collection unit can also analyze the content of the user's social media posts and collect attribute information based on their interests. For example, if the data collection unit frequently posts about music, it can generate music-related questions and prompt for responses. Furthermore, the data collection unit can also collect relevant attribute information by considering the user's social media followers and friendships. For example, it can generate questions and prompt for responses based on topics that the user's followers and friends are interested in. In this way, the data collection unit can collect more relevant attribute information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's social media data into AI, and the AI ​​can collect relevant information.

[0042] The generation unit can adjust the level of detail of questions based on the user's attribute information when generating questions. For example, if the user has specialized knowledge, the generation unit will generate detailed questions. For example, if the user has technical expertise, the generation unit will generate questions that include technical details. The generation unit can also generate basic questions if the user is a beginner. For example, the generation unit will confirm that the user is a beginner and generate questions that include basic concepts. Furthermore, the generation unit can generate questions with an appropriate level of detail depending on the user's age and gender. For example, if the user is young, the generation unit will generate concise and easy-to-understand questions. In this way, the generation unit can generate questions that are suitable for the user by adjusting the level of detail of questions based on the user's attribute information. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the user's attribute information into the AI, and the AI ​​can adjust the level of detail of the questions.

[0043] The generation unit can apply different question algorithms depending on the user's area of ​​interest when generating questions. For example, if the user is interested in technology, the generation unit can apply a technology-related question algorithm. For instance, the generation unit can confirm that the user is interested in technology and generate a technology-related question. The generation unit can also apply an art-related question algorithm if the user is interested in art. For example, the generation unit can confirm that the user is interested in art and generate an art-related question. Furthermore, the generation unit can select the most appropriate question algorithm based on the user's area of ​​interest. For example, if the user is interested in sports, the generation unit can apply a sports-related question algorithm. This allows the generation unit to generate more relevant questions by applying a question algorithm according to the user's area of ​​interest. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user area of ​​interest data into the AI, and the AI ​​can apply the most appropriate question algorithm.

[0044] The generation unit can determine the priority of questions based on the user's past answer history when generating questions. For example, the generation unit can prioritize generating questions that the user has frequently answered in the past. For example, the generation unit can analyze the history of questions the user has answered in the past and generate similar questions again. The generation unit can also prioritize generating the most important questions from the user's past answer history. For example, the generation unit can prioritize generating questions that the user has answered importantly in the past. Furthermore, the generation unit can analyze the user's past answer history and determine the optimal order of questions. For example, the generation unit can analyze the order of questions the user has answered in the past and generate questions in the optimal order. In this way, the generation unit can prioritize generating important questions by determining the priority of questions based on the user's past answer history. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the user's past answer history data into AI, and the AI ​​can determine the priority of questions.

[0045] The generation unit can adjust the order of questions based on user relevance when generating questions. For example, the generation unit can generate highly relevant questions first based on the user's areas of interest. For example, if the user is interested in technology, the generation unit will generate technology-related questions first. The generation unit can also prioritize the generation of highly relevant questions based on the user's attribute information. For example, if the user is a student, the generation unit will prioritize the generation of questions related to students. Furthermore, the generation unit can also prioritize the generation of highly relevant questions based on the user's past answer history. For example, the generation unit can analyze the relevance of questions the user has answered in the past and prioritize the generation of highly relevant questions. In this way, the generation unit can generate more effective questions by adjusting the order of questions based on user relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user relevance data into AI, and the AI ​​can adjust the order of questions.

[0046] The analysis unit can improve the accuracy of its analysis by considering user attribute information during response analysis. For example, the analysis unit can select an appropriate analysis method based on the user's age and gender. For example, if the user is young, the analysis unit will select an analysis method suitable for young people. The analysis unit can also improve the accuracy of its analysis based on the user's occupation and interests. For example, if the user is in a technical position, the analysis unit will select a method for analyzing technical responses. Furthermore, the analysis unit can apply the optimal analysis algorithm based on the user's attribute information. For example, if the user has specific interests, the analysis unit will apply an analysis algorithm based on those interests. In this way, the analysis unit improves the accuracy of its analysis by considering the user's attribute information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user attribute information into AI, and the AI ​​can select the optimal analysis method.

[0047] The analysis unit can perform analysis by referring to the user's past response history during response analysis. For example, the analysis unit can analyze response trends based on the user's past response history. For example, the analysis unit can analyze the content of responses previously given by the user to understand response trends. The analysis unit can also analyze the nuances of responses from the user's past response history. For example, the analysis unit can analyze the nuances of responses previously given by the user and compare them with current responses. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the user's past response history. For example, the analysis unit can improve the accuracy of the current response analysis based on the content of responses previously given by the user. In this way, the analysis unit improves the accuracy of the analysis by referring to the user's past response history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's past response history data into AI, and the AI ​​can perform the response analysis.

[0048] The analysis unit can perform analysis while considering the geographical distribution of users during response analysis. For example, the analysis unit can analyze region-specific response trends based on the geographical distribution of users. For example, if the user lives in an urban area, the analysis unit can analyze response trends specific to urban areas. The analysis unit can also improve the accuracy of the analysis by considering the geographical distribution of users. For example, if the user lives in a specific region, the analysis unit can perform analysis while considering the response trends specific to that region. Furthermore, the analysis unit can apply the most suitable analysis algorithm based on the geographical distribution of users. For example, if the user lives in a specific country, the analysis unit can perform analysis while considering the response trends specific to that country. In this way, the analysis unit can analyze region-specific response trends by considering the geographical distribution of users. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution data of users into AI, and the AI ​​can perform the analysis.

[0049] The analysis unit can improve the accuracy of its analysis by referring to relevant literature related to the user's responses. For example, if the user answers a technical question, the analysis unit will refer to relevant technical literature to perform the analysis. The analysis unit can also improve the accuracy of its analysis by referring to research papers related to the user's responses. For example, if the user answers an academic question, the analysis unit will refer to relevant research papers to perform the analysis. Furthermore, the analysis unit can also improve the accuracy of its analysis by referring to databases related to the user's responses. For example, if the user answers a question about market research, the analysis unit will refer to relevant market databases to perform the analysis. In this way, the analysis unit improves the accuracy of its analysis by referring to relevant literature related to the user. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's response data into AI, and the AI ​​can perform the analysis by referring to relevant literature.

[0050] The suggestion unit can make appropriate suggestions when proposing improvements, taking into account the user's attribute information. For example, the suggestion unit can make appropriate improvement suggestions based on the user's age and gender. For instance, if the user is young, the suggestion unit will propose improvements suitable for young people. The suggestion unit can also make optimal improvement suggestions based on the user's occupation and interests. For example, if the user is in a technical position, the suggestion unit will propose technical improvements. Furthermore, the suggestion unit can apply the optimal improvement algorithm based on the user's attribute information. For example, if the user has specific interests, the suggestion unit will propose improvements based on those interests. In this way, the suggestion unit can make more appropriate improvement suggestions by taking the user's attribute information into account. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's attribute information into AI, and the AI ​​can make optimal improvement suggestions.

[0051] The suggestion department can make suggestions by referring to the user's past feedback when proposing improvements. For example, the suggestion department can make optimal improvement suggestions based on the user's past feedback. For example, the suggestion department can analyze the feedback the user has provided in the past and propose similar improvements again. The suggestion department can also prioritize proposing the most important improvements from the user's past feedback. For example, if the user has provided important feedback in the past, the suggestion department will propose improvements based on that feedback. Furthermore, the suggestion department can improve the accuracy of its suggestions by referring to the user's past feedback. For example, the suggestion department can improve the accuracy of its current suggestions based on the feedback the user has provided in the past. This allows the suggestion department to make more appropriate improvement suggestions by referring to the user's past feedback. Some or all of the above processes in the suggestion department may be performed using AI, for example, or not using AI. For example, the suggestion department can input the user's past feedback data into AI, and the AI ​​can make optimal improvement suggestions.

[0052] The suggestion unit can make optimal suggestions when proposing improvements, taking into account the user's geographical location. For example, the suggestion unit can make region-specific improvement suggestions based on the user's geographical location. For instance, if the user lives in an urban area, the suggestion unit can suggest improvements specific to urban areas. Furthermore, if the user is traveling, the suggestion unit can also make improvement suggestions related to their travel destination. For example, the suggestion unit can suggest improvements based on the user's experiences at their travel destination. In addition, the suggestion unit can apply an optimal improvement algorithm based on the user's geographical location. For example, if the user lives in a specific country, the suggestion unit can suggest improvements specific to that country. This allows the suggestion unit to make region-specific improvement suggestions by considering the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's geographical location information into an AI, which can then make optimal improvement suggestions.

[0053] The suggestion department can analyze a user's social media activity and make suggestions when proposing improvements. For example, the suggestion department can make relevant improvement suggestions based on information shared by the user on social media. For example, the suggestion department can analyze the content posted by the user on social media and propose improvements based on their interests. The suggestion department can also analyze the content of the user's social media posts and make improvement suggestions based on their interests. For example, if the suggestion department frequently posts about music, it will propose improvements related to music. Furthermore, the suggestion department can also consider the user's social media followers and friends when making relevant improvement suggestions. For example, the suggestion department can propose improvements based on topics that the user's followers and friends are interested in. This allows the suggestion department to make more relevant improvement suggestions by analyzing the user's social media activity. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not. For example, the suggestion department can input the user's social media data into AI, which can then make relevant improvement suggestions.

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

[0055] The question generator can analyze a user's past purchase history and generate appropriate questions. For example, it can generate relevant questions based on products and services the user has purchased in the past. For instance, if a user has frequently purchased products from a particular brand in the past, it will generate questions related to that brand. The generator can also generate questions based on the user's purchase history and the categories of greatest interest. For example, if a user has purchased many electronic devices in the past, it will generate questions related to electronic devices. Furthermore, the generator can adjust the content of the questions based on the user's purchase history. For example, it can generate questions about the user's experience using products they have purchased in the past to obtain specific feedback. In this way, the generator can provide questions tailored to the user's interests by generating appropriate questions based on the user's purchase history.

[0056] The analysis unit can extract keywords from user responses and adjust the analysis results based on keyword frequency. For example, the analysis unit can extract keywords that frequently appear in user responses and highlight analysis results based on those keywords. The analysis unit can also identify user interests based on keyword frequency. For example, if the keyword "easy to use" frequently appears in user responses, it will highlight analysis results related to ease of use. Furthermore, the analysis unit can determine the priority of analysis results based on keyword frequency. For example, if the keyword "design" is frequently included in user responses, it will prioritize displaying analysis results related to design. In this way, the analysis unit can provide more relevant analysis results by adjusting the results based on keywords included in user responses.

[0057] The suggestion team can make improvement suggestions based on user responses and by referring to feedback from other users. For example, the suggestion team can collect feedback from other users that is similar to the user's response and make improvement suggestions based on that feedback. The suggestion team can also analyze feedback from other users and identify common areas for improvement. For example, if multiple users respond that "the interface is difficult to use," they can make specific suggestions based on those common areas for improvement. Furthermore, the suggestion team can determine the priority of improvement suggestions based on feedback from other users. For example, if many users are requesting "additional features," they will prioritize suggesting the addition of those features. In this way, the suggestion team can make more effective improvement suggestions by referring to feedback from other users.

[0058] The data collection unit can analyze past user feedback and determine the priority of the information to collect. For example, based on the feedback a user has provided in the past, the data collection unit will prioritize collecting the most important information. For instance, if a user previously provided feedback that "the interface is difficult to use," the unit will prioritize collecting information related to that feedback. The data collection unit can also adjust the types of information collected based on the user's past feedback. For example, if a user previously requested "additional features," the unit will collect information related to those features. Furthermore, the data collection unit can adjust the collection method based on the user's past feedback. For example, if a user previously provided positive feedback in an interview format, the unit will collect information again in an interview format. This allows the data collection unit to perform more effective information gathering by prioritizing the information to collect based on the user's past feedback.

[0059] The generation unit can generate region-specific questions based on the user's geographical location. For example, if the user lives in an urban area, the generation unit will generate urban-specific questions. For instance, it might generate a question like, "What are the important factors in urban life?" The generation unit can also generate questions related to the user's travel destination if the user is traveling. For example, it can generate questions based on the user's travel experiences to obtain specific feedback. Furthermore, the generation unit can generate questions related to region-specific interests based on the user's geographical location. For example, if the user lives in a particular country, it will generate questions related to that country. In this way, the generation unit can provide more relevant questions by generating region-specific questions based on the user's geographical location.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects user attribute information. This includes age, gender, occupation, etc. For example, the data collection unit calculates the user's age based on their date of birth and retrieves their selected gender and occupation information. This information is stored in a database and used for analysis later. Step 2: The generation unit uses a generation AI to generate questions based on the information collected by the collection unit. The generated questions will vary depending on the user's age, gender, and occupation. For example, a man in his 20s will be asked questions appropriate for a man in his 20s, and a woman in her 30s will be asked questions appropriate for a woman in her 30s. Also, if the user is a student, questions appropriate for a student will be generated. Step 3: The analysis unit uses generative AI to analyze the answers obtained from the user based on the questions generated by the generation unit. The analysis is performed to understand the nuances and context of the answers. For example, the analysis unit can use natural language processing technology to accurately grasp the user's intent and analyze the emotional nuances of the answers. This allows the analysis unit to calculate the user's emotion score and understand the user's feelings. Step 4: The proposal unit uses the generation AI to make specific improvement suggestions based on the analysis results obtained by the analysis unit. These suggestions can include improvements to the service interface or the addition or modification of functions. For example, if a user responds that they "want a more user-friendly interface," the proposal unit will suggest specific improvements such as changing the placement or color of buttons. Also, if a user responds that they "want a new function," the proposal unit will suggest specific details of that function.

[0062] (Example of form 2) The system according to the embodiment of the present invention is a system that extracts essential latent needs between service providers and users and contributes to improving service quality based on objective data. This system is designed to solve the problems of current surveys and needs assessments. For example, there are problems such as service providers not asking users appropriate questions and service providers not understanding the significance of the answers obtained from users. To solve these problems, this system consists of the following steps. First, user attribute information (age, gender, occupation, etc.) is collected. Next, based on the collected attribute information, a generating AI generates appropriate questions for the user. The generated questions are from the user's perspective and can extract the opinions that the user truly needs. Next, the generating AI analyzes the answers obtained from the user. The generating AI understands the nuances and context of the answers and accurately grasps the user's intentions. This allows service providers to correctly analyze and reflect user opinions. Finally, based on the analysis results, specific improvement suggestions are made to the service provider. This contributes to improving service quality. For example, if a user responds with "I want a more user-friendly interface," the generating AI accurately grasps that intent and suggests specific improvements (such as changing the placement or color of buttons). In this way, the system extracts users' latent needs and provides resources to contribute to improving service quality. As a result, the system can extract the essential latent needs between service providers and users and contribute to improving service quality based on objective data.

[0063] The service quality improvement system according to the embodiment comprises a collection unit, a generation unit, an analysis unit, and a proposal unit. The collection unit collects user attribute information. User attribute information includes, but is not limited to, age, gender, and occupation. For example, to collect the user's age, the collection unit calculates the user's age based on the date of birth entered by the user. The collection unit can also obtain gender information selected by the user to collect the user's gender. Furthermore, the collection unit can also obtain occupation information entered by the user to collect the user's occupation. For example, the collection unit stores the information entered by the user in a database for later analysis. The generation unit generates questions based on the information collected by the collection unit using a generation AI. The generated questions will differ depending on, for example, the user's age and gender, but is not limited to these examples. For example, if the user is a male in his 20s, the generation unit will generate questions suitable for a male in his 20s. The generation unit can also generate questions suitable for a female in her 30s if the user is a female in her 30s. Furthermore, the generation unit can adjust the content of the questions according to the user's occupation. For example, the generation unit generates questions suitable for a student if the user is a student. The analysis unit uses generational AI to analyze the answers obtained from the user based on the questions generated by the generation unit. Analysis is performed to understand the nuances and context of the answers, but is not limited to such examples. For example, the analysis unit uses natural language processing technology to analyze the content of the user's answers and accurately grasp the user's intent. The analysis unit can also analyze the emotional nuances of the answers. For example, the analysis unit calculates an emotional score from the content of the user's answers to understand the user's emotions. The suggestion unit uses generational AI to make specific improvement suggestions based on the analysis results obtained by the analysis unit. Suggestions can be, for example, suggestions for improvements to the service interface, but are not limited to such examples. For example, if the user answers, "I want a more user-friendly interface," the suggestion unit will suggest specific improvements such as changing the placement or color of buttons. The suggestion unit can also suggest adding or changing the functions of the service.For example, if the user responds that they "want a new feature," the proposal department will propose specific details of that feature. In this way, the service quality improvement system according to the embodiment can contribute to improving service quality by generating appropriate questions based on the user's attribute information, analyzing the responses, and making specific improvement suggestions.

[0064] The data collection unit collects user attribute information. This attribute information includes, but is not limited to, age, gender, and occupation. For example, to collect a user's age, the unit calculates the age based on the date of birth entered by the user. Specifically, it stores the date of birth entered by the user during registration in a database and uses an algorithm to calculate the current age based on that data. The data collection unit can also obtain gender information selected by the user to collect their gender. Gender information is entered by the user through a selection of options and stored in the database. Furthermore, the data collection unit can obtain occupation information entered by the user to collect their occupation. Occupation information is entered by the user through free-form input or by selecting from pre-defined options. For example, the data collection unit stores the information entered by the user in a database for later analysis. The data collection unit centrally manages this information and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible by the generation and analysis units. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0065] The generation unit uses a generation AI to generate questions based on information collected by the collection unit. The generated questions will differ depending on, for example, the user's age and gender, but are not limited to these examples. Specifically, the generation AI takes user attribute information as input and uses an algorithm to generate the most appropriate questions based on that information. For example, if the user is a man in his 20s, the generation unit will generate questions suitable for a man in his 20s. The generation AI selects topics and question formats that are likely to interest men in their 20s based on past data and statistical information. The generation unit can also generate questions suitable for a woman in her 30s if the user is a woman in her 30s. The generation AI selects topics and question formats that are likely to interest women in their 30s and generates appropriate questions. Furthermore, the generation unit can adjust the content of the questions according to the user's occupation. For example, if the user is a student, the generation unit will generate questions suitable for a student. The generation AI selects topics and question formats that are likely to interest students and generates appropriate questions. In this way, the generation unit can generate the most appropriate questions based on the user's attribute information and provide questions that meet the user's interests and needs. The generation unit stores the generated questions in a database, making them accessible to the analysis unit. Furthermore, the generation unit can adjust the frequency and content of question generation, enabling flexible responses to specific situations and conditions. This allows the generation unit to generate questions efficiently and effectively, improving the overall system performance.

[0066] The analysis unit uses generative AI to analyze the answers obtained from the user based on the questions generated by the generation unit. Analysis is performed, for example, to understand the nuances and context of the answers, but is not limited to these examples. Specifically, the analysis unit uses natural language processing techniques to analyze the user's answers and accurately grasp the user's intent. Natural language processing techniques include morphological analysis, contextual analysis, and sentiment analysis. For example, the analysis unit performs morphological analysis on the user's answers, analyzing the meaning and relationships of each word. It also uses contextual analysis to understand the context of the answers and accurately grasp the user's intent. Furthermore, the analysis unit can also analyze the emotional nuances of the answers. For example, the analysis unit calculates an emotion score from the user's answers to understand the user's emotions. The emotion score is categorized into positive, negative, neutral, etc., and indicates the user's emotional state. The analysis unit can also analyze the tone and expression of the answers to more accurately grasp the user's emotions and intentions. This allows the analysis unit to quickly and accurately analyze user answers and grasp the user's intentions and emotions in real time. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term trend analysis and risk assessment. For example, it can predict user responses to specific topics or questions based on past response data and formulate future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0067] The proposal department uses generative AI to make specific improvement suggestions based on the analysis results obtained by the analysis department. These suggestions could, for example, propose improvements to the service interface, but are not limited to such examples. Specifically, if a user responds that they "want a more user-friendly interface," the proposal department will suggest specific improvements such as changing the placement or color of buttons. The generative AI uses an algorithm that proposes the optimal interface design based on past data and user feedback. The proposal department can also propose additions or changes to service functions. For example, if a user responds that they "want a new function," the proposal department will propose specific details of that function. The generative AI uses an algorithm that analyzes user needs and market trends to propose the optimal function. Furthermore, the proposal department can make individually customized suggestions based on user attribute information and responses. For example, if the user is a male in his 20s, the proposal department will make improvement suggestions suitable for men in their 20s. The generative AI selects improvements and functions that are likely to interest men in their 20s and makes appropriate suggestions. The proposal department can also make improvement suggestions suitable for women in their 30s if the user is a female in her 30s. The AI-generating system selects improvements and features that are likely to interest women in their 30s and makes appropriate suggestions. This allows the suggestion department to make optimal improvement suggestions based on user attribute information and responses, contributing to improved service quality. The suggestion department stores the suggestions in a database, making them accessible to other departments and systems. Furthermore, the suggestion department can adjust the frequency and content of suggestions, enabling flexible responses tailored to specific situations and conditions. This allows the suggestion department to make improvement suggestions efficiently and effectively, improving the overall performance of the system.

[0068] The data collection unit can collect attribute information such as the user's age, gender, and occupation. For example, to collect a user's age, the data collection unit calculates the user's age based on the date of birth entered by the user. For example, if a user was born on January 1, 1990, the data collection unit calculates their age as 33 in 2023. The data collection unit can also obtain gender information selected by the user to collect the user's gender. For example, the data collection unit provides a form for the user to select their gender from options such as male, female, or other, and collects the selected information. Furthermore, the data collection unit can obtain occupation information entered by the user to collect the user's occupation. For example, the data collection unit provides a form for the user to select their occupation from options such as student, company employee, or freelancer, and collects the selected information. This allows the data collection unit to generate more appropriate questions by collecting detailed attribute information about the user. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the information entered by the user into an AI, which can then analyze and collect age, gender, and occupation information.

[0069] The generation unit can generate appropriate questions for the user based on collected attribute information. For example, the generation unit generates different questions depending on the user's age and gender. For instance, if the user is a male in his 20s, the generation unit will generate questions suitable for a male in his 20s. Similarly, if the user is a female in her 30s, the generation unit can generate questions suitable for a female in her 30s. Furthermore, the generation unit can adjust the content of the questions according to the user's occupation. For example, if the user is a student, the generation unit will generate questions suitable for a student. The generation unit generates questions using a generation AI. The generation AI, for example, takes user attribute information as input and outputs appropriate questions. The generation AI uses natural language processing technology to generate the content of questions based on the user's attribute information. For example, if the user is a male in his 20s, the generation AI will generate the question, "What are important factors for a male in his 20s?" Similarly, if the user is a female in her 30s, the generation AI can generate the question, "What kind of interface is easy for a female in her 30s to use?" In this way, the generation unit can elicit the user's essential needs by generating appropriate questions based on the user's attribute information. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user attribute information into the AI, which can then generate appropriate questions.

[0070] The analysis unit can understand the detailed meaning and background of the responses obtained from users and accurately grasp the user's intent. For example, the analysis unit analyzes the user's responses using natural language processing technology to accurately grasp the user's intent. For instance, if a user responds, "I want a more user-friendly interface," the analysis unit accurately grasps that intent and proposes specific improvements. The analysis unit can also analyze the emotional nuances of the responses. For example, the analysis unit calculates an emotional score from the user's responses to understand the user's emotions. The analysis unit analyzes responses using generative AI. For example, the generative AI takes a user's response as input and outputs analysis results to understand the nuances and context of the response. The generative AI uses natural language processing technology to analyze the detailed meaning and background of the user's responses. For example, if a user responds, "I want a more user-friendly interface," the generative AI understands the context of that response and proposes specific improvements. In this way, the analysis unit can accurately grasp the user's intent by understanding the nuances and context of the user's responses. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's response into the AI, which can then analyze the nuances and context of the response.

[0071] The proposal department can propose specific improvements based on the analysis results. For example, the proposal department can propose improvements to the service interface. For instance, if a user responds that they "want a more user-friendly interface," the proposal department can propose specific improvements such as changing the placement or color of buttons. The proposal department can also propose the addition or modification of service functions. For example, if a user responds that they "want a new function," the proposal department can propose the specific content of that function. The proposal department makes improvement suggestions using generative AI. For example, the generative AI takes the analysis results as input and outputs specific improvements. The generative AI uses natural language processing technology to propose specific improvements based on the analysis results. For example, if a user responds that they "want a more user-friendly interface," the generative AI accurately grasps their intent and proposes specific improvements. In this way, the proposal department can contribute to improving the quality of the service by proposing specific improvements based on the analysis results. Some or all of the above processing in the proposal department may be performed using AI, or not using AI. For example, the proposal department can input the analysis results into AI, and the AI ​​can propose specific improvements.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of attribute information collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit can immediately collect attribute information. For example, the data collection unit can send a questionnaire to the user when they are relaxed and encourage them to answer. The data collection unit can also delay collection if the user is stressed and wait until the user calms down. For example, the data collection unit can delay sending the questionnaire when the user is stressed and send it again when the user is relaxed. The data collection unit can also set a reminder for later collection if the user is busy. For example, the data collection unit can set a reminder when the user is busy and send the questionnaire when the user has time. This allows the data collection unit to collect attribute information at a more appropriate time by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, 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 AI, for example, or without AI. For example, the data collection unit can input user emotion data into AI, which can estimate the emotion and adjust the timing of data collection.

[0073] The data collection unit can analyze the user's past behavior history and select an appropriate data collection method. For example, the data collection unit may prioritize data collection methods that the user has frequently used in the past (e.g., questionnaire format). For example, the data collection unit may analyze the user's past responses to questionnaires and collect information again in the questionnaire format. The data collection unit can also select the data collection method that yielded the best response based on the user's behavior history. For example, if the data collection unit has shown a good response to interviews in the past, it will collect information in the interview format. Furthermore, the data collection unit can analyze the user's behavior patterns and collect data at the optimal timing. For example, the data collection unit may analyze that the user is most active during a specific time period and collect information during that time period. This allows the data collection unit to efficiently collect attribute information by selecting the optimal data collection method based on the user's past behavior history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input user behavior history data into AI, which can then select the optimal data collection method.

[0074] The data collection unit can filter attribute information based on the user's current living situation and areas of interest. For example, if the user is currently raising children, the data collection unit will prioritize collecting attribute information related to childcare. For instance, the data collection unit will confirm that the user is raising children and generate questions related to childcare. The data collection unit can also collect attribute information related to a user's hobby if the user has one. For example, the data collection unit will confirm that the user is interested in music and generate questions related to music. Furthermore, the data collection unit can filter appropriate attribute information according to the user's living situation (e.g., student, working adult). For example, the data collection unit will confirm that the user is a student and generate questions related to student life. This allows the data collection unit to collect more relevant attribute information by filtering based on the user's living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's living situation and areas of interest into an AI, which can then perform appropriate filtering.

[0075] The data collection unit can estimate the user's emotions and determine the priority of attribute information to collect based on the estimated emotions. For example, if the user is excited, the data collection unit will prioritize collecting attribute information related to those emotions. For example, the data collection unit may generate emotion-related questions and prompt the user to answer when the user is excited. The data collection unit can also collect detailed attribute information if the user is calm. For example, the data collection unit may generate detailed questions and prompt the user to answer when the user is calm. Furthermore, if the user is tired, the data collection unit may collect only the most important attribute information. For example, the data collection unit may generate simple questions and prompt the user to answer when the user is tired. In this way, the data collection unit can prioritize the collection of important information by determining the priority of attribute information according to the user's emotions. Emotion estimation is implemented 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 AI, for example, or without AI. For example, the data collection unit can input user emotion data into an AI, which can then estimate the emotion and determine the priority of attribute information to collect.

[0076] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting attribute information. For example, if the user lives in a specific region, the data collection unit will prioritize the collection of attribute information related to that region. For example, if the user lives in an urban area, the data collection unit will generate questions related to urban areas and prompt the user to answer. The data collection unit can also collect attribute information related to the travel destination if the user is traveling. For example, the data collection unit will generate questions about the user's experiences at the travel destination and prompt the user to answer. Furthermore, the data collection unit can also collect region-specific attribute information based on the user's geographical location. For example, if the user lives in a specific country, the data collection unit will generate questions related to that country and prompt the user to answer. In this way, the data collection unit can collect region-specific attribute information by prioritizing the collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, which can then prioritize the collection of highly relevant information.

[0077] The data collection unit can collect relevant information by analyzing the user's social media activity when collecting attribute information. For example, the data collection unit can collect relevant attribute information based on information shared by the user on social media. For example, the data collection unit can analyze the content posted by the user on social media and generate questions based on their interests. The data collection unit can also analyze the content of the user's social media posts and collect attribute information based on their interests. For example, if the data collection unit frequently posts about music, it can generate music-related questions and prompt for responses. Furthermore, the data collection unit can also collect relevant attribute information by considering the user's social media followers and friendships. For example, it can generate questions and prompt for responses based on topics that the user's followers and friends are interested in. In this way, the data collection unit can collect more relevant attribute information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's social media data into AI, and the AI ​​can collect relevant information.

[0078] The generation unit can estimate the user's emotions and adjust the wording of questions based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate questions in a friendly manner. For instance, it might use a friendly expression such as "Please feel free to answer." The generation unit can also generate questions in a simple and clear manner if the user is nervous. For example, it might use a simple expression such as "Please answer briefly." Furthermore, if the user is excited, the generation unit can generate questions in an engaging manner. For example, it might use an engaging expression such as "Please share your opinion." In this way, the generation unit can generate more appropriate questions by adjusting the wording of questions according to the user's emotions. 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, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input user emotion data into the AI, which can then adjust how the questions are phrased.

[0079] The generation unit can adjust the level of detail of questions based on the user's attribute information when generating questions. For example, if the user has specialized knowledge, the generation unit will generate detailed questions. For example, if the user has technical expertise, the generation unit will generate questions that include technical details. The generation unit can also generate basic questions if the user is a beginner. For example, the generation unit will confirm that the user is a beginner and generate questions that include basic concepts. Furthermore, the generation unit can generate questions with an appropriate level of detail depending on the user's age and gender. For example, if the user is young, the generation unit will generate concise and easy-to-understand questions. In this way, the generation unit can generate questions that are suitable for the user by adjusting the level of detail of questions based on the user's attribute information. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the user's attribute information into the AI, and the AI ​​can adjust the level of detail of the questions.

[0080] The generation unit can apply different question algorithms depending on the user's area of ​​interest when generating questions. For example, if the user is interested in technology, the generation unit can apply a technology-related question algorithm. For instance, the generation unit can confirm that the user is interested in technology and generate a technology-related question. The generation unit can also apply an art-related question algorithm if the user is interested in art. For example, the generation unit can confirm that the user is interested in art and generate an art-related question. Furthermore, the generation unit can select the most appropriate question algorithm based on the user's area of ​​interest. For example, if the user is interested in sports, the generation unit can apply a sports-related question algorithm. This allows the generation unit to generate more relevant questions by applying a question algorithm according to the user's area of ​​interest. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user area of ​​interest data into the AI, and the AI ​​can apply the most appropriate question algorithm.

[0081] The 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 generation unit can generate short, to-the-point questions. For instance, it might generate a short question like "Please answer briefly" when the user is in a hurry. Conversely, if the user is relaxed, the generation unit can generate longer questions that include more detailed explanations. For example, it might generate a long question like "Please answer in detail" when the user is relaxed. Furthermore, if the user is excited, the generation unit can generate questions with visually stimulating effects. For example, it might generate a visually appealing question like "Please tell us your opinion" when the user is excited. In this way, the generation unit can generate questions appropriate to the user by adjusting the length of the question according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the AI, which can then adjust the length of the questions.

[0082] The generation unit can determine the priority of questions based on the user's past answer history when generating questions. For example, the generation unit can prioritize generating questions that the user has frequently answered in the past. For example, the generation unit can analyze the history of questions the user has answered in the past and generate similar questions again. The generation unit can also prioritize generating the most important questions from the user's past answer history. For example, the generation unit can prioritize generating questions that the user has answered importantly in the past. Furthermore, the generation unit can analyze the user's past answer history and determine the optimal order of questions. For example, the generation unit can analyze the order of questions the user has answered in the past and generate questions in the optimal order. In this way, the generation unit can prioritize generating important questions by determining the priority of questions based on the user's past answer history. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the user's past answer history data into AI, and the AI ​​can determine the priority of questions.

[0083] The generation unit can adjust the order of questions based on user relevance when generating questions. For example, the generation unit can generate highly relevant questions first based on the user's areas of interest. For example, if the user is interested in technology, the generation unit will generate technology-related questions first. The generation unit can also prioritize the generation of highly relevant questions based on the user's attribute information. For example, if the user is a student, the generation unit will prioritize the generation of questions related to students. Furthermore, the generation unit can also prioritize the generation of highly relevant questions based on the user's past answer history. For example, the generation unit can analyze the relevance of questions the user has answered in the past and prioritize the generation of highly relevant questions. In this way, the generation unit can generate more effective questions by adjusting the order of questions based on user relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user relevance data into AI, and the AI ​​can adjust the order of questions.

[0084] The analysis unit can estimate the user's emotions and adjust the analysis method of the responses based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. For example, when the user is relaxed, the analysis unit can perform a detailed text analysis to grasp the nuances of the responses. The analysis unit can also perform a simpler analysis if the user is tense. For example, when the user is tense, the analysis unit can perform a simple statistical analysis to grasp the gist of the responses. Furthermore, if the user is excited, the analysis unit can prioritize emotion-related analysis. For example, when the user is excited, the analysis unit can perform emotion analysis to grasp the emotional nuances of the responses. This allows the analysis unit to perform more appropriate analysis by adjusting the analysis method of the responses 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-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the AI, which can then adjust how it analyzes the response.

[0085] The analysis unit can improve the accuracy of its analysis by considering user attribute information during response analysis. For example, the analysis unit can select an appropriate analysis method based on the user's age and gender. For example, if the user is young, the analysis unit will select an analysis method suitable for young people. The analysis unit can also improve the accuracy of its analysis based on the user's occupation and interests. For example, if the user is in a technical position, the analysis unit will select a method for analyzing technical responses. Furthermore, the analysis unit can apply the optimal analysis algorithm based on the user's attribute information. For example, if the user has specific interests, the analysis unit will apply an analysis algorithm based on those interests. In this way, the analysis unit improves the accuracy of its analysis by considering the user's attribute information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user attribute information into AI, and the AI ​​can select the optimal analysis method.

[0086] The analysis unit can perform analysis by referring to the user's past response history during response analysis. For example, the analysis unit can analyze response trends based on the user's past response history. For example, the analysis unit can analyze the content of responses previously given by the user to understand response trends. The analysis unit can also analyze the nuances of responses from the user's past response history. For example, the analysis unit can analyze the nuances of responses previously given by the user and compare them with current responses. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the user's past response history. For example, the analysis unit can improve the accuracy of the current response analysis based on the content of responses previously given by the user. In this way, the analysis unit improves the accuracy of the analysis by referring to the user's past response history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's past response history data into AI, and the AI ​​can perform the response analysis.

[0087] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, when the user is nervous, the analysis unit can display the results using concise graphs or charts. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. For example, when the user is relaxed, the analysis unit can display the results using detailed text or diagrams. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the essentials. For example, when the user is in a hurry, the analysis unit can provide a display method that emphasizes only the essentials. In this way, the analysis unit can provide a more appropriate display by adjusting the display method of the analysis results 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 AI, for example, or without AI. For example, the analysis unit can input user emotion data into the AI, which can then adjust how the analysis results are displayed.

[0088] The analysis unit can perform analysis while considering the geographical distribution of users during response analysis. For example, the analysis unit can analyze region-specific response trends based on the geographical distribution of users. For example, if the user lives in an urban area, the analysis unit can analyze response trends specific to urban areas. The analysis unit can also improve the accuracy of the analysis by considering the geographical distribution of users. For example, if the user lives in a specific region, the analysis unit can perform analysis while considering the response trends specific to that region. Furthermore, the analysis unit can apply the most suitable analysis algorithm based on the geographical distribution of users. For example, if the user lives in a specific country, the analysis unit can perform analysis while considering the response trends specific to that country. In this way, the analysis unit can analyze region-specific response trends by considering the geographical distribution of users. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution data of users into AI, and the AI ​​can perform the analysis.

[0089] The analysis unit can improve the accuracy of its analysis by referring to relevant literature related to the user's responses. For example, if the user answers a technical question, the analysis unit will refer to relevant technical literature to perform the analysis. The analysis unit can also improve the accuracy of its analysis by referring to research papers related to the user's responses. For example, if the user answers an academic question, the analysis unit will refer to relevant research papers to perform the analysis. Furthermore, the analysis unit can also improve the accuracy of its analysis by referring to databases related to the user's responses. For example, if the user answers a question about market research, the analysis unit will refer to relevant market databases to perform the analysis. In this way, the analysis unit improves the accuracy of its analysis by referring to relevant literature related to the user. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's response data into AI, and the AI ​​can perform the analysis by referring to relevant literature.

[0090] The suggestion unit can estimate the user's emotions and adjust its improvement suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed improvement suggestions. For example, if the user is relaxed, the suggestion unit can suggest detailed improvements. The suggestion unit can also provide simple and clear improvement suggestions if the user is tense. For example, if the user is tense, the suggestion unit can suggest concise improvements. Furthermore, if the user is excited, the suggestion unit can prioritize emotion-related improvement suggestions. For example, if the user is excited, the suggestion unit can suggest emotional improvements. This allows the suggestion unit to provide more appropriate suggestions by adjusting its improvement suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion department can input user emotion data into the AI, which can then adjust how it suggests improvements.

[0091] The suggestion unit can make appropriate suggestions when proposing improvements, taking into account the user's attribute information. For example, the suggestion unit can make appropriate improvement suggestions based on the user's age and gender. For instance, if the user is young, the suggestion unit will propose improvements suitable for young people. The suggestion unit can also make optimal improvement suggestions based on the user's occupation and interests. For example, if the user is in a technical position, the suggestion unit will propose technical improvements. Furthermore, the suggestion unit can apply the optimal improvement algorithm based on the user's attribute information. For example, if the user has specific interests, the suggestion unit will propose improvements based on those interests. In this way, the suggestion unit can make more appropriate improvement suggestions by taking the user's attribute information into account. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's attribute information into AI, and the AI ​​can make optimal improvement suggestions.

[0092] The suggestion department can make suggestions by referring to the user's past feedback when proposing improvements. For example, the suggestion department can make optimal improvement suggestions based on the user's past feedback. For example, the suggestion department can analyze the feedback the user has provided in the past and propose similar improvements again. The suggestion department can also prioritize proposing the most important improvements from the user's past feedback. For example, if the user has provided important feedback in the past, the suggestion department will propose improvements based on that feedback. Furthermore, the suggestion department can improve the accuracy of its suggestions by referring to the user's past feedback. For example, the suggestion department can improve the accuracy of its current suggestions based on the feedback the user has provided in the past. This allows the suggestion department to make more appropriate improvement suggestions by referring to the user's past feedback. Some or all of the above processes in the suggestion department may be performed using AI, for example, or not using AI. For example, the suggestion department can input the user's past feedback data into AI, and the AI ​​can make optimal improvement suggestions.

[0093] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is excited, the suggestion unit will prioritize suggestions related to those emotions. For instance, when the user is excited, the suggestion unit will prioritize suggesting emotional improvements. The suggestion unit can also prioritize detailed suggestions if the user is relaxed. For instance, when the user is relaxed, the suggestion unit will prioritize suggesting detailed improvements. Furthermore, if the user is tense, the suggestion unit can prioritize simple and clear suggestions. For instance, when the user is tense, the suggestion unit will prioritize suggesting concise improvements. This allows the suggestion unit to provide more appropriate suggestions by prioritizing suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the proposal department can input user emotion data into an AI, which can then determine the priority of proposals.

[0094] The suggestion unit can make optimal suggestions when proposing improvements, taking into account the user's geographical location. For example, the suggestion unit can make region-specific improvement suggestions based on the user's geographical location. For instance, if the user lives in an urban area, the suggestion unit can suggest improvements specific to urban areas. Furthermore, if the user is traveling, the suggestion unit can also make improvement suggestions related to their travel destination. For example, the suggestion unit can suggest improvements based on the user's experiences at their travel destination. In addition, the suggestion unit can apply an optimal improvement algorithm based on the user's geographical location. For example, if the user lives in a specific country, the suggestion unit can suggest improvements specific to that country. This allows the suggestion unit to make region-specific improvement suggestions by considering the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's geographical location information into an AI, which can then make optimal improvement suggestions.

[0095] The suggestion department can analyze a user's social media activity and make suggestions when proposing improvements. For example, the suggestion department can make relevant improvement suggestions based on information shared by the user on social media. For example, the suggestion department can analyze the content posted by the user on social media and propose improvements based on their interests. The suggestion department can also analyze the content of the user's social media posts and make improvement suggestions based on their interests. For example, if the suggestion department frequently posts about music, it will propose improvements related to music. Furthermore, the suggestion department can also consider the user's social media followers and friends when making relevant improvement suggestions. For example, the suggestion department can propose improvements based on topics that the user's followers and friends are interested in. This allows the suggestion department to make more relevant improvement suggestions by analyzing the user's social media activity. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not. For example, the suggestion department can input the user's social media data into AI, which can then make relevant improvement suggestions.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The data collection unit can collect the user's biometric information and adjust the method of collecting attribute information based on the collected biometric information. For example, the data collection unit can monitor the user's heart rate and skin electrical activity, generating detailed questions when the user is relaxed and concise questions when the user is stressed. The data collection unit can also determine the optimal timing for data collection based on the user's biometric information. For example, it can send a questionnaire to the user when they are relaxed after exercise and encourage them to answer. Furthermore, the data collection unit can adjust the collection method based on the user's biometric information. For example, it can collect information through an interview when the user is relaxed and through an online questionnaire when the user is stressed. In this way, the data collection unit can collect attribute information at a more appropriate time and in a more appropriate manner by adjusting the collection method based on the user's biometric information.

[0098] The question generator can analyze a user's past purchase history and generate appropriate questions. For example, it can generate relevant questions based on products and services the user has purchased in the past. For instance, if a user has frequently purchased products from a particular brand in the past, it will generate questions related to that brand. The generator can also generate questions based on the user's purchase history and the categories of greatest interest. For example, if a user has purchased many electronic devices in the past, it will generate questions related to electronic devices. Furthermore, the generator can adjust the content of the questions based on the user's purchase history. For example, it can generate questions about the user's experience using products they have purchased in the past to obtain specific feedback. In this way, the generator can provide questions tailored to the user's interests by generating appropriate questions based on the user's purchase history.

[0099] The analysis unit can extract keywords from user responses and adjust the analysis results based on keyword frequency. For example, the analysis unit can extract keywords that frequently appear in user responses and highlight analysis results based on those keywords. The analysis unit can also identify user interests based on keyword frequency. For example, if the keyword "easy to use" frequently appears in user responses, it will highlight analysis results related to ease of use. Furthermore, the analysis unit can determine the priority of analysis results based on keyword frequency. For example, if the keyword "design" is frequently included in user responses, it will prioritize displaying analysis results related to design. In this way, the analysis unit can provide more relevant analysis results by adjusting the results based on keywords included in user responses.

[0100] The suggestion team can make improvement suggestions based on user responses and by referring to feedback from other users. For example, the suggestion team can collect feedback from other users that is similar to the user's response and make improvement suggestions based on that feedback. The suggestion team can also analyze feedback from other users and identify common areas for improvement. For example, if multiple users respond that "the interface is difficult to use," they can make specific suggestions based on those common areas for improvement. Furthermore, the suggestion team can determine the priority of improvement suggestions based on feedback from other users. For example, if many users are requesting "additional features," they will prioritize suggesting the addition of those features. In this way, the suggestion team can make more effective improvement suggestions by referring to feedback from other users.

[0101] The data collection unit can estimate the user's emotions and adjust the type of information it collects based on those emotions. For example, if the user is excited, the data collection unit will prioritize collecting emotion-related information. For instance, it might generate emotion-related questions and prompt the user to answer when they are excited. The data collection unit can also collect detailed attribute information if the user is relaxed. For example, it might generate detailed questions and prompt the user to answer when they are relaxed. Furthermore, if the user is stressed, the data collection unit can generate concise questions and prompt the user to answer. For example, it might generate simple questions and prompt the user to answer when they are stressed. In this way, the data collection unit can collect more appropriate information by adjusting the type of information it collects according to the user's emotions.

[0102] The generation unit can estimate the user's emotions and adjust the difficulty of the questions based on those emotions. For example, if the user is relaxed, the generation unit will generate difficult questions. For instance, it might generate a question like "Please answer in detail" when the user is relaxed. The generation unit can also generate easy questions if the user is nervous. For example, it might generate a question like "Please answer briefly" when the user is nervous. Furthermore, if the user is excited, the generation unit can generate intriguing questions. For example, it might generate an intriguing question like "Please share your opinion" when the user is excited. In this way, the generation unit can generate more appropriate questions by adjusting the difficulty of the questions according to the user's emotions.

[0103] The analysis unit can analyze the emotions contained in user responses and adjust the analysis results based on those emotions. For example, the analysis unit can highlight positive emotions contained in user responses and prioritize the display of positive feedback. The analysis unit can also analyze responses containing negative emotions in detail and identify areas for improvement. For example, if a user responds that something is "difficult to use," the analysis unit can analyze the nuances of that response and suggest specific areas for improvement. Furthermore, the analysis unit can adjust how the analysis results are displayed based on emotions. For example, responses containing positive emotions can be displayed visually in graphs and charts, while responses containing negative emotions can be displayed in detail in text. In this way, the analysis unit can provide more appropriate feedback by adjusting the analysis results based on the emotions contained in user responses.

[0104] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion function will use friendly language. For instance, it might say, "Try this improvement." If the user is tense, the suggestion function can also use simple and clear language. For example, it might say, "Implement this improvement." Furthermore, if the user is excited, the suggestion function can use intriguing language. For example, it might say, "Try this improvement and you'll see amazing results." By adjusting the way it presents suggestions according to the user's emotions, the suggestion function can make more effective suggestions.

[0105] The data collection unit can analyze past user feedback and determine the priority of the information to collect. For example, based on the feedback a user has provided in the past, the data collection unit will prioritize collecting the most important information. For instance, if a user previously provided feedback that "the interface is difficult to use," the unit will prioritize collecting information related to that feedback. The data collection unit can also adjust the types of information collected based on the user's past feedback. For example, if a user previously requested "additional features," the unit will collect information related to those features. Furthermore, the data collection unit can adjust the collection method based on the user's past feedback. For example, if a user previously provided positive feedback in an interview format, the unit will collect information again in an interview format. This allows the data collection unit to perform more effective information gathering by prioritizing the information to collect based on the user's past feedback.

[0106] The generation unit can generate region-specific questions based on the user's geographical location. For example, if the user lives in an urban area, the generation unit will generate urban-specific questions. For instance, it might generate a question like, "What are the important factors in urban life?" The generation unit can also generate questions related to the user's travel destination if the user is traveling. For example, it can generate questions based on the user's travel experiences to obtain specific feedback. Furthermore, the generation unit can generate questions related to region-specific interests based on the user's geographical location. For example, if the user lives in a particular country, it will generate questions related to that country. In this way, the generation unit can provide more relevant questions by generating region-specific questions based on the user's geographical location.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data collection unit collects user attribute information. This includes age, gender, occupation, etc. For example, the data collection unit calculates the user's age based on their date of birth and retrieves their selected gender and occupation information. This information is stored in a database and used for analysis later. Step 2: The generation unit uses a generation AI to generate questions based on the information collected by the collection unit. The generated questions will vary depending on the user's age, gender, and occupation. For example, a man in his 20s will be asked questions appropriate for a man in his 20s, and a woman in her 30s will be asked questions appropriate for a woman in her 30s. Also, if the user is a student, questions appropriate for a student will be generated. Step 3: The analysis unit uses generative AI to analyze the answers obtained from the user based on the questions generated by the generation unit. The analysis is performed to understand the nuances and context of the answers. For example, the analysis unit can use natural language processing technology to accurately grasp the user's intent and analyze the emotional nuances of the answers. This allows the analysis unit to calculate the user's emotion score and understand the user's feelings. Step 4: The proposal unit uses the generation AI to make specific improvement suggestions based on the analysis results obtained by the analysis unit. These suggestions can include improvements to the service interface or the addition or modification of functions. For example, if a user responds that they "want a more user-friendly interface," the proposal unit will suggest specific improvements such as changing the placement or color of buttons. Also, if a user responds that they "want a new function," the proposal unit will suggest specific details of that function.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] For example, the collection unit can collect user attribute information using the camera 42 and microphone 38B of the smart device 14. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and generates questions based on the collected information. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the answers obtained from the user based on the generated questions. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and makes specific improvement suggestions based on the analysis results. Each of the collection unit, generation unit, analysis unit, and proposal unit can also be implemented, for example, by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] For example, the data collection unit can collect user attribute information using the camera 42 and microphone 238 of the smart glasses 214. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and generates questions based on the collected information. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the answers obtained from the user based on the generated questions. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and makes specific improvement suggestions based on the analysis results. Each of the data collection unit, generation unit, analysis unit, and proposal unit can also be implemented, for example, by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] For example, the data collection unit can collect user attribute information using the camera 42 and microphone 238 of the headset terminal 314. The data generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and generates questions based on the collected information. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the answers obtained from the user based on the generated questions. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and makes specific improvement suggestions based on the analysis results. Each of the data collection unit, generation unit, analysis unit, and proposal unit can also be implemented, for example, by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] For example, the data collection unit can collect user attribute information using the camera 42 and microphone 238 of the robot 414. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and generates questions based on the collected information. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the answers obtained from the user based on the generated questions. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and makes specific improvement suggestions based on the analysis results. Each of the data collection unit, generation unit, analysis unit, and proposal unit can also be implemented, for example, by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A collection unit that collects user attribute information, A generation unit that generates questions based on the information collected by the collection unit, An analysis unit analyzes the answers obtained from the user based on the questions generated by the generation unit, The system includes a proposal unit that makes specific improvement suggestions based on the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect user attribute information such as age, gender, and occupation. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Based on the collected attribute information, generate appropriate questions for the user. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Understand the detailed meaning and background of the responses received from users, and accurately grasp the users' intentions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Based on the analysis results, we propose specific areas for improvement. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of attribute information collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past behavior history and select the appropriate data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting attribute information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and determines the priority of attribute information to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting attribute information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting attribute information, we analyze the user's social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is 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 13) The generating unit is When generating questions, adjust the level of detail based on the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating questions, different question algorithms are applied depending on the user's area of ​​interest. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is The system estimates the user's emotions and adjusts the length of the questions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating questions, the system prioritizes questions based on the user's past answer history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating questions, the order of questions is adjusted based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, We estimate the user's emotions and adjust the response analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, When analyzing responses, we improve the accuracy of the analysis by considering user attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During response analysis, the analysis is performed by referring to the user's past response history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, When analyzing responses, the analysis takes into account the geographical distribution of users. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During response analysis, we improve the accuracy of the analysis by referring to relevant literature from the user. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and adjusts the method of suggesting improvements based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When proposing improvements, consider user attribute information to make appropriate suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When proposing improvements, refer to past user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When proposing improvements, we take the user's geographical location into consideration to provide the most optimal suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When proposing improvements, we analyze users' social media activity and make suggestions based on that analysis. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 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 collection unit that collects user attribute information, A generation unit that generates questions based on the information collected by the collection unit, An analysis unit analyzes the answers obtained from the user based on the questions generated by the generation unit, The system includes a proposal unit that makes specific improvement suggestions based on the analysis results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect user attribute information such as age, gender, and occupation. The system according to feature 1.

3. The generating unit is Based on the collected attribute information, generate appropriate questions for the user. The system according to feature 1.

4. The aforementioned analysis unit, Understand the detailed meaning and background of the responses received from users, and accurately grasp the users' intentions. The system according to feature 1.

5. The aforementioned proposal section is, Based on the analysis results, we propose specific areas for improvement. The system according to feature 1.

6. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of attribute information collection based on the estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the user's past behavior history and select the appropriate data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting attribute information, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

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