System

Abstract

A system in accordance with an embodiment aims to efficiently collect instant feedback based on a user's current location and filter malicious posts.SOLUTION: A system according to an embodiment includes a question generation unit, a questionnaire provision unit, a reward provision unit, a data analysis unit, a filtering unit, and a time-series analysis unit. The question generation unit generates a question based on the current location of the user. The questionnaire providing unit provides the user with the question generated by the question generating unit. The reward providing unit provides a reward to the user who has responded to the questionnaire provided by the questionnaire providing unit. The data analysis unit analyzes the data collected by the questionnaire providing unit. The filtering unit filters a malicious post from the data analyzed by the data analysis unit. The time series analysis unit analyzes the data analyzed by the data analysis unit for each time series.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had problems in that they do not efficiently collect instant feedback based on a user's current location and do not adequately filter malicious posts.

[0005] The system according to the embodiment aims to efficiently collect instant feedback based on the user's current location and filter malicious posts. [Means for solving the problem]

[0006] The system according to the embodiment includes a question generation unit, a questionnaire providing unit, a reward providing unit, a data analysis unit, a filtering unit, and a time series analysis unit. The question generation unit generates questions based on a user's current location. The questionnaire providing unit provides users with questions generated by the question generation unit. The reward providing unit provides rewards to users who respond to questionnaires provided by the questionnaire providing unit. The data analysis unit analyzes data collected by the questionnaire providing unit. The filtering unit filters malicious posts from the data analyzed by the data analysis unit. The time series analysis unit analyzes the data analyzed by the data analysis unit in chronological order. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect instant feedback based on the user's current location and filter malicious posts. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The interactive survey platform according to an embodiment of the present invention is a system in which users provide instant feedback on their daily experiences, and a generative AI generates questions and analyzes the data, thereby enabling the interactive survey platform to instantly collect user feedback and use it to improve business and public services.

[0029] An interactive survey platform according to an embodiment includes a question generation unit, a survey provision unit, a reward provision unit, a data analysis unit, a filtering unit, and a time series analysis unit. The question generation unit generates questions based on a user's current location. For example, the generation AI acquires the user's current location information and generates questions related to that location. The survey provision unit provides the user with the questions generated by the question generation unit. For example, if the user is at a shopping mall, a question such as "How is your current shopping experience?" is displayed. The reward provision unit provides rewards to users who respond to surveys provided by the survey provision unit. For example, points are added for each survey response, and coupons are issued when a certain number of points are accumulated. The data analysis unit analyzes data collected by the survey provision unit. For example, the generation AI analyzes the collected data and provides valuable insights. The filtering unit filters malicious posts from the data analyzed by the data analysis unit. For example, the generation AI automatically detects and deletes posts containing inappropriate language or content. The time series analysis unit analyzes the data analyzed by the data analysis unit in chronological order. For example, if ratings suddenly increase after a particular event, the generative AI can analyze the factors behind this and identify success factors. This allows the interactive survey platform according to the embodiment to instantly collect user feedback and use it to improve business and public services. For example, a shopping mall operator can improve their services based on user feedback and increase customer satisfaction. Furthermore, a public service provider can optimize their services based on resident feedback and improve the quality of life for residents.

[0030] The question generation unit can generate questions related to the current location based on the user's past movement history. For example, the question generation unit analyzes the history of places the user has visited in the past and generates questions related to the current location. For example, the question generation unit may ask the user to rate a cafe they are currently visiting based on the ratings of cafes they have visited in the past. This allows the system to provide more relevant questions by taking the user's past movement history into consideration.

[0031] The question generation unit can customize questions based on the user's current location as well as sensor data on ambient sounds and temperature. For example, the question generation unit analyzes the ambient sounds of the user's current location and generates questions related to those sounds. For example, the question generation unit may ask, "How's the music in the cafe?" based on the ambient sounds of a cafe. The question generation unit may also customize questions based on data from a temperature sensor. For example, if it's cold outside, the question generation unit may ask, "Are you sufficiently prepared for the cold?" This makes it possible to provide more specific and relevant questions by utilizing sensor data.

[0032] The question generation unit can generate questions related to the user's current location based on the content of the user's social media posts. The question generation unit, for example, analyzes the content of the user's social media posts and generates questions related to the user's current location. For example, if the user posts about a cafe, the question generation unit asks, "How is the cafe's service?" The question generation unit also customizes questions based on the content of the user's posts. For example, if the user posts about a restaurant, the question generation unit asks, "How is the restaurant's food?" This makes it possible to provide more relevant questions by utilizing the content of the social media posts.

[0033] The question generation unit can generate questions based on common interests based on feedback from other users. The question generation unit, for example, analyzes the feedback from other users and generates questions based on common interests. For example, if multiple users report the same problem in the same location, the question generation unit asks, "Have you experienced the same problem?" The question generation unit also customizes questions based on common interests. For example, if a user is interested in a particular event, the question generation unit asks, "What did you think of the event?" This makes it possible to provide questions based on common interests by utilizing feedback from other users.

[0034] The survey providing unit can personalize the next question based on the user's answer history. For example, the survey providing unit analyzes the user's past answer history and personalizes the next question. For example, the survey providing unit provides questions related to items that the user has previously given high ratings. The survey providing unit also customizes questions based on the user's answer history. For example, if the user is interested in a particular topic, the survey providing unit provides questions related to that topic. In this way, by personalizing the next question based on the user's answer history, more relevant questions can be provided.

[0035] The questionnaire providing unit can adjust the difficulty and level of detail of the questions according to the speed and accuracy of the user's answers. The questionnaire providing unit, for example, analyzes the user's answering speed and adjusts the difficulty of the questions. For example, the difficulty is increased if the answer is fast and decreased if the answer is slow. The questionnaire providing unit also adjusts the level of detail of the questions based on the accuracy of the user's answers. For example, detailed questions are provided to users who provide accurate answers. In this way, more appropriate questions can be provided by adjusting the difficulty and level of detail of the questions according to the user's answering speed and accuracy.

[0036] The questionnaire providing unit can add questions using images and audio to a question-and-answer format questionnaire to diversify user responses. For example, the questionnaire providing unit adds images to a question-and-answer format questionnaire to provide visual questions. For example, the questionnaire providing unit shows a photo of a specific location and asks, "Please tell us your rating of this location." The questionnaire providing unit also adds audio questions to provide auditory questions. For example, the questionnaire providing unit plays a specific audio and asks, "Please tell us your rating of this audio." In this way, adding questions using images and audio can diversify user responses and obtain richer feedback.

[0037] The questionnaire providing unit can add a function to present sample answers to a user based on the answers of other users. The questionnaire providing unit, for example, presents sample answers from other users so that the user can refer to them. For example, past sample answers are displayed and the user is guided to "answer like this." The questionnaire providing unit also presents model answers to make it easier for the user to answer. For example, a model answer to a specific question is displayed and the user is guided to "answer like this." In this way, presenting sample answers from other users makes it easier for the user to answer and allows for higher quality feedback.

[0038] The reward providing unit can adjust the timing of reward provision based on the user's behavioral patterns. The reward providing unit, for example, analyzes the user's behavioral patterns and optimizes the timing of reward provision. For example, the reward is provided during a time period when the user frequently answers surveys. The reward providing unit also adjusts the timing of reward provision based on the user's behavioral patterns. For example, if the user is active during a specific time period, the reward is provided during that time period. In this way, by optimizing the timing of reward provision based on the user's behavioral patterns, user satisfaction can be improved.

[0039] The reward providing unit can provide usage rights for digital content and services as a reward. For example, the reward providing unit provides usage rights for digital content (e.g., e-books or music) each time a user answers a questionnaire. For example, the reward providing unit provides download rights for e-books when a certain number of points are accumulated. The reward providing unit also provides usage rights for services as a reward. For example, free usage rights for a specific service are provided. In this way, by providing usage rights for digital content or services as a reward, user satisfaction can be improved.

[0040] The reward providing unit allows the user to select a reward and can provide rewards according to individual needs. The reward providing unit, for example, builds a system that allows the user to select a reward. For example, the reward providing unit allows the user to accumulate points and select from multiple rewards. The reward providing unit also provides rewards according to the user's needs. For example, if the user desires a specific reward, the reward is provided. In this way, by allowing the user to select a reward, rewards according to individual needs can be provided.

[0041] The data analysis unit can provide more accurate insights based on the user's past feedback history. The data analysis unit, for example, analyzes the user's past feedback history and reflects it in the current data analysis. For example, it compares past ratings with current ratings to identify trends. The data analysis unit also provides insights based on the user's feedback history. For example, it suggests improvements for items that the user previously gave low ratings. In this way, by taking the user's past feedback history into consideration, more accurate insights can be provided.

[0042] The data analysis unit can integrate external market data and trend information to provide more comprehensive insights. For example, the data analysis unit collects external market data and integrates it into data analysis. For example, trend information for a specific market is reflected in the analysis. The data analysis unit also provides insights based on trend information. For example, it analyzes social media trends to identify user interests. In this way, by integrating external market data and trend information, more comprehensive insights can be provided.

[0043] The data analysis unit can customize the data according to different industries and uses, and provide insights that meet specific needs. For example, the data analysis unit customizes the data according to different industries and provides insights that meet specific needs. For example, it provides insights for the food and beverage industry. The data analysis unit also customizes the data according to specific uses. For example, it provides insights that are specialized for marketing uses. In this way, by customizing the data according to different industries and uses, it is possible to provide insights that meet specific needs.

[0044] The data analysis unit can visualize and provide the data analysis results so that the user can intuitively understand them. The data analysis unit, for example, visualizes the data analysis results so that the user can intuitively understand them. For example, the analysis results are displayed using graphs and charts. The data analysis unit also visualizes the analysis results using infographics. For example, the main points of the data are visually represented. In this way, visualizing the data analysis results allows the user to intuitively understand them.

[0045] The filtering unit can improve accuracy by improving the filtering algorithm based on the user's past posting history. The filtering unit, for example, analyzes the user's past posting history and improves the filtering algorithm. For example, it prioritizes filtering of posts from users who have made inappropriate posts in the past. The filtering unit also tunes the filtering algorithm based on the user's posting history. For example, it uses past posting data to improve the accuracy of the algorithm. In this way, the filtering algorithm can be improved based on the user's past posting history, thereby improving the accuracy of filtering.

[0046] The filtering unit can achieve more advanced filtering based on the context of the posted content. For example, the filtering unit analyzes the context of the posted content to improve the accuracy of filtering. For example, context analysis is used to detect inappropriate posts. The filtering unit also tunes the filtering algorithm based on the context of the posted content. For example, natural language processing technology is used to analyze the meaning of the posted content. In this way, more advanced filtering can be achieved by analyzing the context of the posted content.

[0047] The filtering unit can provide the filtering results to users and encourage them to improve their posted content. The filtering unit, for example, builds a system that provides users with feedback on the filtering results and encourages them to improve their posted content. For example, it presents areas for improvement to users who have made inappropriate posts. The filtering unit also provides feedback based on the user's posted content. For example, it presents specific ways to improve the posted content. In this way, by providing users with feedback on the filtering results, it is possible to encourage them to improve their posted content and maintain the soundness of the platform.

[0048] The time series analysis unit can analyze the time series data in association with external factors such as seasons and events. For example, the time series analysis unit analyzes the time series data in association with seasons to identify fluctuations in ratings in specific seasons. For example, it compares ratings in summer and winter. The time series analysis unit also analyzes the time series data in association with events. For example, it analyzes fluctuations in ratings after specific events. In this way, by analyzing the time series data in association with external factors such as seasons and events, it is possible to identify factors behind fluctuations in ratings.

[0049] The time series analysis unit can analyze fluctuations in the time series data based on the user's behavioral patterns. For example, the time series analysis unit analyzes fluctuations in the time series data in association with the user's behavioral patterns to identify factors behind fluctuations in evaluation. For example, it analyzes fluctuations in evaluation during a specific time period. The time series analysis unit also analyzes time series data based on the user's behavioral patterns. For example, it analyzes fluctuations in evaluation after the user takes a specific action. In this way, by analyzing fluctuations in the time series data in association with the user's behavioral patterns, it is possible to identify factors behind fluctuations in evaluation.

[0050] The time series analysis unit can compare time series data across different regions and cultural spheres to perform analysis from a global perspective. The time series analysis unit, for example, compares time series data across different regions to identify factors that cause fluctuations in evaluation. For example, it compares evaluations in urban and rural areas. The time series analysis unit also compares time series data across different cultural spheres. For example, it compares evaluations in different countries. In this way, by comparing time series data across different regions and cultural spheres, analysis can be performed from a global perspective.

[0051] The time series analysis unit can integrate the time series data based on other data sets to perform a more comprehensive analysis. For example, the time series analysis unit can integrate the time series data with weather data to identify factors that cause fluctuations in ratings. For example, the time series analysis unit can analyze the relationship between weather and ratings. The time series analysis unit can also integrate economic data with the time series data. For example, the time series analysis unit can analyze fluctuations in economic conditions and ratings. In this way, by integrating the time series data with other data sets, a more comprehensive analysis can be performed.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The question generation unit can also generate questions based on the user's hobbies and interests. For example, if the user is interested in music, it can generate a question such as, "What is the best piece of music you've listened to recently?" If the user is interested in sports, it can generate a question such as, "What was your impression of the sporting event you recently watched?" If the user is interested in traveling, it can generate a question such as, "Where would you like to travel next?" This allows for more personalized feedback by providing questions based on the user's hobbies and interests.

[0054] The survey providing unit can personalize the next question based on the user's answer history. For example, it provides questions related to items that the user has previously given high ratings. The survey providing unit also customizes questions based on the user's answer history. For example, if the user is interested in a particular topic, it provides questions related to that topic. In this way, by personalizing the next question based on the user's answer history, it is possible to provide more relevant questions.

[0055] The reward providing unit can provide digital content and service usage rights as a reward. For example, the reward providing unit can provide digital content (e.g., e-books or music) usage rights every time a user answers a questionnaire. For example, the reward providing unit can provide e-book download rights when a certain number of points are accumulated. The reward providing unit can also provide service usage rights as a reward. For example, the reward providing unit can provide free usage rights for a specific service. In this way, by providing digital content and service usage rights as a reward, it is possible to improve user satisfaction.

[0056] The data analysis unit can integrate external market data and trend information to provide more comprehensive insights. For example, it collects external market data and integrates it into data analysis. For example, it reflects trend information of a specific market in the analysis. The data analysis unit also provides insights based on trend information. For example, it analyzes social media trends to identify user interests. In this way, by integrating external market data and trend information, it can provide more comprehensive insights.

[0057] The filtering unit can achieve more advanced filtering based on the context of the posted content. For example, the filtering unit analyzes the context of the posted content to improve the accuracy of filtering. For example, context analysis is used to detect inappropriate posts. The filtering unit also tunes the filtering algorithm based on the context of the posted content. For example, natural language processing technology is used to analyze the meaning of the posted content. In this way, more advanced filtering can be achieved by analyzing the context of the posted content.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The question generator generates questions based on the user's current location. For example, the AI ​​acquires the user's current location information and generates questions related to that location. Step 2: The questionnaire providing unit provides the user with the questions generated by the question generating unit. For example, if the user is in a shopping mall, a question such as "How is your current shopping experience?" is displayed. Step 3: The reward providing unit provides rewards to users who respond to the survey provided by the survey providing unit. For example, points are added for each survey response, and when a certain number of points are accumulated, a coupon is issued. Step 4: The data analysis unit analyzes the data collected by the survey provider. For example, a generation AI analyzes the collected data and provides valuable insights. Step 5: The filtering unit filters malicious posts from the data analyzed by the data analysis unit. For example, the generation AI automatically detects and deletes posts containing inappropriate language or content. Step 6: The time series analysis unit analyzes the data analyzed by the data analysis unit by time series. For example, if the evaluation suddenly increases after a specific event, the generation AI analyzes the factors behind this and identifies the success factors.

[0060] (Example 2) The interactive survey platform according to an embodiment of the present invention is a system in which users provide instant feedback on their daily experiences, and a generative AI generates questions and analyzes the data, thereby enabling the interactive survey platform to instantly collect user feedback and use it to improve business and public services.

[0061] An interactive survey platform according to an embodiment includes a question generation unit, a survey provision unit, a reward provision unit, a data analysis unit, a filtering unit, and a time series analysis unit. The question generation unit generates questions based on a user's current location. For example, the generation AI acquires the user's current location information and generates questions related to that location. The survey provision unit provides the user with the questions generated by the question generation unit. For example, if the user is at a shopping mall, a question such as "How is your current shopping experience?" is displayed. The reward provision unit provides rewards to users who respond to surveys provided by the survey provision unit. For example, points are added for each survey response, and coupons are issued when a certain number of points are accumulated. The data analysis unit analyzes data collected by the survey provision unit. For example, the generation AI analyzes the collected data and provides valuable insights. The filtering unit filters malicious posts from the data analyzed by the data analysis unit. For example, the generation AI automatically detects and deletes posts containing inappropriate language or content. The time series analysis unit analyzes the data analyzed by the data analysis unit in chronological order. For example, if ratings suddenly increase after a particular event, the generative AI can analyze the factors behind this and identify success factors. This allows the interactive survey platform according to the embodiment to instantly collect user feedback and use it to improve business and public services. For example, a shopping mall operator can improve their services based on user feedback and increase customer satisfaction. Furthermore, a public service provider can optimize their services based on resident feedback and improve the quality of life for residents.

[0062] The question generation unit can generate questions related to the current location based on the user's past movement history. For example, the question generation unit analyzes the history of places the user has visited in the past and generates questions related to the current location. For example, the question generation unit may ask the user to rate a cafe they are currently visiting based on the ratings of cafes they have visited in the past. This allows the system to provide more relevant questions by taking the user's past movement history into consideration.

[0063] The question generation unit can customize questions based on the user's current location as well as sensor data on ambient sounds and temperature. For example, the question generation unit analyzes the ambient sounds of the user's current location and generates questions related to those sounds. For example, the question generation unit may ask, "How's the music in the cafe?" based on the ambient sounds of a cafe. The question generation unit may also customize questions based on data from a temperature sensor. For example, if it's cold outside, the question generation unit may ask, "Are you sufficiently prepared for the cold?" This makes it possible to provide more specific and relevant questions by utilizing sensor data.

[0064] The question generation unit can use the emotion estimation function to analyze the user's current emotional state and generate questions according to that emotion. The question generation unit, for example, analyzes the user's facial expressions and voice to estimate the user's current emotional state. For example, if the user is smiling, the question generation unit asks, "Are you having fun?" The question generation unit also analyzes the user's tone of voice to estimate the user's emotional state. For example, if the user is excited, the question generation unit asks, "What's so fun about that?" This allows the user to receive more appropriate feedback by providing questions according to their emotional state.

[0065] The question generation unit can generate questions related to the user's current location based on the content of the user's social media posts. The question generation unit, for example, analyzes the content of the user's social media posts and generates questions related to the user's current location. For example, if the user posts about a cafe, the question generation unit asks, "How is the cafe's service?" The question generation unit also customizes questions based on the content of the user's posts. For example, if the user posts about a restaurant, the question generation unit asks, "How is the restaurant's food?" This makes it possible to provide more relevant questions by utilizing the content of the social media posts.

[0066] The question generation unit can generate questions based on common interests based on feedback from other users. The question generation unit, for example, analyzes the feedback from other users and generates questions based on common interests. For example, if multiple users report the same problem in the same location, the question generation unit asks, "Have you experienced the same problem?" The question generation unit also customizes questions based on common interests. For example, if a user is interested in a particular event, the question generation unit asks, "What did you think of the event?" This makes it possible to provide questions based on common interests by utilizing feedback from other users.

[0067] The question generation unit can use the emotion estimation function to monitor the emotions of the user when answering questions in real time and dynamically adjust the content of the questions. For example, the question generation unit analyzes the facial expressions and voice of the user when answering questions in real time and adjusts the content of the questions according to the user's emotional state. For example, if the user is confused, the question generation unit simplifies the question. The question generation unit also customizes questions based on the user's emotional state. For example, if the user is excited, the question generation unit asks, "What's so fun about that?" In this way, by monitoring the user's emotions in real time and dynamically adjusting the content of the questions, more appropriate feedback can be obtained.

[0068] The survey providing unit can personalize the next question based on the user's answer history. For example, the survey providing unit analyzes the user's past answer history and personalizes the next question. For example, the survey providing unit provides questions related to items that the user has previously given high ratings. The survey providing unit also customizes questions based on the user's answer history. For example, if the user is interested in a particular topic, the survey providing unit provides questions related to that topic. In this way, by personalizing the next question based on the user's answer history, more relevant questions can be provided.

[0069] The questionnaire providing unit can adjust the difficulty and level of detail of the questions according to the speed and accuracy of the user's answers. The questionnaire providing unit, for example, analyzes the user's answering speed and adjusts the difficulty of the questions. For example, the difficulty is increased if the answer is fast and decreased if the answer is slow. The questionnaire providing unit also adjusts the level of detail of the questions based on the accuracy of the user's answers. For example, detailed questions are provided to users who provide accurate answers. In this way, more appropriate questions can be provided by adjusting the difficulty and level of detail of the questions according to the user's answering speed and accuracy.

[0070] The questionnaire providing unit can use the emotion estimation function to analyze the user's emotions when answering questions and generate questions that elicit positive emotions. The questionnaire providing unit, for example, analyzes the user's facial expressions and voice when answering questions to estimate the user's emotional state. For example, if the user is smiling, the questionnaire providing unit may ask, "Tell us about a fun experience." The questionnaire providing unit may also customize questions based on the user's emotional state. For example, if the user is excited, the questionnaire providing unit may ask, "What is so fun about it?" This allows the user's emotions to be analyzed and questions that elicit positive emotions to be generated, thereby obtaining better feedback.

[0071] The questionnaire providing unit can add questions using images and audio to a question-and-answer format questionnaire to diversify user responses. For example, the questionnaire providing unit adds images to a question-and-answer format questionnaire to provide visual questions. For example, the questionnaire providing unit shows a photo of a specific location and asks, "Please tell us your rating of this location." The questionnaire providing unit also adds audio questions to provide auditory questions. For example, the questionnaire providing unit plays a specific audio and asks, "Please tell us your rating of this audio." In this way, adding questions using images and audio can diversify user responses and obtain richer feedback.

[0072] The questionnaire providing unit can add a function to present sample answers to a user based on the answers of other users. The questionnaire providing unit, for example, presents sample answers from other users so that the user can refer to them. For example, past sample answers are displayed and the user is guided to "answer like this." The questionnaire providing unit also presents model answers to make it easier for the user to answer. For example, a model answer to a specific question is displayed and the user is guided to "answer like this." In this way, presenting sample answers from other users makes it easier for the user to answer and allows for higher quality feedback.

[0073] The questionnaire providing unit uses the emotion estimation function to analyze the emotions of the user when answering questions in real time, thereby improving the quality of the answers. For example, the questionnaire providing unit analyzes the facial expressions and voice of the user when answering questions in real time and provides feedback on the emotional state. For example, if the user is confused, the questionnaire providing unit may advise the user to "relax." The questionnaire providing unit also improves the quality of the answers based on the user's emotional state. For example, if the user is excited, the questionnaire providing unit may ask, "What's so fun about that?" In this way, the quality of the answers can be improved by providing feedback on the user's emotions in real time.

[0074] The reward providing unit can adjust the timing of reward provision based on the user's behavioral patterns. The reward providing unit, for example, analyzes the user's behavioral patterns and optimizes the timing of reward provision. For example, the reward is provided during a time period when the user frequently answers surveys. The reward providing unit also adjusts the timing of reward provision based on the user's behavioral patterns. For example, if the user is active during a specific time period, the reward is provided during that time period. In this way, by optimizing the timing of reward provision based on the user's behavioral patterns, user satisfaction can be improved.

[0075] The reward providing unit uses the emotion estimation function to evaluate the emotion of the user when receiving the reward and can improve the satisfaction level of the reward. For example, the reward providing unit analyzes the facial expression and voice of the user when receiving the reward to estimate the emotional state. For example, if the user is happy, the reward providing unit may say "Congratulations." The reward providing unit may also improve the satisfaction level of the reward based on the user's emotional state. For example, if the user is satisfied, the reward providing unit may provide an additional reward. In this way, the user's emotion can be analyzed and the satisfaction level of the reward can be improved, thereby increasing the user's motivation.

[0076] The reward providing unit can provide usage rights for digital content and services as a reward. For example, the reward providing unit provides usage rights for digital content (e.g., e-books or music) each time a user answers a questionnaire. For example, the reward providing unit provides download rights for e-books when a certain number of points are accumulated. The reward providing unit also provides usage rights for services as a reward. For example, free usage rights for a specific service are provided. In this way, by providing usage rights for digital content or services as a reward, user satisfaction can be improved.

[0077] The reward providing unit allows the user to select a reward and can provide rewards according to individual needs. The reward providing unit, for example, builds a system that allows the user to select a reward. For example, the reward providing unit allows the user to accumulate points and select from multiple rewards. The reward providing unit also provides rewards according to the user's needs. For example, if the user desires a specific reward, the reward is provided. In this way, by allowing the user to select a reward, rewards according to individual needs can be provided.

[0078] The reward providing unit can use the emotion estimation function to identify the reward that the user most likes and provide that reward preferentially. For example, the reward providing unit analyzes the facial expression and voice of the user when receiving a reward to identify the reward that the user most likes. For example, it preferentially provides a reward that makes the user smile. The reward providing unit also customizes rewards based on the user's emotional state. For example, it provides an additional reward when the user is excited. In this way, it is possible to improve user satisfaction by identifying the reward that the user most likes and providing it preferentially.

[0079] The data analysis unit can provide more accurate insights based on the user's past feedback history. The data analysis unit, for example, analyzes the user's past feedback history and reflects it in the current data analysis. For example, it compares past ratings with current ratings to identify trends. The data analysis unit also provides insights based on the user's feedback history. For example, it suggests improvements for items that the user previously gave low ratings. In this way, by taking the user's past feedback history into consideration, more accurate insights can be provided.

[0080] The data analysis unit can integrate external market data and trend information to provide more comprehensive insights. For example, the data analysis unit collects external market data and integrates it into data analysis. For example, trend information for a specific market is reflected in the analysis. The data analysis unit also provides insights based on trend information. For example, it analyzes social media trends to identify user interests. In this way, by integrating external market data and trend information, more comprehensive insights can be provided.

[0081] The data analysis unit can use the emotion estimation function to evaluate the user's emotion data and provide insights based on the emotion. The data analysis unit, for example, analyzes the user's emotion data and provides insights based on the emotion. For example, it increases the rating of places where the user expressed positive emotion. The data analysis unit also provides insights based on the user's emotional state. For example, it suggests improvements to places where the user expressed negative emotion. In this way, by analyzing the user's emotion data and providing insights based on the emotion, it is possible to suggest more appropriate improvement measures.

[0082] The data analysis unit can customize the data according to different industries and uses, and provide insights that meet specific needs. For example, the data analysis unit customizes the data according to different industries and provides insights that meet specific needs. For example, it provides insights for the food and beverage industry. The data analysis unit also customizes the data according to specific uses. For example, it provides insights that are specialized for marketing uses. In this way, by customizing the data according to different industries and uses, it is possible to provide insights that meet specific needs.

[0083] The data analysis unit can visualize and provide the data analysis results so that the user can intuitively understand them. The data analysis unit, for example, visualizes the data analysis results so that the user can intuitively understand them. For example, the analysis results are displayed using graphs and charts. The data analysis unit also visualizes the analysis results using infographics. For example, the main points of the data are visually represented. In this way, visualizing the data analysis results allows the user to intuitively understand them.

[0084] The data analysis unit can use the emotion estimation function to dynamically adjust the method of providing insights based on the user's emotional response. The data analysis unit, for example, builds a system that dynamically adjusts the method of providing insights based on the user's emotional response. For example, detailed insights are provided when the user shows positive emotions. The data analysis unit also customizes the method of providing insights based on the user's emotional state. For example, concise insights are provided when the user shows negative emotions. In this way, more appropriate insights can be provided by dynamically adjusting the method of providing insights based on the user's emotional response.

[0085] The filtering unit can improve accuracy by improving the filtering algorithm based on the user's past posting history. The filtering unit, for example, analyzes the user's past posting history and improves the filtering algorithm. For example, it prioritizes filtering of posts from users who have made inappropriate posts in the past. The filtering unit also tunes the filtering algorithm based on the user's posting history. For example, it uses past posting data to improve the accuracy of the algorithm. In this way, the filtering algorithm can be improved based on the user's past posting history, thereby improving the accuracy of filtering.

[0086] The filtering unit can achieve more advanced filtering based on the context of the posted content. For example, the filtering unit analyzes the context of the posted content to improve the accuracy of filtering. For example, context analysis is used to detect inappropriate posts. The filtering unit also tunes the filtering algorithm based on the context of the posted content. For example, natural language processing technology is used to analyze the meaning of the posted content. In this way, more advanced filtering can be achieved by analyzing the context of the posted content.

[0087] The filtering unit can use the emotion estimation function to evaluate the poster's emotional state and predict malicious posts in advance. The filtering unit, for example, analyzes the poster's emotional state and builds a system that predicts malicious posts in advance. For example, it displays a warning when the poster expresses anger. The filtering unit also tunes the filtering algorithm based on the poster's emotional state. For example, it detects malicious posts using the emotion analysis results. In this way, the soundness of the platform can be maintained by analyzing the poster's emotional state and predicting malicious posts in advance.

[0088] The filtering unit can provide the filtering results to users and encourage them to improve their posted content. The filtering unit, for example, builds a system that provides users with feedback on the filtering results and encourages them to improve their posted content. For example, it presents areas for improvement to users who have made inappropriate posts. The filtering unit also provides feedback based on the user's posted content. For example, it presents specific ways to improve the posted content. In this way, by providing users with feedback on the filtering results, it is possible to encourage them to improve their posted content and maintain the soundness of the platform.

[0089] The filtering unit can use the emotion estimation function to evaluate the user's emotional response to the filtered posts and improve filtering accuracy. The filtering unit, for example, analyzes the user's emotional response to the filtered posts and builds a system that improves filtering accuracy. For example, the filtering unit reviews the filtering criteria when the user expresses dissatisfaction. The filtering unit also tunes the filtering algorithm based on the user's emotional response. For example, the filtering criteria are adjusted using the emotion analysis results. In this way, filtering accuracy can be improved by analyzing the user's emotional response to the filtered posts.

[0090] The time series analysis unit can analyze the time series data in association with external factors such as seasons and events. For example, the time series analysis unit analyzes the time series data in association with seasons to identify fluctuations in ratings in specific seasons. For example, it compares ratings in summer and winter. The time series analysis unit also analyzes the time series data in association with events. For example, it analyzes fluctuations in ratings after specific events. In this way, by analyzing the time series data in association with external factors such as seasons and events, it is possible to identify factors behind fluctuations in ratings.

[0091] The time series analysis unit can analyze fluctuations in the time series data based on the user's behavioral patterns. For example, the time series analysis unit analyzes fluctuations in the time series data in association with the user's behavioral patterns to identify factors behind fluctuations in evaluation. For example, it analyzes fluctuations in evaluation during a specific time period. The time series analysis unit also analyzes time series data based on the user's behavioral patterns. For example, it analyzes fluctuations in evaluation after the user takes a specific action. In this way, by analyzing fluctuations in the time series data in association with the user's behavioral patterns, it is possible to identify factors behind fluctuations in evaluation.

[0092] The time series analysis unit can use the emotion estimation function to evaluate changes in a user's emotions that accompany fluctuations in the time series data. The time series analysis unit, for example, analyzes changes in a user's emotions that accompany fluctuations in the time series data and identifies factors that cause fluctuations in evaluation. For example, it analyzes changes in emotions after a specific event. The time series analysis unit also analyzes time series data based on changes in a user's emotions. For example, it analyzes fluctuations in evaluation after a user expresses positive emotions. In this way, it is possible to identify factors that cause fluctuations in evaluation by analyzing changes in a user's emotions that accompany fluctuations in the time series data.

[0093] The time series analysis unit can compare time series data across different regions and cultural spheres to perform analysis from a global perspective. The time series analysis unit, for example, compares time series data across different regions to identify factors that cause fluctuations in evaluation. For example, it compares evaluations in urban and rural areas. The time series analysis unit also compares time series data across different cultural spheres. For example, it compares evaluations in different countries. In this way, by comparing time series data across different regions and cultural spheres, analysis can be performed from a global perspective.

[0094] The time series analysis unit can integrate the time series data based on other data sets to perform a more comprehensive analysis. For example, the time series analysis unit can integrate the time series data with weather data to identify factors that cause fluctuations in ratings. For example, the time series analysis unit can analyze the relationship between weather and ratings. The time series analysis unit can also integrate economic data with the time series data. For example, the time series analysis unit can analyze fluctuations in economic conditions and ratings. In this way, by integrating the time series data with other data sets, a more comprehensive analysis can be performed.

[0095] The time series analysis unit can use the emotion estimation function to monitor the user's emotional response to fluctuations in time series data in real time and dynamically adjust the analysis results. The time series analysis unit, for example, builds a system that monitors the user's emotional response to fluctuations in time series data in real time and dynamically adjusts the analysis results. For example, it identifies the factors that cause fluctuations in evaluations in response to changes in the user's emotions. The time series analysis unit also adjusts the analysis results based on the user's emotional response. For example, it provides detailed insights when the user expresses positive emotions. This makes it possible to provide more appropriate insights by monitoring the user's emotional response to fluctuations in time series data in real time and dynamically adjusting the analysis results.

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

[0097] The question generation unit can also generate questions based on the user's hobbies and interests. For example, if the user is interested in music, it can generate a question such as, "What is the best piece of music you've listened to recently?" If the user is interested in sports, it can generate a question such as, "What was your impression of the sporting event you recently watched?" If the user is interested in traveling, it can generate a question such as, "Where would you like to travel next?" This allows for more personalized feedback by providing questions based on the user's hobbies and interests.

[0098] The survey providing unit can personalize the next question based on the user's answer history. For example, it provides questions related to items that the user has previously given high ratings. The survey providing unit also customizes questions based on the user's answer history. For example, if the user is interested in a particular topic, it provides questions related to that topic. In this way, by personalizing the next question based on the user's answer history, it is possible to provide more relevant questions.

[0099] The reward providing unit can provide digital content and service usage rights as a reward. For example, the reward providing unit can provide digital content (e.g., e-books or music) usage rights every time a user answers a questionnaire. For example, the reward providing unit can provide e-book download rights when a certain number of points are accumulated. The reward providing unit can also provide service usage rights as a reward. For example, the reward providing unit can provide free usage rights for a specific service. In this way, by providing digital content and service usage rights as a reward, it is possible to improve user satisfaction.

[0100] The data analysis unit can integrate external market data and trend information to provide more comprehensive insights. For example, it collects external market data and integrates it into data analysis. For example, it reflects trend information of a specific market in the analysis. The data analysis unit also provides insights based on trend information. For example, it analyzes social media trends to identify user interests. In this way, by integrating external market data and trend information, it can provide more comprehensive insights.

[0101] The filtering unit can achieve more advanced filtering based on the context of the posted content. For example, the filtering unit analyzes the context of the posted content to improve the accuracy of filtering. For example, context analysis is used to detect inappropriate posts. The filtering unit also tunes the filtering algorithm based on the context of the posted content. For example, natural language processing technology is used to analyze the meaning of the posted content. In this way, more advanced filtering can be achieved by analyzing the context of the posted content.

[0102] The question generation unit can use the emotion estimation function to analyze the user's current emotional state and generate questions according to that emotion. For example, the question generation unit can analyze the user's facial expressions and voice to estimate the user's current emotional state. For example, if the user is smiling, the question generation unit can ask, "Are you having fun?" The question generation unit can also analyze the user's tone of voice to estimate the user's emotional state. For example, if the user is excited, the question generation unit can ask, "What's so fun about that?" This allows the user to receive more appropriate feedback by providing questions according to their emotional state.

[0103] The questionnaire provider can use the emotion estimation function to analyze the emotions of the user when answering questions and generate questions that elicit positive emotions. For example, it can analyze the user's facial expressions and voice when answering questions to estimate their emotional state. For example, if the user is smiling, it can ask, "Tell us about a fun experience." The questionnaire provider can also customize questions based on the user's emotional state. For example, if the user is excited, it can ask, "What is so fun about it?" This allows the user's emotions to be analyzed and questions that elicit positive emotions to be generated, resulting in better feedback.

[0104] The reward providing unit can use the emotion estimation function to evaluate the emotion of the user when receiving the reward and improve the satisfaction level of the reward. For example, the emotional state of the user can be estimated by analyzing the facial expression and voice when receiving the reward. For example, if the user is happy, the reward providing unit can say "Congratulations." The reward providing unit can also improve the satisfaction level of the reward based on the user's emotional state. For example, if the user is satisfied, the reward providing unit can provide an additional reward. In this way, the user's emotion can be analyzed and the satisfaction level of the reward can be improved, thereby increasing the user's motivation.

[0105] The data analysis unit can use the emotion estimation function to evaluate the user's emotion data and provide insights based on the emotion. For example, the data analysis unit analyzes the user's emotion data and provides insights based on the emotion. For example, it may increase the rating of places where the user expressed positive emotion. The data analysis unit also provides insights based on the user's emotional state. For example, it may suggest improvements to places where the user expressed negative emotion. In this way, by analyzing the user's emotion data and providing insights based on the emotion, it is possible to suggest more appropriate improvement measures.

[0106] The filtering unit can use the emotion estimation function to evaluate the poster's emotional state and predict malicious posts in advance. For example, a system can be constructed that analyzes the poster's emotional state and predicts malicious posts in advance. For example, a warning can be displayed if the poster expresses anger. The filtering unit also tunes the filtering algorithm based on the poster's emotional state. For example, malicious posts can be detected using the emotion analysis results. This allows the platform to maintain its soundness by analyzing the poster's emotional state and predicting malicious posts in advance.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The question generator generates questions based on the user's current location. For example, the AI ​​acquires the user's current location information and generates questions related to that location. Step 2: The questionnaire providing unit provides the user with the questions generated by the question generating unit. For example, if the user is in a shopping mall, a question such as "How is your current shopping experience?" is displayed. Step 3: The reward providing unit provides rewards to users who respond to the survey provided by the survey providing unit. For example, points are added for each survey response, and when a certain number of points are accumulated, a coupon is issued. Step 4: The data analysis unit analyzes the data collected by the survey provider. For example, a generation AI analyzes the collected data and provides valuable insights. Step 5: The filtering unit filters malicious posts from the data analyzed by the data analysis unit. For example, the generation AI automatically detects and deletes posts containing inappropriate language or content. Step 6: The time series analysis unit analyzes the data analyzed by the data analysis unit by time series. For example, if the evaluation suddenly increases after a specific event, the generation AI analyzes the factors behind this and identifies the success factors.

[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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 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.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0150] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0153] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0160] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0161] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0166] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0167] 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.

[0168] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0169] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0170] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0171] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0173] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0174] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0175] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a question generator that generates a question based on the user's current location; a questionnaire providing unit that provides the questions generated by the question generating unit to users; a reward providing unit that provides a reward to a user who answers the questionnaire provided by the questionnaire providing unit; a data analysis unit that analyzes the data collected by the questionnaire providing unit; a filtering unit that filters malicious posts from the data analyzed by the data analysis unit; a time series analysis unit that analyzes the data analyzed by the data analysis unit for each time series; A system characterized by:

2. The question generation unit Customize questions based on the user's current location, as well as ambient sound and temperature sensor data.

2. The system of claim 1.

3. The questionnaire providing unit: Personalize the next question based on the user's answer history 2. The system of claim 1.

4. The reward providing unit: Dynamically adjusting the type and amount of the reward based on the user's response.

2. The system of claim 1.

5. The data analysis unit Providing more accurate insights based on the user's past feedback history 2. The system of claim 1.

6. The filtering unit Using emotion estimation function, the emotional state of the poster is evaluated and the malicious posts are predicted in advance.

2. The system of claim 1.

7. The time series analysis unit Analyzing time series data in relation to external factors such as seasons and events 2. The system of claim 1.

8. The question generation unit Using emotion estimation functionality, the current emotional state of the user is analyzed and questions are generated according to the emotion.

2. The system of claim 1.

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