system

Abstract

The system according to this embodiment aims to efficiently collect user feedback, filter out malicious posts, and analyze the feedback over time. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a filtering unit, a time-series analysis unit, and a reward unit. The reception unit receives user feedback. The analysis unit analyzes the feedback received by the reception unit. The filtering unit filters out malicious posts based on the data analyzed by the analysis unit. The time-series analysis unit analyzes the data filtered by the filtering unit in a time-series manner. The reward unit provides rewards to users based on the analysis results obtained by the time-series analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it has not been sufficiently carried out to efficiently collect user feedback, filter malicious posts, and analyze them in time series.

[0005] The system according to the embodiment aims to efficiently collect user feedback, filter malicious posts, and analyze them in time series.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a filtering unit, a time-series analysis unit, and a reward unit. The reception unit receives user feedback. The analysis unit analyzes the feedback received by the reception unit. The filtering unit filters out malicious posts based on the data analyzed by the analysis unit. The time-series analysis unit analyzes the data filtered by the filtering unit in a time-series manner. The reward unit provides rewards to users based on the analysis results obtained by the time-series analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently collect user feedback, filter out malicious posts, and analyze the feedback over time. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An interactive survey platform according to an embodiment of the present invention is a system that allows users to provide instant feedback on their everyday experiences. This system allows users to earn rewards by answering location-based questions and simple question-and-answer surveys about their experiences at that moment. The collected data is used to improve businesses and public services, and malicious posts are filtered by AI. The data is also analyzed chronologically to understand what kind of evaluations were given at what point in time, and is used for analyzing trending topics and other insights. For example, users answer location-based questions and simple question-and-answer surveys about their experiences at that moment. Users answer the surveys using devices such as smartphones or tablets. For example, users can provide real-time ratings of restaurants they have visited or feedback on events they have attended. Next, users are rewarded for answering the surveys. Rewards are provided in the form of points or coupons to increase user engagement. For example, users may receive discount coupons after answering a certain number of surveys. The collected data is used to improve businesses and public services. For example, analyzing restaurant rating data can lead to improvements in menus and services. Furthermore, analyzing event feedback data can be used to plan future events. In addition, malicious posts are filtered by AI. The AI ​​analyzes post content, automatically detecting and removing inappropriate material and spam. This ensures data quality and reliable feedback is collected. Finally, the collected data is analyzed chronologically. This allows for understanding what kind of evaluations were given at what point in time. For example, analyzing evaluations on the day a particular event was held, or restaurant evaluations at specific times, can identify trending points. In this way, real-time analysis is possible while maintaining data freshness. This allows interactive survey platforms to collect user feedback quickly and use it to improve businesses and public services.

[0029] The interactive survey platform according to this embodiment comprises a reception unit, an analysis unit, a filtering unit, a time-series analysis unit, and a reward unit. The reception unit receives user feedback. User feedback includes, but is not limited to, text, audio, and images. The reception unit receives feedback using, for example, a device such as a smartphone or tablet. The reception unit can also receive questions based on the user's current location. For example, it can obtain the user's current location using GPS data and ask questions related to that location. The analysis unit analyzes the feedback received by the reception unit. The analysis is performed using, for example, statistical analysis or machine learning algorithms, but is not limited to these. For example, the analysis unit analyzes the content of the feedback to help improve businesses and public services. The analysis unit can also adjust the level of detail of the analysis based on the importance of the feedback. For example, highly important feedback is analyzed in detail, while less important feedback is analyzed simply. The filtering unit filters out malicious posts based on the data analyzed by the analysis unit. Filtering is performed using, for example, spam detection algorithms or blacklists, but is not limited to these. For example, the filtering unit analyzes the content of posts and automatically detects and removes inappropriate content and spam. The time-series analysis unit analyzes the data filtered by the filtering unit in a time-series manner. The time-series analysis is performed based on, for example, time intervals and the algorithm used, but is not limited to such examples. For example, the time-series analysis unit analyzes evaluations during specific time periods and understands evaluations for each time series. The reward unit provides rewards to users based on the analysis results obtained by the time-series analysis unit. The rewards are provided in the form of, for example, points, coupons, cash, etc., but is not limited to such examples. For example, the reward unit provides discount coupons to users who answer a certain number of surveys. This enables the interactive survey platform according to the embodiment to efficiently receive, analyze, filter, perform time-series analysis on, and provide rewards for user feedback.Some or all of the above-described processes in the reception department may be performed using AI, for example, or without AI. For example, the reception department can input user feedback into the AI ​​and have the AI ​​perform the analysis of the feedback. Some or all of the above-described processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input the content of the feedback into the AI ​​and have the AI ​​perform the analysis of the feedback. Some or all of the above-described processes in the filtering department may be performed using AI, for example, or without AI. For example, the filtering department can input the content of the posts into the AI ​​and have the AI ​​perform the detection of inappropriate content and spam. Some or all of the above-described processes in the time-series analysis department may be performed using AI, for example, or without AI. For example, the time-series analysis department can input filtered data into the AI ​​and have the AI ​​perform the time-series analysis. Some or all of the above-described processes in the reward department may be performed using AI, for example, or without AI. For example, the reward unit can input the analysis results obtained by the time-series analysis unit into the AI, and have the AI ​​execute the provision of rewards.

[0030] The reception desk receives user feedback. User feedback includes, but is not limited to, text, audio, and images. The reception desk receives feedback using devices such as smartphones and tablets. Specifically, when a user answers a survey through a smartphone application, it provides text input fields, voice input functions, and image upload functions using the camera. The reception desk can also receive questions based on the user's current location. For example, it can obtain the user's current location using GPS data and ask questions related to that location. This allows for the provision of location-related feedback when the user is in a specific place. Furthermore, the reception desk can utilize sensor information from the user's device to dynamically generate questions tailored to the user's situation and environment. For example, if the user is outdoors, it can ask questions about the weather and ambient noise levels. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input user feedback into AI and have the AI ​​analyze the feedback. The AI ​​analyzes text feedback using natural language processing technology, converts voice feedback to text using speech recognition technology, and analyzes image feedback using image recognition technology. This allows the reception desk to efficiently receive diverse feedback from users and prepare it for analysis.

[0031] The analysis department analyzes the feedback received by the reception department. This analysis is performed using, but is not limited to, statistical analysis or machine learning algorithms. Specifically, the analysis department analyzes the content of the feedback to improve businesses and public services. For example, for text feedback, it uses natural language processing techniques to perform sentiment analysis and topic modeling to extract user opinions and emotions. For voice feedback, it uses speech recognition technology to convert it to text, and then performs the same analysis as for text feedback. For image feedback, it uses image recognition technology to analyze the image content and extract specific patterns and features. The analysis department can also adjust the level of detail in the analysis based on the importance of the feedback. For example, highly important feedback is analyzed in detail, while less important feedback is analyzed simply. The importance can be determined using algorithms that comprehensively evaluate user attribute information, past feedback history, and the content of the feedback. Furthermore, the analysis department can cluster the content of the feedback to identify common themes and problems. This allows for the grouping and analysis of feedback related to specific themes, enabling efficient extraction of areas for improvement. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the content of the feedback into the AI ​​and have the AI ​​perform the analysis of the feedback. The AI ​​can learn the patterns of the feedback using machine learning algorithms and improve the accuracy of future feedback analysis.

[0032] The filtering unit filters out malicious posts based on data analyzed by the analysis unit. Filtering is performed using, for example, spam detection algorithms or blacklists, but is not limited to these examples. Specifically, the filtering unit analyzes the content of posts and automatically detects and removes inappropriate content and spam. For example, for text feedback, it uses natural language processing technology to detect inappropriate words and phrases and spam filtering algorithms to identify spam posts. For voice feedback, it uses speech recognition technology to convert it to text and then performs the same filtering as for text feedback. For image feedback, it uses image recognition technology to detect inappropriate images and spam images. The filtering unit can also predict malicious posts based on a user's past posting history and behavioral patterns. For example, posts from users who have previously made spam posts are filtered more strictly. The filtering unit can also accept reports from users and perform manual filtering. This allows for the removal of inappropriate posts that the system could not automatically detect. Some or all of the above-described processes in the filtering unit may be performed using, for example, AI, or not using AI. For example, the filtering unit can input the content of posts into the AI ​​and have the AI ​​detect inappropriate content and spam. The AI ​​can use machine learning algorithms to learn patterns of inappropriate posts and improve the accuracy of filtering.

[0033] The time series analysis unit analyzes the data filtered by the filtering unit in a time series. Time series analysis is performed based on, for example, time intervals and the algorithm used, but is not limited to such examples. Specifically, the time series analysis unit analyzes evaluations in specific time periods and understands evaluations for each time series. For example, it analyzes feedback during a specific campaign period and understands fluctuations in user reactions and evaluations during that period. The time series analysis unit can also perform predictive analysis based on historical data to understand long-term trends. For example, it can predict future user evaluations and reactions based on past feedback data and take countermeasures in advance. The time series analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data. For example, it can detect sudden fluctuations in evaluations or an abnormal increase in the number of posts in a specific time period and issue warnings early. Some or all of the above processes in the time series analysis unit may be performed using, for example, AI, or not using AI. For example, the time series analysis unit can input filtered data into AI and have the AI ​​perform the time series analysis. AI can learn patterns in time-series data and predict future trends and anomalies with high accuracy. This allows the time-series analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0034] The rewards unit provides rewards to users based on the analysis results obtained by the time-series analysis unit. Rewards may be provided in the form of points, coupons, cash, etc., but are not limited to these examples. Specifically, the rewards unit may provide discount coupons to users who answer a certain number of surveys. For example, if a user answers 10 surveys, they will be given a discount coupon that can be used on their next purchase. The rewards unit may also adjust rewards based on the quality and importance of user feedback. For example, users who provide particularly helpful feedback will be given higher value rewards. The rewards unit may also provide individually customized rewards based on the user's behavior history and the content of their feedback. For example, users who have provided a lot of feedback on a particular product in the past will be given benefits related to that product. Some or all of the above processing in the rewards unit may be performed using AI, for example, or not using AI. For example, the rewards unit may input the analysis results obtained by the time-series analysis unit into AI and have the AI ​​provide rewards. The AI ​​can learn the user's behavior patterns and the content of their feedback and automatically select the optimal reward. This allows the rewards system to increase user motivation and encourage the provision of continuous feedback.

[0035] The filtering unit can analyze posted content and automatically detect and remove inappropriate content and spam. For example, the filtering unit can analyze posted content and detect inappropriate content such as violent language or discriminatory remarks. The filtering unit can also detect spam such as repeated posts of the same content or posts for advertising purposes. For example, the filtering unit can automatically detect and remove spam using a spam detection algorithm. The filtering unit can also detect inappropriate content and spam using a blacklist. For example, the filtering unit can detect inappropriate content and spam based on keywords and phrases registered in a blacklist. This ensures data quality by automatically removing inappropriate content and spam. Some or all of the above processing in the filtering unit may be performed using AI, for example, or without AI. For example, the filtering unit can input posted content into AI and have the AI ​​perform the detection of inappropriate content and spam.

[0036] The time series analysis unit can analyze evaluations during specific time periods. For example, the time series analysis unit can analyze evaluations during specific time periods to understand evaluations such as peak and off-peak times. For example, the time series analysis unit can analyze evaluations on days when a specific event is held to understand the evaluation of that event. The time series analysis unit can also analyze restaurant evaluations during specific time periods to understand the evaluations for those times. For example, the time series analysis unit can identify buzz points based on evaluations during specific time periods. This allows for understanding evaluations for each time series by analyzing evaluations during specific time periods. Some or all of the above processing in the time series analysis unit may be performed using AI, for example, or without AI. For example, the time series analysis unit can input filtered data into AI and have the AI ​​perform the time series analysis.

[0037] The rewards unit can offer users points or coupons. For example, the rewards unit can offer discount coupons to users who complete a certain number of surveys. The rewards unit can also award users points, which they can then use to earn rewards. For example, the rewards unit can offer specific products or services at a discounted price to users who accumulate a certain number of points. The rewards unit can also offer users cash. For example, the rewards unit can offer cash to users who complete a certain number of surveys. This increases user engagement by offering users points and coupons. Some or all of the above processes in the rewards unit may be performed using AI, for example, or not. For example, the rewards unit can input the analysis results obtained by the time series analysis unit into the AI ​​and have the AI ​​perform the reward provision.

[0038] The reception desk can receive questions based on the user's current location. For example, the reception desk can obtain the user's current location using GPS data and ask questions related to that location. For example, if the user is in a specific store, the reception desk can ask questions about that store. The reception desk can also ask questions about an event if the user is at an event venue. For example, if the reception desk is in a tourist spot, the reception desk can ask questions about that tourist spot. By receiving questions based on the user's current location, more relevant feedback can be obtained. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's current location data into AI and generate relevant questions.

[0039] The analytics department can analyze collected data to improve businesses and public services. For example, the analytics department can analyze restaurant rating data to improve menus and services. It can also analyze event feedback data to inform the planning of future events. For instance, the analytics department analyzes data to improve customer satisfaction and service efficiency. In this way, analyzing collected data can be used to improve businesses and public services. Some or all of the above-described processes in the analytics department may be performed using AI, or not. For example, the analytics department can input collected data into an AI and have the AI ​​perform analyses that help improve businesses and public services.

[0040] The reception desk can analyze the user's past feedback history and select the most appropriate question format. For example, the reception desk may prioritize question formats that the user has preferred to answer in the past. It can also exclude question formats that the user has avoided in the past. For example, the reception desk may analyze the user's past answer patterns and select the most effective question format. In this way, the optimal question format can be selected by analyzing the user's past feedback history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past feedback history into AI and have the AI ​​select the optimal question format.

[0041] The reception desk can customize questions based on the user's current activity status when receiving inquiries. For example, if the user is on the move, the reception desk can ask questions that can be answered quickly. Alternatively, if the user is on a break, the reception desk can ask questions that require more detailed feedback. For example, if the user is in a specific location, the reception desk can ask questions related to that location. By customizing questions based on the user's current activity status, more appropriate feedback can be obtained. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's current activity status data into the AI ​​and have the AI ​​customize the questions.

[0042] The reception desk can prioritize receiving questions that are highly relevant, taking into account the user's geographical location. For example, if the user is at a specific store, the reception desk will prioritize questions related to that store. Similarly, if the user is at an event venue, the reception desk can prioritize questions related to that event. For example, if the user is at a tourist destination, the reception desk will prioritize questions related to that tourist destination. This allows the reception desk to prioritize receiving questions that are highly relevant by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location into AI and generate highly relevant questions.

[0043] The reception desk can analyze the user's social media activity when receiving a question and accept relevant questions. For example, the reception desk can ask questions based on what the user has shared on social media. It can also ask questions related to accounts the user follows. For example, the reception desk can ask questions related to online events the user is participating in. In this way, relevant questions can be accepted by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into AI and generate relevant questions.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the feedback during the analysis. For example, the analysis unit will analyze high-importance feedback in detail, while analyzing low-importance feedback simply. For example, the analysis unit can allocate analysis resources according to importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the feedback. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input feedback importance data into AI and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the feedback category during analysis. For example, the analysis unit can apply a specific algorithm to restaurant rating data. It can also apply a different algorithm to event feedback data. For example, the analysis unit can apply yet another algorithm to product reviews. By applying different analysis algorithms depending on the feedback category, more accurate analysis can be performed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input feedback category data into an AI and have the AI ​​perform the application of different analysis algorithms.

[0046] The analysis department can prioritize analyses based on the timing of feedback submissions. For example, the analysis department might prioritize analyzing the most recent feedback. It could also prioritize analyzing feedback concentrated within a specific period. For instance, the analysis department could allocate analysis resources according to the timing of feedback submissions. This allows for efficient analysis by prioritizing analyses based on the timing of feedback submissions. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department could input feedback submission timing data into an AI and have the AI ​​determine the analysis priorities.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the feedback during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant feedback. It can also postpone the analysis of less relevant feedback. For example, the analysis unit can allocate analysis resources according to the relevance of the feedback. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the feedback. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevance data of the feedback into the AI ​​and have the AI ​​perform the adjustment of the analysis order.

[0048] The filtering unit can improve the accuracy of filtering by considering the interrelationships of feedback during the filtering process. For example, the filtering unit increases reliability when the content of one feedback is consistent with other feedback. Conversely, the filtering unit can decrease reliability when the content of another feedback is contradictory. For example, the filtering unit analyzes the interrelationships of feedback to improve the accuracy of filtering. This improves the accuracy of filtering by considering the interrelationships of feedback. Some or all of the above processing in the filtering unit may be performed using AI, for example, or without AI. For example, the filtering unit can input the interrelationship data of feedback into AI and have the AI ​​perform the filtering accuracy improvement.

[0049] The filtering unit can perform filtering while considering the attribute information of the feedback submitter. For example, the filtering unit can filter based on the submitter's trustworthiness. The filtering unit can also filter based on the submitter's past feedback history. For example, the filtering unit can analyze the submitter's attribute information to improve the accuracy of filtering. This improves the accuracy of filtering by considering the attribute information of the feedback submitter. Some or all of the above processing in the filtering unit may be performed using AI, for example, or without AI. For example, the filtering unit can input the submitter's attribute information data into AI and have the AI ​​perform the filtering.

[0050] The filtering unit can perform filtering while considering the geographical distribution of feedback. For example, the filtering unit can prioritize filtering feedback from a specific region. The filtering unit can also exclude geographically biased feedback. For example, the filtering unit can improve the accuracy of filtering by considering the geographical distribution. This improves the accuracy of filtering by considering the geographical distribution of feedback. Some or all of the above processing in the filtering unit may be performed using AI, for example, or without AI. For example, the filtering unit can input the geographical distribution data of the feedback into AI and have the AI ​​perform the filtering.

[0051] The filtering unit can improve the accuracy of filtering by referring to relevant literature in the feedback during the filtering process. For example, the filtering unit can increase reliability if the content of the feedback matches the relevant literature. Conversely, the filtering unit can decrease reliability if the content of the feedback contradicts the relevant literature. For example, the filtering unit improves the accuracy of filtering by referring to relevant literature. Thus, the accuracy of filtering is improved by referring to relevant literature in the feedback. Some or all of the above processing in the filtering unit may be performed using AI, for example, or without using AI. For example, the filtering unit can input the content of the feedback and the relevant literature into the AI ​​and have the AI ​​perform the filtering accuracy improvement.

[0052] The time series analysis unit can predict current trends by referring to past data during time series analysis. For example, the time series analysis unit predicts current trends based on past data. The time series analysis unit can also predict future trends by analyzing past data. For example, the time series analysis unit refers to past data to understand current trends. This allows it to predict current trends by referring to past data. Some or all of the above processes in the time series analysis unit may be performed using AI, for example, or without AI. For example, the time series analysis unit can input past data into AI and have the AI ​​perform the prediction of current trends.

[0053] The time series analysis unit can apply different analytical methods to each category of feedback during time series analysis. For example, the time series analysis unit can apply a specific analytical method to restaurant rating data. It can also apply a different analytical method to event feedback data. For example, the time series analysis unit can apply yet another analytical method to product reviews. By applying different analytical methods to each category of feedback, more accurate analysis can be performed. Some or all of the above processing in the time series analysis unit may be performed using AI, for example, or without AI. For example, the time series analysis unit can input feedback category data into AI and have the AI ​​perform the application of different analytical methods.

[0054] The time series analysis unit can analyze changes in the time series based on the timing of feedback submissions. For example, the time series analysis unit can prioritize the analysis of feedback concentrated in a specific period. The time series analysis unit can also grasp changes in the time series according to the timing of feedback submissions. For example, the time series analysis unit can predict changes in the time series based on the timing of feedback submissions. This allows for more appropriate analysis by analyzing changes in the time series based on the timing of feedback submissions. Some or all of the above processes in the time series analysis unit may be performed using AI, for example, or without AI. For example, the time series analysis unit can input feedback submission timing data into AI and have the AI ​​perform the analysis of changes in the time series.

[0055] The time series analysis unit can analyze time series by referring to relevant market data of feedback during time series analysis. For example, the time series analysis unit can analyze time series changes of feedback based on market data. The time series analysis unit can also predict time series changes of feedback by referring to market data. For example, the time series analysis unit can grasp time series changes of feedback based on market data. This allows for a more accurate analysis of time series changes by referring to relevant market data of feedback. Some or all of the above processing in the time series analysis unit may be performed using AI, for example, or without AI. For example, the time series analysis unit can input relevant market data into AI and have AI perform the analysis of time series changes.

[0056] The rewards unit can analyze the user's past feedback history to select the optimal reward when providing rewards. For example, the rewards unit may prioritize rewards that the user has preferred in the past. The rewards unit can also exclude rewards that the user has avoided in the past. For example, the rewards unit analyzes the user's past feedback history and selects the most effective reward. In this way, the optimal reward can be selected by analyzing the user's past feedback history. Some or all of the above processing in the rewards unit may be performed using AI, for example, or not using AI. For example, the rewards unit can input the user's past feedback history data into AI and have the AI ​​perform the selection of the optimal reward.

[0057] The rewards unit can customize the type of reward based on the user's current activity when providing rewards. For example, if the user is on the move, the rewards unit can provide a mobile coupon. Alternatively, if the user is on a break, the rewards unit can provide a reward after requesting detailed feedback. For example, if the user is in a specific location, the rewards unit can provide a location-related reward. This allows for the provision of more appropriate rewards by customizing the type of reward based on the user's current activity. Some or all of the above processing in the rewards unit may be performed using AI, for example, or not. For example, the rewards unit can input the user's current activity data into the AI ​​and have the AI ​​customize the type of reward.

[0058] The rewards unit can provide the most suitable reward by considering the user's geographical location when providing rewards. For example, if the user is at a specific store, the rewards unit can provide a coupon that can be used at that store. The rewards unit can also provide rewards related to an event if the user is at an event venue. For example, if the rewards unit is at a tourist destination, it can provide rewards related to that tourist destination. This allows for the provision of the most suitable reward by considering the user's geographical location. Some or all of the above processing in the rewards unit may be performed using AI, or not. For example, the rewards unit can input the user's geographical location data into an AI and have the AI ​​provide the most suitable reward.

[0059] The rewards department can analyze a user's social media activity and suggest reward types when providing rewards. For example, the rewards department can provide rewards based on content shared by the user on social media. It can also provide rewards related to accounts that the user follows. For example, the rewards department can provide rewards related to online events that the user participates in. This allows the department to suggest the most suitable reward type by analyzing the user's social media activity. Some or all of the above processing in the rewards department may be performed using AI, for example, or not. For example, the rewards department can input the user's social media activity data into an AI and have the AI ​​suggest reward types.

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

[0061] The reception desk can analyze the user's past feedback history and select the most appropriate question format. For example, it can prioritize question formats that the user has preferred to answer in the past. It can also exclude question formats that the user has avoided in the past. Furthermore, it can analyze the user's past answer patterns and select the most effective question format. In this way, the optimal question format can be selected by analyzing the user's past feedback history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past feedback history into AI and have the AI ​​select the optimal question format.

[0062] The reception desk can customize questions based on the user's current activity when receiving inquiries. For example, if the user is on the move, it can ask questions that can be answered quickly. If the user is on a break, it can ask questions that require more detailed feedback. Furthermore, if the user is in a specific location, it can ask questions related to that location. By customizing questions based on the user's current activity, more appropriate feedback can be obtained. Some or all of the above processing at the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's current activity data into the AI ​​and have the AI ​​customize the questions.

[0063] The analysis unit can adjust the level of detail of the analysis based on the importance of the feedback during the analysis. For example, highly important feedback can be analyzed in detail, while less important feedback can be analyzed simply. Furthermore, analysis resources can be allocated according to importance. This allows for efficient analysis by adjusting the level of detail based on the importance of the feedback. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input feedback importance data into AI and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0064] The filtering unit can improve the accuracy of filtering by considering the interrelationships of feedback during the filtering process. For example, reliability can be increased if the content of one feedback is consistent with other feedback. Conversely, reliability can be decreased if the content of another feedback is contradictory. Furthermore, the filtering unit can analyze the interrelationships of feedback to improve filtering accuracy. Thus, by considering the interrelationships of feedback, filtering accuracy is improved. Some or all of the above processing in the filtering unit may be performed using AI, for example, or without AI. For example, the filtering unit can input the interrelationship data of feedback into AI and have the AI ​​perform the filtering accuracy improvement.

[0065] The time series analysis unit can predict current trends by referring to past data during time series analysis. For example, it can predict current trends based on past data. It can also analyze past data and predict future trends. Furthermore, it can understand current trends by referring to past data. In this way, it can predict current trends by referring to past data. Some or all of the above processes in the time series analysis unit may be performed using AI, for example, or without AI. For example, the time series analysis unit can input past data into AI and have the AI ​​perform the prediction of current trends.

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

[0067] Step 1: The reception desk receives user feedback. User feedback can include text, audio, and images. The reception desk accepts feedback using devices such as smartphones and tablets. The reception desk can also accept questions based on the user's current location. For example, it can use GPS data to obtain the user's current location and ask questions related to that location. Step 2: The analysis department analyzes the feedback received by the reception department. The analysis is performed using statistical analysis and machine learning algorithms. For example, the content of the feedback is analyzed to help improve the business or public services. The level of detail of the analysis can also be adjusted based on the importance of the feedback. High-priority feedback is analyzed in detail, while low-priority feedback is analyzed simply. Step 3: The filtering unit filters out malicious posts based on the data analyzed by the analysis unit. Filtering is performed using spam detection algorithms and blacklists. For example, it analyzes the content of posts and automatically detects and removes inappropriate content and spam. Step 4: The time series analysis unit analyzes the data filtered by the filtering unit in a time series manner. Time series analysis is performed based on the time interval and the algorithm used. For example, it analyzes evaluations during a specific time period to understand evaluations for each time series. Step 5: The rewards department provides rewards to users based on the analysis results obtained by the time-series analysis department. Rewards are provided in the form of points, coupons, cash, etc. For example, a discount coupon may be offered to a user who answers a certain number of surveys.

[0068] (Example of form 2) An interactive survey platform according to an embodiment of the present invention is a system that allows users to provide instant feedback on their everyday experiences. This system allows users to earn rewards by answering location-based questions and simple question-and-answer surveys about their experiences at that moment. The collected data is used to improve businesses and public services, and malicious posts are filtered by AI. The data is also analyzed chronologically to understand what kind of evaluations were given at what point in time, and is used for analyzing trending topics and other insights. For example, users answer location-based questions and simple question-and-answer surveys about their experiences at that moment. Users answer the surveys using devices such as smartphones or tablets. For example, users can provide real-time ratings of restaurants they have visited or feedback on events they have attended. Next, users are rewarded for answering the surveys. Rewards are provided in the form of points or coupons to increase user engagement. For example, users may receive discount coupons after answering a certain number of surveys. The collected data is used to improve businesses and public services. For example, analyzing restaurant rating data can lead to improvements in menus and services. Furthermore, analyzing event feedback data can be used to plan future events. In addition, malicious posts are filtered by AI. The AI ​​analyzes post content, automatically detecting and removing inappropriate material and spam. This ensures data quality and reliable feedback is collected. Finally, the collected data is analyzed chronologically. This allows for understanding what kind of evaluations were given at what point in time. For example, analyzing evaluations on the day a particular event was held, or restaurant evaluations at specific times, can identify trending points. In this way, real-time analysis is possible while maintaining data freshness. This allows interactive survey platforms to collect user feedback quickly and use it to improve businesses and public services.

[0069] The interactive survey platform according to this embodiment comprises a reception unit, an analysis unit, a filtering unit, a time-series analysis unit, and a reward unit. The reception unit receives user feedback. User feedback includes, but is not limited to, text, audio, and images. The reception unit receives feedback using, for example, a device such as a smartphone or tablet. The reception unit can also receive questions based on the user's current location. For example, it can obtain the user's current location using GPS data and ask questions related to that location. The analysis unit analyzes the feedback received by the reception unit. The analysis is performed using, for example, statistical analysis or machine learning algorithms, but is not limited to these. For example, the analysis unit analyzes the content of the feedback to help improve businesses and public services. The analysis unit can also adjust the level of detail of the analysis based on the importance of the feedback. For example, highly important feedback is analyzed in detail, while less important feedback is analyzed simply. The filtering unit filters out malicious posts based on the data analyzed by the analysis unit. Filtering is performed using, for example, spam detection algorithms or blacklists, but is not limited to these. For example, the filtering unit analyzes the content of posts and automatically detects and removes inappropriate content and spam. The time-series analysis unit analyzes the data filtered by the filtering unit in a time-series manner. The time-series analysis is performed based on, for example, time intervals and the algorithm used, but is not limited to such examples. For example, the time-series analysis unit analyzes evaluations during specific time periods and understands evaluations for each time series. The reward unit provides rewards to users based on the analysis results obtained by the time-series analysis unit. The rewards are provided in the form of, for example, points, coupons, cash, etc., but is not limited to such examples. For example, the reward unit provides discount coupons to users who answer a certain number of surveys. This enables the interactive survey platform according to the embodiment to efficiently receive, analyze, filter, perform time-series analysis on, and provide rewards for user feedback.Some or all of the above-described processes in the reception department may be performed using AI, for example, or without AI. For example, the reception department can input user feedback into the AI ​​and have the AI ​​perform the analysis of the feedback. Some or all of the above-described processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input the content of the feedback into the AI ​​and have the AI ​​perform the analysis of the feedback. Some or all of the above-described processes in the filtering department may be performed using AI, for example, or without AI. For example, the filtering department can input the content of the posts into the AI ​​and have the AI ​​perform the detection of inappropriate content and spam. Some or all of the above-described processes in the time-series analysis department may be performed using AI, for example, or without AI. For example, the time-series analysis department can input filtered data into the AI ​​and have the AI ​​perform the time-series analysis. Some or all of the above-described processes in the reward department may be performed using AI, for example, or without AI. For example, the reward unit can input the analysis results obtained by the time-series analysis unit into the AI, and have the AI ​​execute the provision of rewards.

[0070] The reception desk receives user feedback. User feedback includes, but is not limited to, text, audio, and images. The reception desk receives feedback using devices such as smartphones and tablets. Specifically, when a user answers a survey through a smartphone application, it provides text input fields, voice input functions, and image upload functions using the camera. The reception desk can also receive questions based on the user's current location. For example, it can obtain the user's current location using GPS data and ask questions related to that location. This allows for the provision of location-related feedback when the user is in a specific place. Furthermore, the reception desk can utilize sensor information from the user's device to dynamically generate questions tailored to the user's situation and environment. For example, if the user is outdoors, it can ask questions about the weather and ambient noise levels. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input user feedback into AI and have the AI ​​analyze the feedback. The AI ​​analyzes text feedback using natural language processing technology, converts voice feedback to text using speech recognition technology, and analyzes image feedback using image recognition technology. This allows the reception desk to efficiently receive diverse feedback from users and prepare it for analysis.

[0071] The analysis department analyzes the feedback received by the reception department. This analysis is performed using, but is not limited to, statistical analysis or machine learning algorithms. Specifically, the analysis department analyzes the content of the feedback to improve businesses and public services. For example, for text feedback, it uses natural language processing techniques to perform sentiment analysis and topic modeling to extract user opinions and emotions. For voice feedback, it uses speech recognition technology to convert it to text, and then performs the same analysis as for text feedback. For image feedback, it uses image recognition technology to analyze the image content and extract specific patterns and features. The analysis department can also adjust the level of detail in the analysis based on the importance of the feedback. For example, highly important feedback is analyzed in detail, while less important feedback is analyzed simply. The importance can be determined using algorithms that comprehensively evaluate user attribute information, past feedback history, and the content of the feedback. Furthermore, the analysis department can cluster the content of the feedback to identify common themes and problems. This allows for the grouping and analysis of feedback related to specific themes, enabling efficient extraction of areas for improvement. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the content of the feedback into the AI ​​and have the AI ​​perform the analysis of the feedback. The AI ​​can learn the patterns of the feedback using machine learning algorithms and improve the accuracy of future feedback analysis.

[0072] The filtering unit filters out malicious posts based on data analyzed by the analysis unit. Filtering is performed using, for example, spam detection algorithms or blacklists, but is not limited to these examples. Specifically, the filtering unit analyzes the content of posts and automatically detects and removes inappropriate content and spam. For example, for text feedback, it uses natural language processing technology to detect inappropriate words and phrases and spam filtering algorithms to identify spam posts. For voice feedback, it uses speech recognition technology to convert it to text and then performs the same filtering as for text feedback. For image feedback, it uses image recognition technology to detect inappropriate images and spam images. The filtering unit can also predict malicious posts based on a user's past posting history and behavioral patterns. For example, posts from users who have previously made spam posts are filtered more strictly. The filtering unit can also accept reports from users and perform manual filtering. This allows for the removal of inappropriate posts that the system could not automatically detect. Some or all of the above-described processes in the filtering unit may be performed using, for example, AI, or not using AI. For example, the filtering unit can input the content of posts into the AI ​​and have the AI ​​detect inappropriate content and spam. The AI ​​can use machine learning algorithms to learn patterns of inappropriate posts and improve the accuracy of filtering.

[0073] The time series analysis unit analyzes the data filtered by the filtering unit in a time series. Time series analysis is performed based on, for example, time intervals and the algorithm used, but is not limited to such examples. Specifically, the time series analysis unit analyzes evaluations in specific time periods and understands evaluations for each time series. For example, it analyzes feedback during a specific campaign period and understands fluctuations in user reactions and evaluations during that period. The time series analysis unit can also perform predictive analysis based on historical data to understand long-term trends. For example, it can predict future user evaluations and reactions based on past feedback data and take countermeasures in advance. The time series analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data. For example, it can detect sudden fluctuations in evaluations or an abnormal increase in the number of posts in a specific time period and issue warnings early. Some or all of the above processes in the time series analysis unit may be performed using, for example, AI, or not using AI. For example, the time series analysis unit can input filtered data into AI and have the AI ​​perform the time series analysis. AI can learn patterns in time-series data and predict future trends and anomalies with high accuracy. This allows the time-series analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0074] The rewards unit provides rewards to users based on the analysis results obtained by the time-series analysis unit. Rewards may be provided in the form of points, coupons, cash, etc., but are not limited to these examples. Specifically, the rewards unit may provide discount coupons to users who answer a certain number of surveys. For example, if a user answers 10 surveys, they will be given a discount coupon that can be used on their next purchase. The rewards unit may also adjust rewards based on the quality and importance of user feedback. For example, users who provide particularly helpful feedback will be given higher value rewards. The rewards unit may also provide individually customized rewards based on the user's behavior history and the content of their feedback. For example, users who have provided a lot of feedback on a particular product in the past will be given benefits related to that product. Some or all of the above processing in the rewards unit may be performed using AI, for example, or not using AI. For example, the rewards unit may input the analysis results obtained by the time-series analysis unit into AI and have the AI ​​provide rewards. The AI ​​can learn the user's behavior patterns and the content of their feedback and automatically select the optimal reward. This allows the rewards system to increase user motivation and encourage the provision of continuous feedback.

[0075] The filtering unit can analyze posted content and automatically detect and remove inappropriate content and spam. For example, the filtering unit can analyze posted content and detect inappropriate content such as violent language or discriminatory remarks. The filtering unit can also detect spam such as repeated posts of the same content or posts for advertising purposes. For example, the filtering unit can automatically detect and remove spam using a spam detection algorithm. The filtering unit can also detect inappropriate content and spam using a blacklist. For example, the filtering unit can detect inappropriate content and spam based on keywords and phrases registered in a blacklist. This ensures data quality by automatically removing inappropriate content and spam. Some or all of the above processing in the filtering unit may be performed using AI, for example, or without AI. For example, the filtering unit can input posted content into AI and have the AI ​​perform the detection of inappropriate content and spam.

[0076] The time series analysis unit can analyze evaluations during specific time periods. For example, the time series analysis unit can analyze evaluations during specific time periods to understand evaluations such as peak and off-peak times. For example, the time series analysis unit can analyze evaluations on days when a specific event is held to understand the evaluation of that event. The time series analysis unit can also analyze restaurant evaluations during specific time periods to understand the evaluations for those times. For example, the time series analysis unit can identify buzz points based on evaluations during specific time periods. This allows for understanding evaluations for each time series by analyzing evaluations during specific time periods. Some or all of the above processing in the time series analysis unit may be performed using AI, for example, or without AI. For example, the time series analysis unit can input filtered data into AI and have the AI ​​perform the time series analysis.

[0077] The rewards unit can offer users points or coupons. For example, the rewards unit can offer discount coupons to users who complete a certain number of surveys. The rewards unit can also award users points, which they can then use to earn rewards. For example, the rewards unit can offer specific products or services at a discounted price to users who accumulate a certain number of points. The rewards unit can also offer users cash. For example, the rewards unit can offer cash to users who complete a certain number of surveys. This increases user engagement by offering users points and coupons. Some or all of the above processes in the rewards unit may be performed using AI, for example, or not. For example, the rewards unit can input the analysis results obtained by the time series analysis unit into the AI ​​and have the AI ​​perform the reward provision.

[0078] The reception desk can receive questions based on the user's current location. For example, the reception desk can obtain the user's current location using GPS data and ask questions related to that location. For example, if the user is in a specific store, the reception desk can ask questions about that store. The reception desk can also ask questions about an event if the user is at an event venue. For example, if the reception desk is in a tourist spot, the reception desk can ask questions about that tourist spot. By receiving questions based on the user's current location, more relevant feedback can be obtained. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's current location data into AI and generate relevant questions.

[0079] The analytics department can analyze collected data to improve businesses and public services. For example, the analytics department can analyze restaurant rating data to improve menus and services. It can also analyze event feedback data to inform the planning of future events. For instance, the analytics department analyzes data to improve customer satisfaction and service efficiency. In this way, analyzing collected data can be used to improve businesses and public services. Some or all of the above-described processes in the analytics department may be performed using AI, or not. For example, the analytics department can input collected data into an AI and have the AI ​​perform analyses that help improve businesses and public services.

[0080] The reception desk can estimate the user's emotions and adjust the content and timing of questions based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize simple and short questions. Conversely, if the user is relaxed, the reception desk may ask questions that request detailed feedback. For example, if the user is in a hurry, the reception desk will ask one-word answer questions that can be answered immediately. By adjusting the content and timing of questions according to the user's emotions, more appropriate feedback can be obtained. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into AI and have the AI ​​adjust the content and timing of questions.

[0081] The reception desk can analyze the user's past feedback history and select the most appropriate question format. For example, the reception desk may prioritize question formats that the user has preferred to answer in the past. It can also exclude question formats that the user has avoided in the past. For example, the reception desk may analyze the user's past answer patterns and select the most effective question format. In this way, the optimal question format can be selected by analyzing the user's past feedback history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past feedback history into AI and have the AI ​​select the optimal question format.

[0082] The reception desk can customize questions based on the user's current activity status when receiving inquiries. For example, if the user is on the move, the reception desk can ask questions that can be answered quickly. Alternatively, if the user is on a break, the reception desk can ask questions that require more detailed feedback. For example, if the user is in a specific location, the reception desk can ask questions related to that location. By customizing questions based on the user's current activity status, more appropriate feedback can be obtained. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's current activity status data into the AI ​​and have the AI ​​customize the questions.

[0083] The reception desk can estimate the user's emotions and prioritize questions based on those emotions. For example, if the user is stressed, the reception desk may postpone less important questions. Conversely, if the user is relaxed, the reception desk may prioritize more important questions. For example, if the user is in a hurry, the reception desk may prioritize questions that can be answered immediately. This allows for more appropriate feedback by prioritizing questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into an AI and have the AI ​​determine the priority of questions.

[0084] The reception desk can prioritize receiving questions that are highly relevant, taking into account the user's geographical location. For example, if the user is at a specific store, the reception desk will prioritize questions related to that store. Similarly, if the user is at an event venue, the reception desk can prioritize questions related to that event. For example, if the user is at a tourist destination, the reception desk will prioritize questions related to that tourist destination. This allows the reception desk to prioritize receiving questions that are highly relevant by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location into AI and generate highly relevant questions.

[0085] The reception desk can analyze the user's social media activity when receiving a question and accept relevant questions. For example, the reception desk can ask questions based on what the user has shared on social media. It can also ask questions related to accounts the user follows. For example, the reception desk can ask questions related to online events the user is participating in. In this way, relevant questions can be accepted by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into AI and generate relevant questions.

[0086] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit can perform a simple analysis. If the user is relaxed, the analysis unit can also perform a detailed analysis. For example, if the user is in a hurry, the analysis unit can perform a rapid analysis. This allows for more appropriate analysis by adjusting the data analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​adjust the data analysis method.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the feedback during the analysis. For example, the analysis unit will analyze high-importance feedback in detail, while analyzing low-importance feedback simply. For example, the analysis unit can allocate analysis resources according to importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the feedback. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input feedback importance data into AI and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the feedback category during analysis. For example, the analysis unit can apply a specific algorithm to restaurant rating data. It can also apply a different algorithm to event feedback data. For example, the analysis unit can apply yet another algorithm to product reviews. By applying different analysis algorithms depending on the feedback category, more accurate analysis can be performed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input feedback category data into an AI and have the AI ​​perform the application of different analysis algorithms.

[0089] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a concise display method. It can also provide a detailed display method if the user is relaxed. For example, if the user is in a hurry, the analysis unit can provide a concise display method. This allows for more appropriate display by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into an AI and have the AI ​​adjust the display method of the analysis results.

[0090] The analysis department can prioritize analyses based on the timing of feedback submissions. For example, the analysis department might prioritize analyzing the most recent feedback. It could also prioritize analyzing feedback concentrated within a specific period. For instance, the analysis department could allocate analysis resources according to the timing of feedback submissions. This allows for efficient analysis by prioritizing analyses based on the timing of feedback submissions. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department could input feedback submission timing data into an AI and have the AI ​​determine the analysis priorities.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the feedback during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant feedback. It can also postpone the analysis of less relevant feedback. For example, the analysis unit can allocate analysis resources according to the relevance of the feedback. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the feedback. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevance data of the feedback into the AI ​​and have the AI ​​perform the adjustment of the analysis order.

[0092] The filtering unit can estimate the user's emotions and adjust the filtering criteria based on the estimated emotions. For example, if the user is stressed, the filtering unit can apply strict filtering criteria. Conversely, if the user is relaxed, the filtering unit can apply flexible filtering criteria. For example, if the user is in a hurry, the filtering unit can perform rapid filtering. This allows for more appropriate filtering by adjusting the filtering criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the filtering unit may be performed using AI or not. For example, the filtering unit can input user emotion data into an AI and have the AI ​​adjust the filtering criteria.

[0093] The filtering unit can improve the accuracy of filtering by considering the interrelationships of feedback during the filtering process. For example, the filtering unit increases reliability when the content of one feedback is consistent with other feedback. Conversely, the filtering unit can decrease reliability when the content of another feedback is contradictory. For example, the filtering unit analyzes the interrelationships of feedback to improve the accuracy of filtering. This improves the accuracy of filtering by considering the interrelationships of feedback. Some or all of the above processing in the filtering unit may be performed using AI, for example, or without AI. For example, the filtering unit can input the interrelationship data of feedback into AI and have the AI ​​perform the filtering accuracy improvement.

[0094] The filtering unit can perform filtering while considering the attribute information of the feedback submitter. For example, the filtering unit can filter based on the submitter's trustworthiness. The filtering unit can also filter based on the submitter's past feedback history. For example, the filtering unit can analyze the submitter's attribute information to improve the accuracy of filtering. This improves the accuracy of filtering by considering the attribute information of the feedback submitter. Some or all of the above processing in the filtering unit may be performed using AI, for example, or without AI. For example, the filtering unit can input the submitter's attribute information data into AI and have the AI ​​perform the filtering.

[0095] The filtering unit can estimate the user's emotions and adjust the order in which the filtering results are displayed based on the estimated emotions. For example, if the user is stressed, the filtering unit may postpone displaying less important feedback. Conversely, if the user is relaxed, the filtering unit may prioritize displaying more important feedback. For example, if the user is in a hurry, the filtering unit may prioritize displaying feedback that requires immediate attention. This allows for more appropriate display by adjusting the order in which the filtering results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the filtering unit may be performed using AI, or not using AI. For example, the filtering unit can input user emotion data into an AI and have the AI ​​adjust the display order of the filtering results.

[0096] The filtering unit can perform filtering while considering the geographical distribution of feedback. For example, the filtering unit can prioritize filtering feedback from a specific region. The filtering unit can also exclude geographically biased feedback. For example, the filtering unit can improve the accuracy of filtering by considering the geographical distribution. This improves the accuracy of filtering by considering the geographical distribution of feedback. Some or all of the above processing in the filtering unit may be performed using AI, for example, or without AI. For example, the filtering unit can input the geographical distribution data of the feedback into AI and have the AI ​​perform the filtering.

[0097] The filtering unit can improve the accuracy of filtering by referring to relevant literature in the feedback during the filtering process. For example, the filtering unit can increase reliability if the content of the feedback matches the relevant literature. Conversely, the filtering unit can decrease reliability if the content of the feedback contradicts the relevant literature. For example, the filtering unit improves the accuracy of filtering by referring to relevant literature. Thus, the accuracy of filtering is improved by referring to relevant literature in the feedback. Some or all of the above processing in the filtering unit may be performed using AI, for example, or without using AI. For example, the filtering unit can input the content of the feedback and the relevant literature into the AI ​​and have the AI ​​perform the filtering accuracy improvement.

[0098] The time series analysis unit can estimate the user's emotions and adjust the display method of the time series analysis based on the estimated user emotions. For example, if the user is stressed, the time series analysis unit can provide a concise display method. It can also provide a detailed display method if the user is relaxed. For example, if the user is in a hurry, the time series analysis unit can provide a concise display method. This allows for a more appropriate display by adjusting the time series analysis display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the time series analysis unit may be performed using AI, or not. For example, the time series analysis unit can input user emotion data into an AI and have the AI ​​adjust the display method of the time series analysis.

[0099] The time series analysis unit can predict current trends by referring to past data during time series analysis. For example, the time series analysis unit predicts current trends based on past data. The time series analysis unit can also predict future trends by analyzing past data. For example, the time series analysis unit refers to past data to understand current trends. This allows it to predict current trends by referring to past data. Some or all of the above processes in the time series analysis unit may be performed using AI, for example, or without AI. For example, the time series analysis unit can input past data into AI and have the AI ​​perform the prediction of current trends.

[0100] The time series analysis unit can apply different analytical methods to each category of feedback during time series analysis. For example, the time series analysis unit can apply a specific analytical method to restaurant rating data. It can also apply a different analytical method to event feedback data. For example, the time series analysis unit can apply yet another analytical method to product reviews. By applying different analytical methods to each category of feedback, more accurate analysis can be performed. Some or all of the above processing in the time series analysis unit may be performed using AI, for example, or without AI. For example, the time series analysis unit can input feedback category data into AI and have the AI ​​perform the application of different analytical methods.

[0101] The time series analysis unit can estimate the user's emotions and adjust the importance of the time series analysis based on the estimated user emotions. For example, if the user is stressed, the time series analysis unit will postpone analyzing data of lower importance. Conversely, if the user is relaxed, the time series analysis unit can prioritize analyzing data of higher importance. For example, if the user is in a hurry, the time series analysis unit will prioritize analyzing data that requires immediate attention. This allows for more appropriate analysis by adjusting the importance of the time series analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the time series analysis unit may be performed using AI, or not using AI. For example, the time series analysis unit can input user emotion data into AI and have the AI ​​adjust the importance of the time series analysis.

[0102] The time series analysis unit can analyze changes in the time series based on the timing of feedback submissions. For example, the time series analysis unit can prioritize the analysis of feedback concentrated in a specific period. The time series analysis unit can also grasp changes in the time series according to the timing of feedback submissions. For example, the time series analysis unit can predict changes in the time series based on the timing of feedback submissions. This allows for more appropriate analysis by analyzing changes in the time series based on the timing of feedback submissions. Some or all of the above processes in the time series analysis unit may be performed using AI, for example, or without AI. For example, the time series analysis unit can input feedback submission timing data into AI and have the AI ​​perform the analysis of changes in the time series.

[0103] The time series analysis unit can analyze time series by referring to relevant market data of feedback during time series analysis. For example, the time series analysis unit can analyze time series changes of feedback based on market data. The time series analysis unit can also predict time series changes of feedback by referring to market data. For example, the time series analysis unit can grasp time series changes of feedback based on market data. This allows for a more accurate analysis of time series changes by referring to relevant market data of feedback. Some or all of the above processing in the time series analysis unit may be performed using AI, for example, or without AI. For example, the time series analysis unit can input relevant market data into AI and have AI perform the analysis of time series changes.

[0104] The reward unit can estimate the user's emotions and adjust the method of providing rewards based on the estimated emotions. For example, if the user is stressed, the reward unit can provide a reward immediately. Alternatively, if the user is relaxed, the reward unit can request detailed feedback before providing a reward. For example, if the user is in a hurry, the reward unit can provide a reward with simple feedback. This allows for more appropriate rewards to be provided by adjusting the method of providing rewards according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reward unit may be performed using AI or not. For example, the reward unit can input user emotion data into an AI and have the AI ​​adjust the method of providing rewards.

[0105] The rewards unit can analyze the user's past feedback history to select the optimal reward when providing rewards. For example, the rewards unit may prioritize rewards that the user has preferred in the past. The rewards unit can also exclude rewards that the user has avoided in the past. For example, the rewards unit analyzes the user's past feedback history and selects the most effective reward. In this way, the optimal reward can be selected by analyzing the user's past feedback history. Some or all of the above processing in the rewards unit may be performed using AI, for example, or not using AI. For example, the rewards unit can input the user's past feedback history data into AI and have the AI ​​perform the selection of the optimal reward.

[0106] The rewards unit can customize the type of reward based on the user's current activity when providing rewards. For example, if the user is on the move, the rewards unit can provide a mobile coupon. Alternatively, if the user is on a break, the rewards unit can provide a reward after requesting detailed feedback. For example, if the user is in a specific location, the rewards unit can provide a location-related reward. This allows for the provision of more appropriate rewards by customizing the type of reward based on the user's current activity. Some or all of the above processing in the rewards unit may be performed using AI, for example, or not. For example, the rewards unit can input the user's current activity data into the AI ​​and have the AI ​​customize the type of reward.

[0107] The reward unit can estimate the user's emotions and determine the priority of rewards based on the estimated emotions. For example, if the user is stressed, the reward unit can immediately provide a reward. Alternatively, if the user is relaxed, the reward unit can request detailed feedback before providing a reward. For example, if the user is in a hurry, the reward unit can provide a reward with simple feedback. This allows for more appropriate rewards to be provided by prioritizing rewards according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reward unit may be performed using AI or not. For example, the reward unit can input user emotion data into an AI and have the AI ​​determine the priority of rewards.

[0108] The rewards unit can provide the most suitable reward by considering the user's geographical location when providing rewards. For example, if the user is at a specific store, the rewards unit can provide a coupon that can be used at that store. The rewards unit can also provide rewards related to an event if the user is at an event venue. For example, if the rewards unit is at a tourist destination, it can provide rewards related to that tourist destination. This allows for the provision of the most suitable reward by considering the user's geographical location. Some or all of the above processing in the rewards unit may be performed using AI, or not. For example, the rewards unit can input the user's geographical location data into an AI and have the AI ​​provide the most suitable reward.

[0109] The rewards department can analyze a user's social media activity and suggest reward types when providing rewards. For example, the rewards department can provide rewards based on content shared by the user on social media. It can also provide rewards related to accounts that the user follows. For example, the rewards department can provide rewards related to online events that the user participates in. This allows the department to suggest the most suitable reward type by analyzing the user's social media activity. Some or all of the above processing in the rewards department may be performed using AI, for example, or not. For example, the rewards department can input the user's social media activity data into an AI and have the AI ​​suggest reward types.

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

[0111] The reception desk can estimate the user's emotions and adjust the content and timing of questions based on the estimated emotions. For example, if the user is stressed, simple and short questions will be prioritized. If the user is relaxed, questions requesting detailed feedback may be asked. Furthermore, if the user is in a hurry, one-word answer questions that can be answered immediately may be asked. By adjusting the content and timing of questions according to the user's emotions, more appropriate feedback can be obtained. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into AI and have the AI ​​adjust the content and timing of questions.

[0112] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated emotions. For example, if the user is stressed, a simple analysis can be performed. If the user is relaxed, a detailed analysis can be performed. Furthermore, if the user is in a hurry, a rapid analysis can be performed. This allows for more appropriate analysis by adjusting the data analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​adjust the data analysis method.

[0113] The filtering unit can estimate the user's emotions and adjust the filtering criteria based on the estimated emotions. For example, if the user is stressed, a strict filtering criterion can be applied. If the user is relaxed, a flexible filtering criterion can be applied. Furthermore, if the user is in a hurry, rapid filtering can be performed. This allows for more appropriate filtering by adjusting the filtering criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the filtering unit may be performed using AI or not. For example, the filtering unit can input user emotion data into an AI and have the AI ​​adjust the filtering criteria.

[0114] The time series analysis unit can estimate the user's emotions and adjust the display method of the time series analysis based on the estimated user emotions. For example, if the user is stressed, a concise display method can be provided. If the user is relaxed, a detailed display method can be provided. Furthermore, if the user is in a hurry, a concise display method can be provided. In this way, a more appropriate display can be achieved by adjusting the display method of the time series analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the time series analysis unit may be performed using AI, or not using AI. For example, the time series analysis unit can input user emotion data into AI and have the AI ​​perform the adjustment of the display method of the time series analysis.

[0115] The reward unit can estimate the user's emotions and adjust the reward delivery method based on the estimated emotions. For example, if the user is stressed, a reward can be provided immediately. If the user is relaxed, a reward can be provided after requesting detailed feedback. Furthermore, if the user is in a hurry, a reward can be provided with simple feedback. This allows for more appropriate rewards to be provided by adjusting the reward delivery method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reward unit may be performed using AI or not. For example, the reward unit can input user emotion data into an AI and have the AI ​​adjust the reward delivery method.

[0116] The reception desk can analyze the user's past feedback history and select the most appropriate question format. For example, it can prioritize question formats that the user has preferred to answer in the past. It can also exclude question formats that the user has avoided in the past. Furthermore, it can analyze the user's past answer patterns and select the most effective question format. In this way, the optimal question format can be selected by analyzing the user's past feedback history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past feedback history into AI and have the AI ​​select the optimal question format.

[0117] The reception desk can customize questions based on the user's current activity when receiving inquiries. For example, if the user is on the move, it can ask questions that can be answered quickly. If the user is on a break, it can ask questions that require more detailed feedback. Furthermore, if the user is in a specific location, it can ask questions related to that location. By customizing questions based on the user's current activity, more appropriate feedback can be obtained. Some or all of the above processing at the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's current activity data into the AI ​​and have the AI ​​customize the questions.

[0118] The analysis unit can adjust the level of detail of the analysis based on the importance of the feedback during the analysis. For example, highly important feedback can be analyzed in detail, while less important feedback can be analyzed simply. Furthermore, analysis resources can be allocated according to importance. This allows for efficient analysis by adjusting the level of detail based on the importance of the feedback. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input feedback importance data into AI and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0119] The filtering unit can improve the accuracy of filtering by considering the interrelationships of feedback during the filtering process. For example, reliability can be increased if the content of one feedback is consistent with other feedback. Conversely, reliability can be decreased if the content of another feedback is contradictory. Furthermore, the filtering unit can analyze the interrelationships of feedback to improve filtering accuracy. Thus, by considering the interrelationships of feedback, filtering accuracy is improved. Some or all of the above processing in the filtering unit may be performed using AI, for example, or without AI. For example, the filtering unit can input the interrelationship data of feedback into AI and have the AI ​​perform the filtering accuracy improvement.

[0120] The time series analysis unit can predict current trends by referring to past data during time series analysis. For example, it can predict current trends based on past data. It can also analyze past data and predict future trends. Furthermore, it can understand current trends by referring to past data. In this way, it can predict current trends by referring to past data. Some or all of the above processes in the time series analysis unit may be performed using AI, for example, or without AI. For example, the time series analysis unit can input past data into AI and have the AI ​​perform the prediction of current trends.

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

[0122] Step 1: The reception desk receives user feedback. User feedback can include text, audio, and images. The reception desk accepts feedback using devices such as smartphones and tablets. The reception desk can also accept questions based on the user's current location. For example, it can use GPS data to obtain the user's current location and ask questions related to that location. Step 2: The analysis department analyzes the feedback received by the reception department. The analysis is performed using statistical analysis and machine learning algorithms. For example, the content of the feedback is analyzed to help improve the business or public services. The level of detail of the analysis can also be adjusted based on the importance of the feedback. High-priority feedback is analyzed in detail, while low-priority feedback is analyzed simply. Step 3: The filtering unit filters out malicious posts based on the data analyzed by the analysis unit. Filtering is performed using spam detection algorithms and blacklists. For example, it analyzes the content of posts and automatically detects and removes inappropriate content and spam. Step 4: The time series analysis unit analyzes the data filtered by the filtering unit in a time series manner. Time series analysis is performed based on the time interval and the algorithm used. For example, it analyzes evaluations during a specific time period to understand evaluations for each time series. Step 5: The rewards department provides rewards to users based on the analysis results obtained by the time-series analysis department. Rewards are provided in the form of points, coupons, cash, etc. For example, a discount coupon may be offered to a user who answers a certain number of surveys.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the reception unit, analysis unit, filtering unit, time-series analysis unit, and reward unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the computer 36 of the smart device 14 and receives user feedback. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the feedback. The filtering unit is implemented by the specific processing unit 290 of the data processing unit 12 and filters out malicious posts. The time-series analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the filtered data in a time series. The reward unit is implemented by the control unit 46A of the smart device 14 and provides rewards to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

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

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

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

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the reception unit, analysis unit, filtering unit, time-series analysis unit, and reward unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the computer 36 of the smart glasses 214 and receives user feedback. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the feedback. The filtering unit is implemented by the specific processing unit 290 of the data processing unit 12 and filters out malicious posts. The time-series analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the filtered data in a time series. The reward unit is implemented by the control unit 46A of the smart glasses 214 and provides rewards to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the reception unit, analysis unit, filtering unit, time-series analysis unit, and reward unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the computer 36 of the headset terminal 314 and receives user feedback. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the feedback. The filtering unit is implemented by the specific processing unit 290 of the data processing unit 12 and filters out malicious posts. The time-series analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the filtered data in a time series. The reward unit is implemented by the control unit 46A of the headset terminal 314 and provides rewards to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the reception unit, analysis unit, filtering unit, time-series analysis unit, and reward unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the computer 36 of the robot 414 and receives user feedback. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the feedback. The filtering unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and filters out malicious posts. The time-series analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the filtered data in a time series. The reward unit is implemented by, for example, the control unit 46A of the robot 414 and provides rewards to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0176] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) A reception desk for receiving user feedback, An analysis unit that analyzes the feedback received by the reception unit, A filtering unit filters out malicious posts based on the data analyzed by the aforementioned analysis unit, A time-series analysis unit analyzes the data filtered by the filtering unit in a time-series manner. The system includes a reward unit that provides rewards to the user based on the analysis results obtained by the time series analysis unit. A system characterized by the following features. (Note 2) The filtering unit is It analyzes the content of posts and automatically detects and removes inappropriate content and spam. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned time series analysis unit, Analyze evaluations during specific time periods. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned compensation unit is, Offer users points and coupons. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It accepts questions based on the user's current location. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is The collected data is analyzed to help improve businesses and public services. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the content and timing of questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past feedback history and select the most suitable question format. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When a question is submitted, the question is customized based on the user's current activity status. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and prioritizes questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving questions, the system prioritizes questions that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving questions, the system analyzes the user's social media activity and selects relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is We estimate user emotions and adjust the data analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the feedback. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analysis algorithms are applied depending on the feedback category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During the analysis, prioritize the analysis based on when feedback is submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During the analysis, adjust the order of analysis based on the relevance of the feedback. The system described in Appendix 1, characterized by the features described herein. (Note 19) The filtering unit is It estimates the user's sentiment and adjusts the filtering criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The filtering unit is During filtering, consider the interrelationships of feedback to improve the accuracy of the filtering process. The system described in Appendix 1, characterized by the features described herein. (Note 21) The filtering unit is When filtering, the attribute information of the feedback submitter is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The filtering unit is It estimates the user's sentiment and adjusts the order in which filtering results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The filtering unit is When filtering, consider the geographical distribution of the feedback. The system described in Appendix 1, characterized by the features described herein. (Note 24) The filtering unit is During filtering, we improve the accuracy of filtering by referring to relevant literature for feedback. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned time series analysis unit, It estimates the user's emotions and adjusts how the time series analysis is displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned time series analysis unit, When performing time series analysis, we refer to past data to predict current trends. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned time series analysis unit, When performing time-series analysis, different analytical methods are applied to each feedback category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned time series analysis unit, We estimate user sentiment and adjust the importance of time series analysis based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned time series analysis unit, When performing time series analysis, analyze changes in the time series based on when feedback was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned time series analysis unit, When performing time series analysis, we analyze the time series by referring to relevant market data for feedback. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned compensation unit is, The system estimates the user's emotions and adjusts the reward system based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned compensation unit is, When providing rewards, the system analyzes the user's past feedback history to select the most appropriate reward. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned compensation unit is, When providing rewards, customize the type of reward based on the user's current activity level. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned compensation unit is, The system estimates the user's emotions and prioritizes rewards based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned compensation unit is, When providing rewards, we will consider the user's geographical location to provide the most appropriate reward. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned compensation unit is, When providing rewards, we analyze the user's social media activity and suggest the type of reward. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk for receiving user feedback, An analysis unit that analyzes the feedback received by the reception unit, A filtering unit filters out malicious posts based on the data analyzed by the aforementioned analysis unit, A time-series analysis unit analyzes the data filtered by the filtering unit in a time-series manner. The system includes a reward unit that provides rewards to the user based on the analysis results obtained by the time series analysis unit. A system characterized by the following features.

2. The filtering unit is It analyzes the content of posts and automatically detects and removes inappropriate content and spam. The system according to feature 1.

3. The aforementioned time series analysis unit, Analyze evaluations during specific time periods. The system according to feature 1.

4. The aforementioned compensation unit is, Offer users points and coupons. The system according to feature 1.

5. The aforementioned reception unit is It accepts questions based on the user's current location. The system according to feature 1.

6. The aforementioned analysis unit is The collected data is analyzed to help improve businesses and public services. The system according to feature 1.

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

8. The aforementioned reception unit is Analyze the user's past feedback history and select the most suitable question format. The system according to feature 1.

9. The aforementioned reception unit is When a question is submitted, the question is customized based on the user's current activity status. The system according to feature 1.

10. The aforementioned reception unit is The system estimates the user's emotions and prioritizes questions based on those estimated emotions. The system according to feature 1.

Images