system

Abstract

The system according to this embodiment aims to analyze data on social media and propose appropriate services to companies. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a ranking unit, and a proposal unit. The collection unit collects data from social media. The analysis unit analyzes the data collected by the collection unit based on factors such as sentiment, topicality, and urgency. The ranking unit ranks the data according to importance based on the data analyzed by the analysis unit. The proposal unit proposes services to companies based on the information ranked by the ranking 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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to efficiently extract useful information from a large amount of posted data on SNS and make an appropriate service proposal to a company.

[0005] The system according to the embodiment aims to analyze data on SNS and make an appropriate service proposal to a company.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a ranking unit, and a proposal unit. The data collection unit collects data from social media. The analysis unit analyzes the data collected by the data collection unit based on factors such as sentiment, topicality, and urgency. The ranking unit ranks the data according to importance based on the data analyzed by the analysis unit. The proposal unit proposes services to companies based on the information ranked by the ranking unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze data on social media and make appropriate service proposals to companies. [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 applicable 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 AI agent system according to an embodiment of the present invention is a system that analyzes the true feelings of users posted on social media and proposes new services. This AI agent system acquires data from social media, analyzes it based on factors such as emotion, topicality, and urgency, ranks it according to importance, and proposes services to companies. The AI ​​agent plans and autonomously and continuously executes this series of steps. For example, the AI ​​agent system acquires data from various social media platforms. At this time, it collects the content of user posts and stores it in a database. For example, it can collect posts related to specific hashtags or keywords. This allows for the collection of users' true feelings over a wide range of areas. Next, the AI ​​agent system analyzes the acquired data based on factors such as emotion, topicality, and urgency. The AI ​​agent uses natural language processing technology to analyze the emotion of the post content. For example, it can determine positive emotions, negative emotions, neutral emotions, etc. Regarding topicality, it evaluates it based on factors such as the frequency of posts and the number of shares. Regarding urgency, it determines the urgency of the post content. This allows for a multifaceted analysis of user voices. Next, the AI ​​agent system ranks the posts according to importance based on the analysis results. The AI ​​agent comprehensively evaluates factors such as emotion, topicality, and urgency, and ranks the importance of each post. For example, posts with positive emotions, high topicality, and high urgency will be ranked highly. This allows companies to efficiently identify posts that deserve attention. Finally, the AI ​​agent system makes service suggestions to companies based on the ranked information. The AI ​​agent analyzes the ranked posts and explores new service ideas. For example, it can suggest new features and improvements that users are requesting. This allows companies to provide services that reflect user feedback. The AI ​​agent plans and autonomously and continuously executes this entire process. By regularly acquiring data from social media and repeating the analysis and suggestions, the AI ​​agent can continuously provide companies with new service ideas. This allows companies to consistently provide services that reflect user feedback.This allows the AI ​​agent system to analyze and rank data collected from social media, and then propose services to companies.

[0029] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, a ranking unit, and a suggestion unit. The collection unit collects data from social networking services (SNS). For example, the collection unit can collect posts related to specific hashtags or keywords. The collection unit collects user posts and stores them in a database. For example, the collection unit can collect posts related to specific hashtags or keywords. This allows the collection unit to collect a wide range of users' genuine opinions. The analysis unit analyzes the data collected by the collection unit based on factors such as sentiment, topicality, and urgency. The analysis unit analyzes the sentiment of the posts using natural language processing technology. For example, the analysis unit can determine positive, negative, or neutral sentiments. The analysis unit also evaluates topicality based on factors such as the frequency of posts and the number of shares. The analysis unit determines the urgency of the posts. This allows the analysis unit to analyze user opinions from multiple perspectives. The ranking unit ranks the data analyzed by the analysis unit according to its importance. The ranking unit comprehensively evaluates factors such as emotion, topicality, and urgency to rank the importance of each post. For example, the ranking unit places a high rank on posts that are highly topical, have positive emotions, and are also highly urgent. This allows the ranking unit to efficiently identify posts that companies should pay attention to. The proposal unit makes service proposals to companies based on the information ranked by the ranking unit. The proposal unit analyzes the ranked posts and explores new service ideas. For example, the proposal unit can propose new features and improvements that users are requesting. This allows the proposal unit to enable companies to provide services that reflect user feedback. Thus, the AI ​​agent system according to this embodiment can analyze and rank data collected from social media and make service proposals to companies.

[0030] The data collection unit collects data from social media. For example, it can collect posts related to specific hashtags or keywords. Specifically, the unit uses social media APIs to monitor posts in real time and automatically collects posts that match specified hashtags or keywords. This allows the unit to collect a wide range of user posts and store them in a database. To efficiently manage the collected data, the unit optimizes the database design and indexing. For example, it adds metadata such as post timestamps, user IDs, hashtags, and keywords to facilitate searching and filtering. The unit also includes filtering functions to eliminate data duplication and maintain data quality. This enables the unit to provide highly reliable data. Furthermore, the unit regularly backs up the collected data and takes measures to prevent data loss. This allows the unit to efficiently and effectively collect data from social media, improving the overall system performance.

[0031] The analysis department analyzes data collected by the data collection department based on factors such as sentiment, topicality, and urgency. The analysis department uses natural language processing technology to analyze the sentiment of posts. Specifically, it uses text sentiment analysis algorithms to determine positive, negative, and neutral sentiment. For example, it calculates sentiment scores for words and phrases within posts to evaluate the overall sentiment. Regarding topicality, the analysis department evaluates factors such as post frequency, number of shares, and number of comments. This allows the analysis department to determine how much attention a particular topic is receiving. Furthermore, regarding urgency, the analysis department detects specific keywords and phrases to determine the urgency of the post content. For example, posts containing keywords such as "urgent," "immediately," or "now" are judged to be highly urgent. This allows the analysis department to analyze user feedback from multiple perspectives and extract important information that companies should address quickly. Additionally, the analysis department can perform future predictions and trend analysis based on past data and trends. This enables the analysis department to provide valuable insights for companies to develop long-term strategies.

[0032] The ranking unit ranks posts according to their importance based on data analyzed by the analytics unit. The ranking unit comprehensively evaluates factors such as sentiment, topicality, and urgency to rank the importance of each post. Specifically, the ranking unit assigns weights to each element and calculates an overall score. For example, a post with positive sentiment, high topicality, and high urgency will be ranked highly. This allows the ranking unit to efficiently identify posts that companies should pay attention to. The ranking unit can flexibly adjust its ranking criteria and algorithms, allowing for customization to meet the needs and circumstances of each company. For example, during a specific campaign period, the weight of topicality can be increased and the weight of urgency decreased to prioritize ranking posts related to the campaign. Furthermore, the ranking unit includes dashboards and reporting functions to visually display ranking results, supporting companies in making quick decisions. This allows the ranking unit to provide a foundation for companies to efficiently grasp important information and take appropriate action.

[0033] The Proposal Department proposes services to companies based on information ranked by the Ranking Department. The Proposal Department analyzes ranked posts and explores new service ideas. Specifically, the Proposal Department extracts user needs and requests and proposes new services and products based on them. For example, the Proposal Department can identify new features and improvements that users want and propose them to companies. The Proposal Department uses AI to analyze post content and predict users' potential needs and trends. This allows the Proposal Department to enable companies to provide services that reflect user feedback. Furthermore, the Proposal Department evaluates the feasibility and marketability of proposals and develops concrete implementation plans. For example, the Proposal Department predicts the development costs and market response of proposed new features, helping companies introduce new services while minimizing risk. The Proposal Department can also monitor the effectiveness of proposals and propose improvements as needed. This allows the Proposal Department to provide support to companies in continuously meeting user needs and maintaining their competitiveness.

[0034] The collection unit can collect posts related to specific hashtags or keywords. For example, the collection unit collects posts related to specific hashtags or keywords. By collecting posts related to specific hashtags or keywords, the collection unit can obtain highly relevant data. For example, the collection unit collects posts related to specific hashtags or keywords. By collecting posts related to specific hashtags or keywords, the collection unit can obtain highly relevant data. By collecting posts related to specific hashtags or keywords, the collection unit can obtain highly relevant data. Thus, the collection unit can obtain highly relevant data by collecting posts related to specific hashtags or keywords.

[0035] The analysis unit includes an emotion analysis unit that analyzes the sentiment of the posted content using natural language processing technology. The analysis unit can analyze the sentiment of the posted content using natural language processing technology. The analysis unit can analyze the sentiment of the posted content by analyzing the sentiment of the posted content using natural language processing technology. The analysis unit can analyze the sentiment of the posted content by analyzing the sentiment of the posted content using natural language processing technology. The analysis unit can analyze the sentiment of the posted content by analyzing the sentiment of the posted content using natural language processing technology. The analysis unit can analyze the sentiment of the posted content by analyzing the sentiment of the posted content using natural language processing technology. Thus, the analysis unit can analyze the sentiment of the posted content by analyzing the sentiment of the posted content using natural language processing technology.

[0036] The ranking unit includes an evaluation unit that comprehensively evaluates elements such as emotion, topicality, and urgency. The ranking unit can comprehensively evaluate elements such as emotion, topicality, and urgency. For example, the ranking unit comprehensively evaluates elements such as emotion, topicality, and urgency. By comprehensively evaluating elements such as emotion, topicality, and urgency, the ranking unit can assign rankings according to importance. For example, the ranking unit comprehensively evaluates elements such as emotion, topicality, and urgency. By comprehensively evaluating elements such as emotion, topicality, and urgency, the ranking unit can assign rankings according to importance. By comprehensively evaluating elements such as emotion, topicality, and urgency, the ranking unit can assign rankings according to importance. As a result, the ranking unit can assign rankings according to importance by comprehensively evaluating elements such as emotion, topicality, and urgency.

[0037] The proposal department includes an idea exploration department that explores new service ideas based on ranked posts. The proposal department can explore new service ideas based on ranked posts. For example, the proposal department can explore new service ideas based on ranked posts. The proposal department can explore new service ideas by exploring new service ideas based on ranked posts. For example, the proposal department can explore new service ideas based on ranked posts. The proposal department can explore new service ideas by exploring new service ideas based on ranked posts. The proposal department can explore new service ideas by exploring new service ideas based on ranked posts. As a result, the proposal department can explore new service ideas by exploring new service ideas based on ranked posts.

[0038] The proposal department can propose new features and improvements that users are requesting. For example, the proposal department can propose new features and improvements that users are requesting. By proposing new features and improvements that users are requesting, the proposal department enables companies to provide services that reflect user feedback. For example, the proposal department can propose new features and improvements that users are requesting. By proposing new features and improvements that users are requesting, the proposal department enables companies to provide services that reflect user feedback. As a result, by proposing new features and improvements that users are requesting, the proposal department enables companies to provide services that reflect user feedback.

[0039] The data collection unit can analyze a user's past posting history and select the optimal collection method. For example, based on hashtags and keywords frequently used by the user in the past, the data collection unit prioritizes collecting posts related to those. For example, the data collection unit can analyze a user's past posting history to determine if they tend to post actively during specific time periods and collect data during those times. For example, the data collection unit can analyze a user's past posting history and prioritize collecting posts on specific topics. In this way, the data collection unit can select the optimal collection method by analyzing a user's past posting history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past posting history data into a generating AI and have the generating AI select the optimal collection method.

[0040] The data collection unit can filter data based on the user's current areas of interest during data collection. For example, the data collection unit can prioritize collecting posts related to topics the user is currently interested in. For example, the data collection unit can collect posts related to accounts the user has recently followed or groups the user has joined. For example, the data collection unit can filter and collect relevant posts based on keywords the user has recently searched for. In this way, the data collection unit can collect highly relevant data by filtering based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current areas of interest data into a generating AI and have the generating AI perform the filtering.

[0041] The data collection unit can prioritize collecting highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize collecting posts related to that region. For example, if the user is traveling, the data collection unit will prioritize collecting posts related to their travel destination. For example, if the user is participating in a specific event, the data collection unit will prioritize collecting posts related to that event. In this way, the data collection unit can collect region-specific data by prioritizing the collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0042] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to posts the user has recently "liked" or "shared." For example, the data collection unit can collect data related to posts the user has recently commented on. For example, the data collection unit can collect data related to accounts the user has recently followed or groups the user has joined. In this way, the data collection unit can collect relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on highly important data. For example, the analysis unit performs a simplified analysis on less important data. For example, the analysis unit performs an analysis with an appropriate level of detail on moderately important data. This allows the analysis unit to perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a sentiment analysis algorithm to sentiment data. For example, the analysis unit can apply a topic modeling algorithm to topicality data. For example, the analysis unit can apply an urgency assessment algorithm to urgency data. This allows the analysis unit to perform more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0045] The analysis unit can determine the priority of analysis based on the data posting date during the analysis process. For example, the analysis unit may prioritize the analysis of the most recently posted data. For example, the analysis unit may prioritize the most recent data while referring to past posted data. For example, the analysis unit may prioritize the analysis of data posted in a concentrated period. This allows the analysis unit to prioritize the most up-to-date information by determining the priority of analysis based on the data posting date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data posting date into a generating AI and have the generating AI determine the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. For example, the analysis unit may analyze data of moderate relevance in an appropriate order. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The ranking unit can improve the accuracy of ranking by considering the interrelationships of data during the ranking process. For example, the ranking unit groups related posts and evaluates the importance of the entire group. For example, the ranking unit analyzes the interrelationships between posts and ranks highly relevant posts higher. For example, the ranking unit improves the accuracy of ranking by considering the interrelationships of posts. In this way, the ranking unit improves the accuracy of ranking by considering the interrelationships of data. Some or all of the above processing in the ranking unit may be performed using AI, for example, or without AI. For example, the ranking unit can input the interrelationships of data into a generating AI and have the generating AI perform the rank accuracy improvement.

[0048] The ranking unit can perform ranking by considering the attribute information of the data contributor. For example, the ranking unit may rank by considering the number of followers and influence of the contributor. For example, the ranking unit may rank by referring to the contributor's past posting history. For example, the ranking unit may rank by considering the contributor's expertise and experience. This allows the ranking unit to perform more appropriate rankings by considering the attribute information of the data contributor. Some or all of the above processing in the ranking unit may be performed using AI, for example, or without AI. For example, the ranking unit can input the contributor's attribute information into a generating AI and have the generating AI perform the ranking.

[0049] The ranking unit can perform ranking while considering the geographical distribution of the data. For example, the ranking unit may rank posts related to a specific region highly. For example, the ranking unit may rank posts that are geographically widespread highly. For example, the ranking unit may rank geographically limited posts appropriately. In this way, the ranking unit can perform region-specific ranking by considering the geographical distribution of the data. Some or all of the above processing in the ranking unit may be performed using AI, for example, or without AI. For example, the ranking unit can input the geographical distribution of the data into a generating AI and have the generating AI perform the ranking.

[0050] The ranking unit can improve the accuracy of its ranking by referring to relevant literature for the data during the ranking process. For example, the ranking unit may refer to relevant academic papers or articles for ranking. For example, the ranking unit may refer to relevant patent documents for ranking. For example, the ranking unit may refer to relevant industry reports for ranking. In this way, the ranking unit improves the accuracy of its ranking by referring to relevant literature for the data. Some or all of the above processing in the ranking unit may be performed using AI, for example, or not using AI. For example, the ranking unit may input relevant literature into a generating AI and have the generating AI perform the rank improvement.

[0051] The proposal unit can adjust the level of detail of its proposals based on the importance of the data. For example, the proposal unit may provide detailed explanations for proposals based on highly important data, concise explanations for proposals based on less important data, and appropriate levels of detail for proposals based on moderately important data. This allows the proposal unit to make efficient proposals by adjusting the level of detail based on the importance of the data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0052] The proposal unit can apply different proposal algorithms depending on the data category when making a proposal. For example, the proposal unit applies a sentiment analysis algorithm to proposals based on sentiment data. For example, the proposal unit applies a topic modeling algorithm to proposals based on topicality data. For example, the proposal unit applies an urgency assessment algorithm to proposals based on urgency data. By applying different proposal algorithms depending on the data category, the proposal unit can make more accurate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the data category into a generating AI and have the generating AI apply different proposal algorithms.

[0053] The proposal department can determine the priority of proposals based on the data submission timing when submitting a proposal. For example, the proposal department may prioritize proposals based on the most recent submitted data. For example, the proposal department may prioritize the latest data while referring to past submitted data. For example, the proposal department may prioritize proposals based on data submitted in a concentrated period. This allows the proposal department to prioritize proposals based on the data submission timing, thereby enabling proposals that emphasize the latest information. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department may input the data submission timing into a generating AI and have the generating AI determine the priority of proposals.

[0054] The proposal unit can adjust the order of proposals based on the relevance of the data during the proposal process. For example, the proposal unit may prioritize proposals based on highly relevant data. For example, it may postpone proposals based on less relevant data. For example, it may make proposals based on moderately relevant data in an appropriate order. This allows the proposal unit to make efficient proposals by adjusting the order of proposals based on the relevance of the data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the relevance of the data into a generating AI and have the generating AI adjust the order of proposals.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit can analyze a user's past posting history and select the optimal collection method. For example, based on hashtags and keywords frequently used by the user in the past, the collection unit prioritizes collecting posts related to them. It can also analyze a user's past posting history to identify periods of active posting and collect data during those times. By analyzing a user's past posting history, it can prioritize collecting posts related to specific topics. In this way, the data collection unit can select the optimal collection method by analyzing a user's past posting history.

[0057] The data collection unit can filter data based on the user's current areas of interest. For example, it can prioritize collecting posts related to topics the user is currently interested in, collect posts related to accounts the user has recently followed or groups the user has joined, and filter and collect relevant posts based on keywords the user has recently searched for. In this way, the data collection unit can collect highly relevant data by filtering based on the user's current areas of interest.

[0058] The data collection unit can prioritize collecting highly relevant data based on the user's geographical location. For example, if a user is in a specific region, it will prioritize collecting posts related to that region. If a user is traveling, it will prioritize collecting posts related to their travel destination. If a user is attending a specific event, it will prioritize collecting posts related to that event. In this way, the data collection unit can collect region-specific data by prioritizing the collection of highly relevant data based on the user's geographical location.

[0059] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, it can collect data related to posts that users have recently "liked" or "shared," data related to posts that users have recently commented on, and data related to accounts that users have recently followed or groups that they have joined. In this way, the data collection unit can collect relevant data by analyzing users' social media activity.

[0060] The analysis department can adjust the level of detail in its analysis based on the importance of the data. For example, it can perform a detailed analysis on highly important data, a simplified analysis on less important data, and an analysis with an appropriate level of detail on moderately important data. This allows the analysis department to perform efficient analysis by adjusting the level of detail based on the importance of the data.

[0061] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, it can apply a sentiment analysis algorithm to sentiment data, a topic modeling algorithm to topicality data, and an urgency assessment algorithm to urgency data. This allows the analysis unit to perform more accurate analyses by applying different analysis algorithms depending on the data category.

[0062] The analysis department can prioritize analysis based on the data posting date. For example, it can prioritize analyzing the most recent posted data, or it can prioritize analyzing the latest data while referring to past posted data, or it can prioritize analyzing data posted in a concentrated period. This allows the analysis department to prioritize analysis based on the data posting date, enabling analysis that emphasizes the latest information.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The data collection unit collects data from social media. For example, the data collection unit can collect posts related to specific hashtags or keywords. The data collection unit collects user posts and stores them in a database. This allows the data collection unit to gather a wide range of users' genuine opinions. Step 2: The analysis unit analyzes the data collected by the data collection unit based on factors such as sentiment, topicality, and urgency. The analysis unit uses natural language processing technology to analyze the sentiment of the posts. For example, the analysis unit can determine whether the sentiment is positive, negative, or neutral. The analysis unit also evaluates topicality based on factors such as the frequency of posts and the number of shares. The analysis unit determines the urgency of the posts. This allows the analysis unit to analyze user feedback from multiple perspectives. Step 3: The ranking department ranks posts according to their importance based on the data analyzed by the analysis department. The ranking department comprehensively evaluates factors such as sentiment, topicality, and urgency to rank the importance of each post. For example, the ranking department will assign a high rank to posts that are highly topical, have a positive sentiment, and are also highly urgent. This allows the ranking department to efficiently identify posts that companies should pay attention to. Step 4: The proposal team proposes services to companies based on the information ranked by the ranking team. The proposal team analyzes the ranked posts and explores new service ideas. For example, the proposal team can suggest new features and improvements that users want. This allows the proposal team to enable companies to provide services that reflect user feedback.

[0065] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that analyzes the true feelings of users posted on social media and proposes new services. This AI agent system acquires data from social media, analyzes it based on factors such as emotion, topicality, and urgency, ranks it according to importance, and proposes services to companies. The AI ​​agent plans and autonomously and continuously executes this series of steps. For example, the AI ​​agent system acquires data from various social media platforms. At this time, it collects the content of user posts and stores it in a database. For example, it can collect posts related to specific hashtags or keywords. This allows for the collection of users' true feelings over a wide range of areas. Next, the AI ​​agent system analyzes the acquired data based on factors such as emotion, topicality, and urgency. The AI ​​agent uses natural language processing technology to analyze the emotion of the post content. For example, it can determine positive emotions, negative emotions, neutral emotions, etc. Regarding topicality, it evaluates it based on factors such as the frequency of posts and the number of shares. Regarding urgency, it determines the urgency of the post content. This allows for a multifaceted analysis of user voices. Next, the AI ​​agent system ranks the posts according to importance based on the analysis results. The AI ​​agent comprehensively evaluates factors such as emotion, topicality, and urgency, and ranks the importance of each post. For example, posts with positive emotions, high topicality, and high urgency will be ranked highly. This allows companies to efficiently identify posts that deserve attention. Finally, the AI ​​agent system makes service suggestions to companies based on the ranked information. The AI ​​agent analyzes the ranked posts and explores new service ideas. For example, it can suggest new features and improvements that users are requesting. This allows companies to provide services that reflect user feedback. The AI ​​agent plans and autonomously and continuously executes this entire process. By regularly acquiring data from social media and repeating the analysis and suggestions, the AI ​​agent can continuously provide companies with new service ideas. This allows companies to consistently provide services that reflect user feedback.This allows the AI ​​agent system to analyze and rank data collected from social media, and then propose services to companies.

[0066] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, a ranking unit, and a suggestion unit. The collection unit collects data from social networking services (SNS). For example, the collection unit can collect posts related to specific hashtags or keywords. The collection unit collects user posts and stores them in a database. For example, the collection unit can collect posts related to specific hashtags or keywords. This allows the collection unit to collect a wide range of users' genuine opinions. The analysis unit analyzes the data collected by the collection unit based on factors such as sentiment, topicality, and urgency. The analysis unit analyzes the sentiment of the posts using natural language processing technology. For example, the analysis unit can determine positive, negative, or neutral sentiments. The analysis unit also evaluates topicality based on factors such as the frequency of posts and the number of shares. The analysis unit determines the urgency of the posts. This allows the analysis unit to analyze user opinions from multiple perspectives. The ranking unit ranks the data analyzed by the analysis unit according to its importance. The ranking unit comprehensively evaluates factors such as emotion, topicality, and urgency to rank the importance of each post. For example, the ranking unit places a high rank on posts that are highly topical, have positive emotions, and are also highly urgent. This allows the ranking unit to efficiently identify posts that companies should pay attention to. The proposal unit makes service proposals to companies based on the information ranked by the ranking unit. The proposal unit analyzes the ranked posts and explores new service ideas. For example, the proposal unit can propose new features and improvements that users are requesting. This allows the proposal unit to enable companies to provide services that reflect user feedback. Thus, the AI ​​agent system according to this embodiment can analyze and rank data collected from social media and make service proposals to companies.

[0067] The data collection unit collects data from social media. For example, it can collect posts related to specific hashtags or keywords. Specifically, the unit uses social media APIs to monitor posts in real time and automatically collects posts that match specified hashtags or keywords. This allows the unit to collect a wide range of user posts and store them in a database. To efficiently manage the collected data, the unit optimizes the database design and indexing. For example, it adds metadata such as post timestamps, user IDs, hashtags, and keywords to facilitate searching and filtering. The unit also includes filtering functions to eliminate data duplication and maintain data quality. This enables the unit to provide highly reliable data. Furthermore, the unit regularly backs up the collected data and takes measures to prevent data loss. This allows the unit to efficiently and effectively collect data from social media, improving the overall system performance.

[0068] The analysis department analyzes data collected by the data collection department based on factors such as sentiment, topicality, and urgency. The analysis department uses natural language processing technology to analyze the sentiment of posts. Specifically, it uses text sentiment analysis algorithms to determine positive, negative, and neutral sentiment. For example, it calculates sentiment scores for words and phrases within posts to evaluate the overall sentiment. Regarding topicality, the analysis department evaluates factors such as post frequency, number of shares, and number of comments. This allows the analysis department to determine how much attention a particular topic is receiving. Furthermore, regarding urgency, the analysis department detects specific keywords and phrases to determine the urgency of the post content. For example, posts containing keywords such as "urgent," "immediately," or "now" are judged to be highly urgent. This allows the analysis department to analyze user feedback from multiple perspectives and extract important information that companies should address quickly. Additionally, the analysis department can perform future predictions and trend analysis based on past data and trends. This enables the analysis department to provide valuable insights for companies to develop long-term strategies.

[0069] The ranking unit ranks posts according to their importance based on data analyzed by the analytics unit. The ranking unit comprehensively evaluates factors such as sentiment, topicality, and urgency to rank the importance of each post. Specifically, the ranking unit assigns weights to each element and calculates an overall score. For example, a post with positive sentiment, high topicality, and high urgency will be ranked highly. This allows the ranking unit to efficiently identify posts that companies should pay attention to. The ranking unit can flexibly adjust its ranking criteria and algorithms, allowing for customization to meet the needs and circumstances of each company. For example, during a specific campaign period, the weight of topicality can be increased and the weight of urgency decreased to prioritize ranking posts related to the campaign. Furthermore, the ranking unit includes dashboards and reporting functions to visually display ranking results, supporting companies in making quick decisions. This allows the ranking unit to provide a foundation for companies to efficiently grasp important information and take appropriate action.

[0070] The Proposal Department proposes services to companies based on information ranked by the Ranking Department. The Proposal Department analyzes ranked posts and explores new service ideas. Specifically, the Proposal Department extracts user needs and requests and proposes new services and products based on them. For example, the Proposal Department can identify new features and improvements that users want and propose them to companies. The Proposal Department uses AI to analyze post content and predict users' potential needs and trends. This allows the Proposal Department to enable companies to provide services that reflect user feedback. Furthermore, the Proposal Department evaluates the feasibility and marketability of proposals and develops concrete implementation plans. For example, the Proposal Department predicts the development costs and market response of proposed new features, helping companies introduce new services while minimizing risk. The Proposal Department can also monitor the effectiveness of proposals and propose improvements as needed. This allows the Proposal Department to provide support to companies in continuously meeting user needs and maintaining their competitiveness.

[0071] The collection unit can collect posts related to specific hashtags or keywords. For example, the collection unit collects posts related to specific hashtags or keywords. By collecting posts related to specific hashtags or keywords, the collection unit can obtain highly relevant data. For example, the collection unit collects posts related to specific hashtags or keywords. By collecting posts related to specific hashtags or keywords, the collection unit can obtain highly relevant data. By collecting posts related to specific hashtags or keywords, the collection unit can obtain highly relevant data. Thus, the collection unit can obtain highly relevant data by collecting posts related to specific hashtags or keywords.

[0072] The analysis unit includes an emotion analysis unit that analyzes the sentiment of the posted content using natural language processing technology. The analysis unit can analyze the sentiment of the posted content using natural language processing technology. The analysis unit can analyze the sentiment of the posted content by analyzing the sentiment of the posted content using natural language processing technology. The analysis unit can analyze the sentiment of the posted content by analyzing the sentiment of the posted content using natural language processing technology. The analysis unit can analyze the sentiment of the posted content by analyzing the sentiment of the posted content using natural language processing technology. The analysis unit can analyze the sentiment of the posted content by analyzing the sentiment of the posted content using natural language processing technology. Thus, the analysis unit can analyze the sentiment of the posted content by analyzing the sentiment of the posted content using natural language processing technology.

[0073] The ranking unit includes an evaluation unit that comprehensively evaluates elements such as emotion, topicality, and urgency. The ranking unit can comprehensively evaluate elements such as emotion, topicality, and urgency. For example, the ranking unit comprehensively evaluates elements such as emotion, topicality, and urgency. By comprehensively evaluating elements such as emotion, topicality, and urgency, the ranking unit can assign rankings according to importance. For example, the ranking unit comprehensively evaluates elements such as emotion, topicality, and urgency. By comprehensively evaluating elements such as emotion, topicality, and urgency, the ranking unit can assign rankings according to importance. By comprehensively evaluating elements such as emotion, topicality, and urgency, the ranking unit can assign rankings according to importance. As a result, the ranking unit can assign rankings according to importance by comprehensively evaluating elements such as emotion, topicality, and urgency.

[0074] The proposal department includes an idea exploration department that explores new service ideas based on ranked posts. The proposal department can explore new service ideas based on ranked posts. For example, the proposal department can explore new service ideas based on ranked posts. The proposal department can explore new service ideas by exploring new service ideas based on ranked posts. For example, the proposal department can explore new service ideas based on ranked posts. The proposal department can explore new service ideas by exploring new service ideas based on ranked posts. The proposal department can explore new service ideas by exploring new service ideas based on ranked posts. As a result, the proposal department can explore new service ideas by exploring new service ideas based on ranked posts.

[0075] The proposal department can propose new features and improvements that users are requesting. For example, the proposal department can propose new features and improvements that users are requesting. By proposing new features and improvements that users are requesting, the proposal department enables companies to provide services that reflect user feedback. For example, the proposal department can propose new features and improvements that users are requesting. By proposing new features and improvements that users are requesting, the proposal department enables companies to provide services that reflect user feedback. As a result, by proposing new features and improvements that users are requesting, the proposal department enables companies to provide services that reflect user feedback.

[0076] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is showing positive emotions, the data collection unit will collect data at that time and prioritize obtaining positive feedback. For example, if the user is showing negative emotions, the data collection unit will collect data at that time and prioritize obtaining areas for improvement and points of dissatisfaction. For example, if the user is showing neutral emotions, the data collection unit will collect data at that time and obtain general opinions and requests. In this way, the data collection unit can collect more appropriate data by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0077] The data collection unit can analyze a user's past posting history and select the optimal collection method. For example, based on hashtags and keywords frequently used by the user in the past, the data collection unit prioritizes collecting posts related to those. For example, the data collection unit can analyze a user's past posting history to determine if they tend to post actively during specific time periods and collect data during those times. For example, the data collection unit can analyze a user's past posting history and prioritize collecting posts on specific topics. In this way, the data collection unit can select the optimal collection method by analyzing a user's past posting history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past posting history data into a generating AI and have the generating AI select the optimal collection method.

[0078] The data collection unit can filter data based on the user's current areas of interest during data collection. For example, the data collection unit can prioritize collecting posts related to topics the user is currently interested in. For example, the data collection unit can collect posts related to accounts the user has recently followed or groups the user has joined. For example, the data collection unit can filter and collect relevant posts based on keywords the user has recently searched for. In this way, the data collection unit can collect highly relevant data by filtering based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current areas of interest data into a generating AI and have the generating AI perform the filtering.

[0079] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is expressing positive emotions, the data collection unit will prioritize collecting positive feedback. For example, if the user is expressing negative emotions, the data collection unit will prioritize collecting areas for improvement or complaints. For example, if the user is expressing neutral emotions, the data collection unit will prioritize collecting general opinions and requests. In this way, the data collection unit can prioritize the collection of important data by determining the priority of data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The data collection unit can prioritize collecting highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize collecting posts related to that region. For example, if the user is traveling, the data collection unit will prioritize collecting posts related to their travel destination. For example, if the user is participating in a specific event, the data collection unit will prioritize collecting posts related to that event. In this way, the data collection unit can collect region-specific data by prioritizing the collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0081] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to posts the user has recently "liked" or "shared." For example, the data collection unit can collect data related to posts the user has recently commented on. For example, the data collection unit can collect data related to accounts the user has recently followed or groups the user has joined. In this way, the data collection unit can collect relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is expressing positive emotions, the analysis unit will emphasize positive feedback in its presentation of the analysis results. For example, if the user is expressing negative emotions, the analysis unit will emphasize areas for improvement and points of dissatisfaction in its presentation of the analysis results. For example, if the user is expressing neutral emotions, the analysis unit will present a balanced presentation of the analysis results. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on highly important data. For example, the analysis unit performs a simplified analysis on less important data. For example, the analysis unit performs an analysis with an appropriate level of detail on moderately important data. This allows the analysis unit to perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0084] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a sentiment analysis algorithm to sentiment data. For example, the analysis unit can apply a topic modeling algorithm to topicality data. For example, the analysis unit can apply an urgency assessment algorithm to urgency data. This allows the analysis unit to perform more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is showing positive emotions, the analysis unit provides a detailed analysis. For example, if the user is showing negative emotions, the analysis unit provides a concise analysis. For example, if the user is showing neutral emotions, the analysis unit provides an analysis of appropriate length. In this way, the analysis unit can provide more appropriate analysis results by adjusting the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0086] The analysis unit can determine the priority of analysis based on the data posting date during the analysis process. For example, the analysis unit may prioritize the analysis of the most recently posted data. For example, the analysis unit may prioritize the most recent data while referring to past posted data. For example, the analysis unit may prioritize the analysis of data posted in a concentrated period. This allows the analysis unit to prioritize the most up-to-date information by determining the priority of analysis based on the data posting date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data posting date into a generating AI and have the generating AI determine the analysis priority.

[0087] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. For example, the analysis unit may analyze data of moderate relevance in an appropriate order. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0088] The ranking unit can estimate the user's sentiment and adjust the ranking criteria based on the estimated user sentiment. For example, the ranking unit may rank posts that indicate positive sentiment highly. For example, the ranking unit may rank posts that indicate negative sentiment highly. For example, the ranking unit may rank posts that indicate neutral sentiment moderately. In this way, the ranking unit can perform more appropriate rankings by adjusting the ranking criteria based on the user's sentiment. Sentiment estimation is achieved using a sentiment estimation function, for example, using a sentiment 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 ranking unit may be performed using AI, for example, or not using AI. For example, the ranking unit can input user sentiment data into a generative AI and have the generative AI perform sentiment estimation.

[0089] The ranking unit can improve the accuracy of ranking by considering the interrelationships of data during the ranking process. For example, the ranking unit groups related posts and evaluates the importance of the entire group. For example, the ranking unit analyzes the interrelationships between posts and ranks highly relevant posts higher. For example, the ranking unit improves the accuracy of ranking by considering the interrelationships of posts. In this way, the ranking unit improves the accuracy of ranking by considering the interrelationships of data. Some or all of the above processing in the ranking unit may be performed using AI, for example, or without AI. For example, the ranking unit can input the interrelationships of data into a generating AI and have the generating AI perform the rank accuracy improvement.

[0090] The ranking unit can perform ranking by considering the attribute information of the data contributor. For example, the ranking unit may rank by considering the number of followers and influence of the contributor. For example, the ranking unit may rank by referring to the contributor's past posting history. For example, the ranking unit may rank by considering the contributor's expertise and experience. This allows the ranking unit to perform more appropriate rankings by considering the attribute information of the data contributor. Some or all of the above processing in the ranking unit may be performed using AI, for example, or without AI. For example, the ranking unit can input the contributor's attribute information into a generating AI and have the generating AI perform the ranking.

[0091] The ranking unit can estimate the user's sentiment and adjust the order in which the ranking results are displayed based on the estimated user sentiment. For example, the ranking unit may display posts showing positive sentiment at the top. For example, the ranking unit may display posts showing negative sentiment at the top. For example, the ranking unit may display posts showing neutral sentiment at an appropriate position. In this way, the ranking unit can provide more relevant information by adjusting the order in which the ranking results are displayed based on the user's sentiment. Sentiment estimation is achieved using a sentiment estimation function, for example, using a sentiment 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 ranking unit may be performed using AI, for example, or not using AI. For example, the ranking unit can input user sentiment data into a generative AI and have the generative AI perform sentiment estimation.

[0092] The ranking unit can perform ranking while considering the geographical distribution of the data. For example, the ranking unit may rank posts related to a specific region highly. For example, the ranking unit may rank posts that are geographically widespread highly. For example, the ranking unit may rank geographically limited posts appropriately. In this way, the ranking unit can perform region-specific ranking by considering the geographical distribution of the data. Some or all of the above processing in the ranking unit may be performed using AI, for example, or without AI. For example, the ranking unit can input the geographical distribution of the data into a generating AI and have the generating AI perform the ranking.

[0093] The ranking unit can improve the accuracy of its ranking by referring to relevant literature for the data during the ranking process. For example, the ranking unit may refer to relevant academic papers or articles for ranking. For example, the ranking unit may refer to relevant patent documents for ranking. For example, the ranking unit may refer to relevant industry reports for ranking. In this way, the ranking unit improves the accuracy of its ranking by referring to relevant literature for the data. Some or all of the above processing in the ranking unit may be performed using AI, for example, or not using AI. For example, the ranking unit may input relevant literature into a generating AI and have the generating AI perform the rank improvement.

[0094] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user expresses positive emotions, the suggestion unit will present suggestions in a positive manner. If the user expresses negative emotions, the suggestion unit will emphasize areas for improvement in its suggestions. If the user expresses neutral emotions, the suggestion unit will present suggestions in a balanced manner. This allows the suggestion unit to provide more appropriate suggestions by adjusting the way it presents its suggestions based on 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-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0095] The proposal unit can adjust the level of detail of its proposals based on the importance of the data. For example, the proposal unit may provide detailed explanations for proposals based on highly important data, concise explanations for proposals based on less important data, and appropriate levels of detail for proposals based on moderately important data. This allows the proposal unit to make efficient proposals by adjusting the level of detail based on the importance of the data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0096] The proposal unit can apply different proposal algorithms depending on the data category when making a proposal. For example, the proposal unit applies a sentiment analysis algorithm to proposals based on sentiment data. For example, the proposal unit applies a topic modeling algorithm to proposals based on topicality data. For example, the proposal unit applies an urgency assessment algorithm to proposals based on urgency data. By applying different proposal algorithms depending on the data category, the proposal unit can make more accurate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the data category into a generating AI and have the generating AI apply different proposal algorithms.

[0097] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is showing positive emotions, the suggestion unit will provide a detailed suggestion. For example, if the user is showing negative emotions, the suggestion unit will provide a concise suggestion. For example, if the user is showing neutral emotions, the suggestion unit will provide a suggestion of appropriate length. In this way, the suggestion unit can provide more appropriate suggestions by adjusting the length of the suggestion based on 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 suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0098] The proposal department can determine the priority of proposals based on the data submission timing when submitting a proposal. For example, the proposal department may prioritize proposals based on the most recent submitted data. For example, the proposal department may prioritize the latest data while referring to past submitted data. For example, the proposal department may prioritize proposals based on data submitted in a concentrated period. This allows the proposal department to prioritize proposals based on the data submission timing, thereby enabling proposals that emphasize the latest information. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department may input the data submission timing into a generating AI and have the generating AI determine the priority of proposals.

[0099] The proposal unit can adjust the order of proposals based on the relevance of the data during the proposal process. For example, the proposal unit may prioritize proposals based on highly relevant data. For example, it may postpone proposals based on less relevant data. For example, it may make proposals based on moderately relevant data in an appropriate order. This allows the proposal unit to make efficient proposals by adjusting the order of proposals based on the relevance of the data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the relevance of the data into a generating AI and have the generating AI adjust the order of proposals.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if a user is showing positive emotions, the unit will collect data at that time and prioritize obtaining positive feedback. If a user is showing negative emotions, the unit will collect data at that time and prioritize obtaining areas for improvement and areas of dissatisfaction. If a user is showing neutral emotions, the unit will collect data at that time and obtain general opinions and requests. In this way, the data collection unit can collect more appropriate data by adjusting the timing of data collection based on the user's emotions.

[0102] The data collection unit can analyze a user's past posting history and select the optimal collection method. For example, based on hashtags and keywords frequently used by the user in the past, the collection unit prioritizes collecting posts related to them. It can also analyze a user's past posting history to identify periods of active posting and collect data during those times. By analyzing a user's past posting history, it can prioritize collecting posts related to specific topics. In this way, the data collection unit can select the optimal collection method by analyzing a user's past posting history.

[0103] The data collection unit can filter data based on the user's current areas of interest. For example, it can prioritize collecting posts related to topics the user is currently interested in, collect posts related to accounts the user has recently followed or groups the user has joined, and filter and collect relevant posts based on keywords the user has recently searched for. In this way, the data collection unit can collect highly relevant data by filtering based on the user's current areas of interest.

[0104] The data collection unit can prioritize collecting highly relevant data based on the user's geographical location. For example, if a user is in a specific region, it will prioritize collecting posts related to that region. If a user is traveling, it will prioritize collecting posts related to their travel destination. If a user is attending a specific event, it will prioritize collecting posts related to that event. In this way, the data collection unit can collect region-specific data by prioritizing the collection of highly relevant data based on the user's geographical location.

[0105] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, it can collect data related to posts that users have recently "liked" or "shared," data related to posts that users have recently commented on, and data related to accounts that users have recently followed or groups that they have joined. In this way, the data collection unit can collect relevant data by analyzing users' social media activity.

[0106] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those estimated emotions. For example, if the user expresses positive emotions, the analysis results will be presented with emphasis on positive feedback. If the user expresses negative emotions, the analysis results will be presented with emphasis on areas for improvement and points of dissatisfaction. If the user expresses neutral emotions, the analysis results will be presented in a balanced manner. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis based on the user's emotions.

[0107] The analysis department can adjust the level of detail in its analysis based on the importance of the data. For example, it can perform a detailed analysis on highly important data, a simplified analysis on less important data, and an analysis with an appropriate level of detail on moderately important data. This allows the analysis department to perform efficient analysis by adjusting the level of detail based on the importance of the data.

[0108] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, it can apply a sentiment analysis algorithm to sentiment data, a topic modeling algorithm to topicality data, and an urgency assessment algorithm to urgency data. This allows the analysis unit to perform more accurate analyses by applying different analysis algorithms depending on the data category.

[0109] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user shows positive emotions, it provides a detailed analysis. If the user shows negative emotions, it provides a concise analysis. If the user shows neutral emotions, it provides an analysis of appropriate length. In this way, the analysis unit can provide more appropriate analysis results by adjusting the length of the analysis based on the user's emotions.

[0110] The analysis department can prioritize analysis based on the data posting date. For example, it can prioritize analyzing the most recent posted data, or it can prioritize analyzing the latest data while referring to past posted data, or it can prioritize analyzing data posted in a concentrated period. This allows the analysis department to prioritize analysis based on the data posting date, enabling analysis that emphasizes the latest information.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The data collection unit collects data from social media. For example, the data collection unit can collect posts related to specific hashtags or keywords. The data collection unit collects user posts and stores them in a database. This allows the data collection unit to gather a wide range of users' genuine opinions. Step 2: The analysis unit analyzes the data collected by the data collection unit based on factors such as sentiment, topicality, and urgency. The analysis unit uses natural language processing technology to analyze the sentiment of the posts. For example, the analysis unit can determine whether the sentiment is positive, negative, or neutral. The analysis unit also evaluates topicality based on factors such as the frequency of posts and the number of shares. The analysis unit determines the urgency of the posts. This allows the analysis unit to analyze user feedback from multiple perspectives. Step 3: The ranking department ranks posts according to their importance based on the data analyzed by the analysis department. The ranking department comprehensively evaluates factors such as sentiment, topicality, and urgency to rank the importance of each post. For example, the ranking department will assign a high rank to posts that are highly topical, have a positive sentiment, and are also highly urgent. This allows the ranking department to efficiently identify posts that companies should pay attention to. Step 4: The proposal team proposes services to companies based on the information ranked by the ranking team. The proposal team analyzes the ranked posts and explores new service ideas. For example, the proposal team can suggest new features and improvements that users want. This allows the proposal team to enable companies to provide services that reflect user feedback.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the data collection unit, analysis unit, ranking unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14, which collects data from SNS and stores it in a database. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data based on elements such as sentiment, topicality, and urgency. The ranking unit is implemented by the specific processing unit 290 of the data processing unit 12, which ranks the data according to its importance based on the analysis results. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes services to companies based on the ranked information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the data collection unit, analysis unit, ranking unit, and proposal unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214, which collects data from SNS and stores it in a database. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data based on elements such as sentiment, topicality, and urgency. The ranking unit is implemented by the identification processing unit 290 of the data processing unit 12, which ranks the data according to its importance based on the analysis results. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12, which proposes services to companies based on the ranked information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the data collection unit, analysis unit, ranking unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314, which collects data from SNS and stores it in a database. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data based on elements such as sentiment, topicality, and urgency. The ranking unit is implemented by the specific processing unit 290 of the data processing unit 12, which ranks the data according to its importance based on the analysis results. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes services to companies based on the ranked information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the data collection unit, analysis unit, ranking unit, and proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414, which collects data from SNS and stores it in a database. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the collected data based on elements such as sentiment, topicality, and urgency. The ranking unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which ranks the data according to its importance based on the analysis results. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which proposes services to companies based on the ranked information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) The data collection department collects data from social media, The data collected by the aforementioned collection unit is analyzed by an analysis unit based on factors such as emotion, topicality, and urgency. A ranking unit that ranks data according to importance based on the data analyzed by the aforementioned analysis unit, The system includes a proposal unit that proposes services to companies based on the information ranked by the aforementioned ranking unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect posts related to specific hashtags or keywords. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is It includes an emotion analysis unit that uses natural language processing technology to analyze the emotions contained in posts. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned ranking unit, It includes an evaluation unit that comprehensively assesses factors such as emotion, topicality, and urgency. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, It includes an idea generation department that explores new service ideas based on ranked posts. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Suggest new features and improvements that users want. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past posting history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented 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 data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data 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 the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was posted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned ranking unit, It estimates user sentiment and adjusts ranking criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned ranking unit, When ranking data, consider the interrelationships between data to improve ranking accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned ranking unit, When ranking data, the attribute information of the data contributor is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned ranking unit, It estimates user sentiment and adjusts the order in which ranking results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned ranking unit, When ranking data, the geographical distribution of the data should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned ranking unit, When ranking data, we refer to relevant literature to improve the accuracy of the ranking. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When submitting a proposal, prioritize it based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0185] 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. The data collection department collects data from social media, The data collected by the aforementioned collection unit is analyzed by an analysis unit based on factors such as emotion, topicality, and urgency. A ranking unit that ranks data according to importance based on the data analyzed by the aforementioned analysis unit, The system includes a proposal unit that proposes services to companies based on the information ranked by the aforementioned ranking unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect posts related to specific hashtags or keywords. The system according to feature 1.

3. The aforementioned analysis unit is It includes an emotion analysis unit that uses natural language processing technology to analyze the emotions contained in posts. The system according to feature 1.

4. The aforementioned ranking unit, It includes an evaluation unit that comprehensively assesses factors such as emotion, topicality, and urgency. The system according to feature 1.

5. The aforementioned proposal section is, It includes an idea generation department that explores new service ideas based on ranked posts. The system according to feature 1.

6. The aforementioned proposal section is, Suggest new features and improvements that users want. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past posting history and select the optimal data collection method. The system according to feature 1.

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