system

Abstract

The system according to this embodiment aims to analyze data on the internet and quickly provide useful insights. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a visualization unit, and a provision unit. The collection unit collects search terms from the internet and topics from social media. The analysis unit analyzes the data collected by the collection unit. The visualization unit visualizes the analysis results obtained by the analysis unit. The provision unit provides insights based on the information visualized by the visualization 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, including 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 prior art, there was a problem that it was difficult to quickly obtain useful insights from a vast amount of data on the Internet.

[0005] The system according to the embodiment aims to analyze data on the Internet and quickly provide useful insights.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a visualization unit, and a data provision unit. The data collection unit collects search terms from the internet and topics from social media. The analysis unit analyzes the data collected by the data collection unit. The visualization unit visualizes the analysis results obtained by the analysis unit. The data provision unit provides insights based on the information visualized by the visualization unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze data on the internet and quickly provide useful insights. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The trend analysis assistant according to an embodiment of the present invention is a system that analyzes search terms on the internet and topics on social media in real time and visualizes trends. This trend analysis assistant allows companies and individuals to quickly grasp market trends and use this information to inform marketing strategies and product development. Furthermore, it collects and analyzes data from news articles, blog posts, forums, review sites, etc., to visualize consumer trends and competitive situations. It helps to understand consumer needs and frustrations in real time and supports rapid decision-making. For example, the trend analysis assistant collects search terms on the internet and topics on social media. For example, it collects how often specific keywords are searched and what topics are trending on social media. This information is analyzed in real time by AI. Next, it visualizes trends based on the collected data. For example, it intuitively displays search term trends in graphs and dashboards. This allows companies and individuals to quickly grasp market trends. Furthermore, it collects data from news articles, blog posts, forums, review sites, etc., to analyze consumer trends and competitive situations. For example, it can understand consumer evaluations of specific products and the trends of competitors. This information is analyzed by AI, allowing for real-time understanding of consumer needs and frustrations. Finally, the analysis results support rapid decision-making. For example, it provides insights to optimize marketing strategies and determine the direction of product development. This enables businesses and individuals to implement effective marketing strategies and product development. This system allows businesses and individuals to quickly grasp market trends and implement effective marketing strategies and product development. For example, if a particular keyword is trending, offering products or services related to that keyword can attract consumer interest. Furthermore, real-time understanding of consumer needs and frustrations allows for rapid response. For example, if consumers are dissatisfied with a particular product, improvement measures to address that dissatisfaction can be implemented quickly.This allows trend analysis assistants to quickly grasp market trends and develop effective marketing strategies and products.

[0029] The trend analysis assistant according to this embodiment comprises a collection unit, an analysis unit, a visualization unit, and a provision unit. The collection unit collects search terms on the internet and topics on social media. For example, the collection unit collects how often a particular keyword is searched and what topics are trending on social media. For example, if a particular keyword is trending rapidly, the collection unit can prioritize collecting data related to that keyword. For example, if a particular hashtag is trending rapidly, the collection unit can prioritize collecting data related to that hashtag. For example, if a particular topic is trending on social media, the collection unit can prioritize collecting data related to that topic. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes trends based on the collected data. For example, the analysis unit can analyze consumer trends based on the collected data. For example, the analysis unit can analyze the actions of competitors based on the collected data. For example, the analysis unit can analyze consumer needs based on the collected data. The visualization unit visualizes the analysis results obtained by the analysis unit. The visualization unit intuitively displays search term trends in graphs and dashboards. The visualization unit can intuitively display consumer trends in graphs and dashboards. The visualization unit can also intuitively display competitor trends in graphs and dashboards. The visualization unit can also intuitively display consumer needs in graphs and dashboards. The provisioning unit provides insights based on the information visualized by the visualization unit. The provisioning unit provides insights for optimizing marketing strategies and determining the direction of product development. The provisioning unit can provide insights for optimizing marketing strategies based on consumer trends. The provisioning unit can also provide insights for determining the direction of product development based on competitor trends. The provisioning unit can also provide insights for optimizing marketing strategies based on consumer needs.As a result, the trend analysis assistant according to this embodiment can analyze search terms on the internet and topics on social media in real time and visualize trends.

[0030] The data collection unit gathers search terms and social media topics from the internet. Specifically, it uses web crawlers and APIs to obtain data from search engines and social media platforms. For example, it uses search engine APIs to periodically retrieve data on how often specific keywords are searched and stores it in a database. It also uses social media APIs to collect data on how popular specific hashtags and topics are. The data collection unit collects this data in real time and has an algorithm that prioritizes data collection for rapidly trending keywords and topics. For example, if a particular keyword is trending, it prioritizes collecting posts and comments related to that keyword and stores them in the database. Furthermore, the data collection unit has a function to filter out spam and bot posts to ensure data reliability. This allows the data collection unit to efficiently collect useful information from the vast amount of data on the internet and provide it to the analysis unit. Because the data collection unit can flexibly set the frequency and target of data collection, it is also possible to collect data tailored to specific campaigns and events. For example, when a new product is launched, it can intensively collect keywords and topics related to that product to help in formulating marketing strategies.

[0031] The analysis department analyzes the data collected by the data collection department. Specifically, the analysis department uses natural language processing (NLP) techniques and machine learning algorithms to classify, aggregate, and analyze the collected data. For example, it performs text mining on collected search terms and social media posts to extract frequently occurring keywords and phrases. This allows for an understanding of current trends and consumer interests. Furthermore, the analysis department performs sentiment analysis, classifying the sentiment of consumer posts and comments as positive, negative, or neutral. This allows for a quantitative evaluation of consumer reactions to specific topics. Based on this data, the analysis department can analyze consumer trends and needs and understand the actions of competitors. For example, it can analyze consumer evaluations and opinions on specific products to reveal their strengths and weaknesses. It can also analyze posts about competitors' products and services to evaluate their strategies and market position. Based on these analysis results, the analysis department provides insights to propose marketing strategies and product development directions. Furthermore, the analysis department can compare historical and current data to detect changes in trends and the emergence of new trends. This allows companies to quickly grasp market trends and take appropriate action.

[0032] The Visualization Unit visualizes the analysis results obtained by the Analysis Unit. Specifically, the Visualization Unit uses tools and libraries for visually representing data to create graphs, charts, dashboards, and more. For example, it can display search term trends using line graphs and bar graphs, allowing for an intuitive understanding of fluctuations in the search frequency of specific keywords. It can also display consumer trends using pie charts and heatmaps, enabling users to quickly grasp which topics are attracting the most interest. Furthermore, it can display competitor trends using radar charts and bubble charts, allowing for visual comparisons with competitors. The Visualization Unit makes these visual representations interactive, enabling users to analyze the data in detail. For example, users can filter data by selecting a specific period or region to view detailed trends. The Visualization Unit also updates analysis results in real time, providing information based on the latest data. This allows users to always grasp the latest trend information and make quick decisions. In addition, the Visualization Unit can output analysis results in report format and share them with stakeholders. This facilitates strategic planning and decision-making based on the analysis results.

[0033] The Insights Department provides insights based on information visualized by the Visualization Department. Specifically, the Insights Department provides insights to optimize marketing strategies and determine the direction of product development. For example, based on consumer trends, it predicts which products and services will see increased demand in the future and optimizes marketing strategies. It also determines the direction of its own product development based on the actions of competitors and proposes strategies to strengthen its competitiveness. The Insights Department provides these insights in the form of reports and presentations to help management and marketing personnel make quick decisions. Furthermore, the Insights Department collects feedback from users and continuously improves the accuracy and usefulness of the insights it provides. For example, it analyzes the results of marketing campaigns, evaluates which strategies were effective, and incorporates these findings into future campaigns. The Insights Department also proposes action plans based on the insights and provides support for implementing specific measures. In this way, the Insights Department can play a crucial role in enabling companies to respond quickly to market changes and maintain their competitiveness.

[0034] The news article collection unit collects news articles. The news article collection unit collects news articles from, for example, specific news sites. The news article collection unit can collect news articles using, for example, RSS feeds. The news article collection unit can also collect news articles using, for example, the API of a specific news site. The news article collection unit can also collect news articles related to, for example, specific keywords. This allows the news article collection unit to understand consumer trends and the competitive landscape. Some or all of the above-described processes in the news article collection unit may be performed using, for example, AI, or not using AI. For example, the news article collection unit can input news articles collected from a specific news site into a generating AI and have the generating AI perform analysis of the news articles.

[0035] The blog post collection unit collects blog posts. The blog post collection unit collects blog posts from, for example, a specific blog platform. The blog post collection unit can collect blog posts using, for example, an RSS feed. The blog post collection unit can also collect blog posts using, for example, the API of a specific blog platform. The blog post collection unit can also collect blog posts related to, for example, specific keywords. This allows the blog post collection unit to understand consumer trends and the competitive landscape. Some or all of the above processing in the blog post collection unit may be performed using, for example, AI, or not using AI. For example, the blog post collection unit can input blog posts collected from a specific blog platform into a generating AI and have the generating AI perform analysis of the blog posts.

[0036] The forum collection unit collects forum data. For example, the forum collection unit collects forum data from a specific forum site. The forum collection unit can collect forum data using an API, for example. The forum collection unit can also collect forum data related to a specific keyword, for example. The forum collection unit can also collect forum data related to a specific topic, for example. This allows the forum collection unit to understand consumer trends and competitive situations. Some or all of the above processing in the forum collection unit may be performed using AI, for example, or not using AI. For example, the forum collection unit can input forum data collected from a specific forum site into a generating AI and have the generating AI perform analysis of the forum data.

[0037] The review site collection unit collects data from review sites. The review site collection unit can, for example, collect review site data from specific review sites. The review site collection unit can, for example, collect review site data using APIs. The review site collection unit can also, for example, collect review site data related to specific keywords. The review site collection unit can also, for example, collect review site data related to specific products. This allows the review site collection unit to understand consumer trends and competitive situations. Some or all of the above processing in the review site collection unit may be performed using AI, for example, or without AI. For example, the review site collection unit can input review site data collected from specific review sites into a generating AI and have the generating AI perform analysis of the review site data.

[0038] The analysis unit can analyze the collected data to understand consumer trends and the competitive landscape. For example, the analysis unit can analyze consumer trends based on the collected data. The analysis unit can also analyze the actions of competitors based on the collected data. The analysis unit can also analyze consumer needs based on the collected data. The analysis unit can also analyze consumer dissatisfaction based on the collected data. In this way, the analysis unit can understand consumer trends and the competitive landscape. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform an analysis of consumer trends and the competitive landscape.

[0039] The service provider can provide insights for marketing strategies and product development based on the analysis results. For example, the service provider can provide insights to optimize marketing strategies and determine the direction of product development. For example, the service provider can provide insights to optimize marketing strategies based on consumer trends. For example, the service provider can also provide insights to determine the direction of product development based on the actions of competitors. For example, the service provider can also provide insights to optimize marketing strategies based on consumer needs. This allows the service provider to support rapid decision-making by companies and individuals. Some or all of the above-described processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the analysis results into a generating AI and have the generating AI perform the task of providing insights for marketing strategies and product development.

[0040] The data collection unit can change the frequency of data collection based on specific time periods or events. For example, the collection unit can increase the frequency of collecting relevant data before and after major events or holidays. It can also increase the frequency of collection during times when social media activity increases, such as at night or on weekends. It can also increase the frequency of collection of relevant data around the time of a new product launch. This allows the collection unit to collect data more effectively based on specific time periods or events. Some or all of the above processing in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can have a generating AI execute an algorithm to change the collection frequency based on specific time periods or events.

[0041] The data collection unit can prioritize the collection of highly relevant data by referring to the user's past search history during the collection process. For example, the data collection unit can prioritize the collection of data related to keywords the user has searched for in the past. The data collection unit can also prioritize the collection of data related to websites the user has visited in the past. The data collection unit can also prioritize the collection of data related to topics the user has shown interest in in the past. In this way, the data collection unit can prioritize the collection of highly relevant data by referring to the user's past search 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 search history into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during the collection process. For example, if the user is in a specific region, the data collection unit can prioritize the collection of news and topics related to that region. For example, if the user is traveling, the data collection unit can prioritize the collection of information related to the travel destination. For example, if the user is participating in a specific event, the data collection unit can prioritize the collection of information related to that event. In this way, the data collection unit can prioritize the collection of highly relevant data by considering 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 into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0043] The data collection unit can analyze the user's social media activity and collect relevant data during the collection process. For example, the data collection unit can analyze the content of posts from accounts the user follows and collect relevant data. The data collection unit can also analyze the activities of groups and communities the user participates in and collect relevant data. The data collection unit can also analyze content shared by the user and collect relevant data. 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.

[0044] 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 can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. The analysis unit can also apply multiple analysis methods to high-importance data and a single analysis method to low-importance data. For example, the analysis unit can perform real-time analysis on high-importance data and batch processing analysis on low-importance data. This allows the analysis unit to perform more effective analysis based on the importance of the data. 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 importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis methods depending on the data category during analysis. For example, the analysis unit can apply natural language processing to text data and image recognition to image data. For example, the analysis unit can apply sentiment analysis to social media data and topic modeling to news article data. For example, the analysis unit can apply evaluation analysis to review site data and community detection to forum data. This allows the analysis unit to provide more appropriate analysis results depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis methods.

[0046] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data and postpone the analysis of older data. The analysis unit may also prioritize the analysis of data related to a specific event or campaign. For example, the analysis unit may prioritize the analysis of data with high urgency and postpone the analysis of data with low urgency. This allows the analysis unit to perform analysis more quickly based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit may input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant data and postpone the analysis of less relevant data. The analysis unit may also prioritize the analysis of data related to a specific topic. For example, the analysis unit may prioritize the analysis of data related to user interests. This allows the analysis unit to perform more effective analysis based on the relevance of the data. 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 may input the relevance of the data into a generating AI and have the generating AI adjust the order of analysis.

[0048] The visualization unit can adjust the level of detail of the visualization based on the importance of the data during visualization. For example, the visualization unit can provide detailed graphs and charts for highly important data and simplified graphs and charts for less important data. The visualization unit can also apply multiple visualization methods to highly important data and a single visualization method to less important data. For example, the visualization unit can provide interactive visualizations for highly important data and static visualizations for less important data. This allows the visualization unit to perform more effective visualizations based on the importance of the data. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the visualization.

[0049] The visualization unit can apply different visualization methods depending on the data category during visualization. For example, the visualization unit can use word clouds for text data and bar graphs or line graphs for numerical data. For example, the visualization unit can use network graphs for social media data and timelines for news article data. For example, the visualization unit can use rating distribution graphs for review site data and heatmaps for forum data. This allows the visualization unit to perform more appropriate visualizations depending on the data category. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the data category into a generating AI and have the generating AI apply different visualization methods.

[0050] The visualization unit can determine visualization priorities based on the data submission date during visualization. For example, the visualization unit may prioritize the visualization of the most recent data and postpone older data. The visualization unit may also prioritize the visualization of data related to a specific event or campaign. For example, the visualization unit may prioritize the visualization of urgent data and postpone less urgent data. This allows the visualization unit to perform visualizations more quickly based on the data submission date. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the data submission date into a generating AI and have the generating AI determine the visualization priorities.

[0051] The visualization unit can adjust the order of visualization based on the relevance of the data during visualization. For example, the visualization unit can prioritize the visualization of highly relevant data and postpone the visualization of less relevant data. The visualization unit can also prioritize the visualization of data related to a specific topic. For example, the visualization unit can prioritize the visualization of data related to the user's interests. This allows the visualization unit to perform more effective visualization based on the relevance of the data. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the visualization order.

[0052] The delivery unit can adjust the level of detail provided based on the importance of the insights at the time of delivery. For example, the delivery unit can provide detailed explanations for high-importance insights and simplified explanations for low-importance insights. For example, the delivery unit can provide multi-perspective analysis for high-importance insights and a single-perspective analysis for low-importance insights. For example, the delivery unit can provide interactive elements for high-importance insights and static content for low-importance insights. This allows the delivery unit to provide more effective insights based on their importance. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the importance of the insights into a generating AI and have the generating AI adjust the level of detail of the delivery.

[0053] The delivery unit can apply different delivery methods depending on the category of the insight at the time of delivery. For example, the delivery unit may use graphs and charts for marketing-related insights and prototypes and mockups for product development-related insights. For example, the delivery unit may use timelines for consumer trend-related insights and comparison tables for competitive landscape-related insights. For example, the delivery unit may use survey results for needs-related insights and reviews and comments for dissatisfaction-related insights. This allows the delivery unit to provide more appropriate insights depending on the category of the insight. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the category of the insight into a generating AI and have the generating AI apply different delivery methods.

[0054] The delivery unit can prioritize the delivery of insights based on when they were submitted. For example, the delivery unit might prioritize the most recent insights and postpone older ones. The delivery unit might also prioritize insights related to specific events or campaigns. For example, the delivery unit might prioritize urgent insights and postpone less urgent ones. This allows the delivery unit to deliver insights more quickly based on when they were submitted. Some or all of the above processes in the delivery unit may be performed using AI, for example, or not. For example, the delivery unit could input the submission dates of insights into a generating AI and have the generating AI determine the delivery priorities.

[0055] The delivery unit can adjust the order of delivery based on the relevance of the insights at the time of delivery. For example, the delivery unit can prioritize the delivery of highly relevant insights and postpone the delivery of less relevant insights. The delivery unit can also prioritize the delivery of insights related to a specific topic. The delivery unit can also prioritize the delivery of insights related to user interests. This enables the delivery unit to deliver insights more effectively based on their relevance. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the relevance of the insights into a generating AI and have the generating AI adjust the order of delivery.

[0056] The news article collection unit can change the collection frequency of news articles based on specific times of day or events. For example, the news article collection unit can increase the collection frequency of relevant news articles before and after major events or holidays. It can also increase the collection frequency during times when news updates are more frequent, such as at night or on weekends. It can also increase the collection frequency of relevant news articles around the time of new product launches. This allows the news article collection unit to collect news articles more effectively based on specific times of day or events. Some or all of the above processing in the news article collection unit may be performed using AI, for example, or not. For example, the news article collection unit can have a generating AI execute an algorithm to change the collection frequency based on specific times of day or events.

[0057] The news article collection unit can prioritize collecting highly relevant articles by considering the user's geographical location information when collecting news articles. For example, if the user is in a specific region, the news article collection unit can prioritize collecting news articles related to that region. For example, if the user is traveling, the news article collection unit can also prioritize collecting news articles related to the travel destination. For example, if the user is participating in a specific event, the news article collection unit can also prioritize collecting news articles related to that event. In this way, the news article collection unit can prioritize collecting highly relevant news articles by considering the user's geographical location information. Some or all of the above processing in the news article collection unit may be performed using AI, for example, or without AI. For example, the news article collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the priority collection of highly relevant news articles.

[0058] The blog post collection unit can change the collection frequency of blog posts based on specific times of day or events. For example, the blog post collection unit can increase the collection frequency of relevant blog posts before and after major events or holidays. It can also increase the collection frequency during times when blog updates increase, such as at night or on weekends. It can also increase the collection frequency of relevant blog posts around the time of a new product launch. This allows the blog post collection unit to collect blog posts more effectively based on specific times of day or events. Some or all of the above processing in the blog post collection unit may be performed using AI, for example, or not. For example, the blog post collection unit can have a generating AI execute an algorithm to change the collection frequency based on specific times of day or events.

[0059] The blog post collection unit can prioritize collecting blog posts that are highly relevant, taking into account the user's geographical location information. For example, if the user is in a specific region, the blog post collection unit can prioritize collecting blog posts related to that region. For example, if the user is traveling, the blog post collection unit can prioritize collecting blog posts related to their travel destination. For example, if the user is participating in a specific event, the blog post collection unit can prioritize collecting blog posts related to that event. In this way, the blog post collection unit can prioritize collecting blog posts that are highly relevant by taking into account the user's geographical location information. Some or all of the above processing in the blog post collection unit may be performed using AI, for example, or without AI. For example, the blog post collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the priority collection of highly relevant blog posts.

[0060] The forum data collection unit can vary the collection frequency based on specific time periods or events when collecting forum data. For example, the forum data collection unit can increase the collection frequency of relevant forum data before and after major events or holidays. It can also increase the collection frequency during times when forum activity increases, such as at night or on weekends. For example, it can increase the collection frequency of relevant forum data around the time of a new product launch. This allows the forum data collection unit to collect forum data more effectively based on specific time periods or events. Some or all of the above processing in the forum data collection unit may be performed using AI, for example, or not. For example, the forum data collection unit can have a generating AI execute an algorithm to vary the collection frequency based on specific time periods or events.

[0061] The forum collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting forum data. For example, if a user is in a specific region, the forum collection unit will prioritize the collection of forum data related to that region. For example, if a user is traveling, the forum collection unit can also prioritize the collection of forum data related to their travel destination. For example, if a user is participating in a specific event, the forum collection unit can also prioritize the collection of forum data related to that event. In this way, the forum collection unit can prioritize the collection of highly relevant forum data by considering the user's geographical location. Some or all of the above processing in the forum collection unit may be performed using AI, for example, or without AI. For example, the forum collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the priority collection of highly relevant forum data.

[0062] The review site collection unit can change the collection frequency of review site data based on specific times of day or events. For example, the review site collection unit can increase the collection frequency of relevant reviews before and after major events or holidays. It can also increase the collection frequency during times when reviews are more frequently posted, such as at night or on weekends. It can also increase the collection frequency of relevant reviews around the time of a new product launch. This allows the review site collection unit to collect review site data more effectively based on specific times of day or events. Some or all of the above processing in the review site collection unit may be performed using AI, for example, or not. For example, the review site collection unit can have a generating AI execute an algorithm to change the collection frequency based on specific times of day or events.

[0063] The review site collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting review site data. For example, if the user is in a specific region, the review site collection unit can prioritize the collection of reviews related to that region. For example, if the user is traveling, the review site collection unit can prioritize the collection of reviews related to the travel destination. For example, if the user is participating in a specific event, the review site collection unit can prioritize the collection of reviews related to that event. In this way, the review site collection unit can prioritize the collection of highly relevant review site data by considering the user's geographical location. Some or all of the above processing in the review site collection unit may be performed using AI, for example, or without AI. For example, the review site collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the priority collection of highly relevant review site data.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] The trend analysis assistant can further prioritize the collection of highly relevant data by considering the user's geographical location. For example, if the user is in a specific region, it can prioritize the collection of news and topics related to that region. If the user is traveling, it can also prioritize the collection of information related to their travel destination. If the user is attending a specific event, it can also prioritize the collection of information related to that event. In this way, by considering the user's geographical location, it is possible to prioritize the collection of highly relevant data. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0066] The trend analysis assistant can further prioritize the collection of highly relevant data by referring to the user's past search history. For example, it can prioritize the collection of data related to keywords the user has searched for in the past. It can also prioritize the collection of data related to websites the user has visited in the past. It can also prioritize the collection of data related to topics the user has shown interest in in the past. In this way, highly relevant data can be prioritized by referring to the user's past search history. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's past search history into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0067] The trend analysis assistant can further vary the data collection frequency in the data collection unit based on specific time periods or events. For example, it can increase the collection frequency of relevant data before and after major events or holidays. It can also increase the collection frequency during times when social media activity is high, such as at night or on weekends. It can also increase the collection frequency of relevant data around the time of a new product launch. This enables more effective data collection based on specific time periods or events. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can have a generating AI execute an algorithm to vary the collection frequency based on specific time periods or events.

[0068] The trend analysis assistant can further adjust the level of detail of the analysis in the analysis unit based on the importance of the data. For example, it can perform detailed analysis on high-importance data and simplified analysis on low-importance data. It can also apply multiple analysis methods to high-importance data and a single analysis method to low-importance data. High-importance data can be analyzed in real time, while low-importance data can be analyzed in batch processing. This enables more effective analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. 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.

[0069] The trend analysis assistant can further apply different visualization methods in its visualization unit depending on the data category. For example, it can use word clouds for text data and bar graphs or line graphs for numerical data. It can also use network graphs for social media data and timelines for news article data. It can use rating distribution graphs for review site data and heatmaps for forum data. This allows for more appropriate visualization depending on the data category. Some or all of the above processing in the visualization unit may be performed using AI or not. For example, the visualization unit can input data categories into a generating AI and have the generating AI apply different visualization methods.

[0070] The trend analysis assistant can further adjust the level of detail provided in the delivery section based on the importance of the insights. For example, it can provide detailed explanations for high-importance insights and simplified explanations for low-importance insights. It can also provide multi-perspective analysis for high-importance insights and a single-perspective analysis for low-importance insights. It can also provide interactive elements for high-importance insights and static content for low-importance insights. This enables the delivery of more effective insights based on their importance. Some or all of the above processing in the delivery section may be performed using AI or not. For example, the delivery section can input the importance of the insights into a generating AI and have the generating AI adjust the level of detail of the delivery.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The data collection unit gathers search terms from the internet and topics from social media. For example, it collects how often specific keywords are searched and what topics are trending on social media. Furthermore, if a particular keyword or hashtag is trending, it can prioritize the collection of related data. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it can analyze trends, consumer needs, and the activities of competitors based on the collected data. Step 3: The visualization unit visualizes the analysis results obtained by the analysis unit. For example, it intuitively displays search term trends, consumer trends, competitor activity, and consumer needs using graphs and dashboards. Step 4: The delivery unit provides insights based on the information visualized by the visualization unit. For example, it provides insights to optimize marketing strategies or determine the direction of product development.

[0073] (Example of form 2) The trend analysis assistant according to an embodiment of the present invention is a system that analyzes search terms on the internet and topics on social media in real time and visualizes trends. This trend analysis assistant allows companies and individuals to quickly grasp market trends and use this information to inform marketing strategies and product development. Furthermore, it collects and analyzes data from news articles, blog posts, forums, review sites, etc., to visualize consumer trends and competitive situations. It helps to understand consumer needs and frustrations in real time and supports rapid decision-making. For example, the trend analysis assistant collects search terms on the internet and topics on social media. For example, it collects how often specific keywords are searched and what topics are trending on social media. This information is analyzed in real time by AI. Next, it visualizes trends based on the collected data. For example, it intuitively displays search term trends in graphs and dashboards. This allows companies and individuals to quickly grasp market trends. Furthermore, it collects data from news articles, blog posts, forums, review sites, etc., to analyze consumer trends and competitive situations. For example, it can understand consumer evaluations of specific products and the trends of competitors. This information is analyzed by AI, allowing for real-time understanding of consumer needs and frustrations. Finally, the analysis results support rapid decision-making. For example, it provides insights to optimize marketing strategies and determine the direction of product development. This enables businesses and individuals to implement effective marketing strategies and product development. This system allows businesses and individuals to quickly grasp market trends and implement effective marketing strategies and product development. For example, if a particular keyword is trending, offering products or services related to that keyword can attract consumer interest. Furthermore, real-time understanding of consumer needs and frustrations allows for rapid response. For example, if consumers are dissatisfied with a particular product, improvement measures to address that dissatisfaction can be implemented quickly.This allows trend analysis assistants to enable businesses and individuals to quickly grasp market trends and develop effective marketing strategies and products.

[0074] The trend analysis assistant according to this embodiment comprises a collection unit, an analysis unit, a visualization unit, and a provision unit. The collection unit collects search terms on the internet and topics on social media. For example, the collection unit collects how often a particular keyword is searched and what topics are trending on social media. For example, if a particular keyword is trending rapidly, the collection unit can prioritize collecting data related to that keyword. For example, if a particular hashtag is trending rapidly, the collection unit can prioritize collecting data related to that hashtag. For example, if a particular topic is trending on social media, the collection unit can prioritize collecting data related to that topic. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes trends based on the collected data. For example, the analysis unit can analyze consumer trends based on the collected data. For example, the analysis unit can analyze the actions of competitors based on the collected data. For example, the analysis unit can analyze consumer needs based on the collected data. The visualization unit visualizes the analysis results obtained by the analysis unit. The visualization unit intuitively displays search term trends in graphs and dashboards. The visualization unit can intuitively display consumer trends in graphs and dashboards. The visualization unit can also intuitively display competitor trends in graphs and dashboards. The visualization unit can also intuitively display consumer needs in graphs and dashboards. The provisioning unit provides insights based on the information visualized by the visualization unit. The provisioning unit provides insights for optimizing marketing strategies and determining the direction of product development. The provisioning unit can provide insights for optimizing marketing strategies based on consumer trends. The provisioning unit can also provide insights for determining the direction of product development based on competitor trends. The provisioning unit can also provide insights for optimizing marketing strategies based on consumer needs.As a result, the trend analysis assistant according to this embodiment can analyze search terms on the internet and topics on social media in real time and visualize trends.

[0075] The data collection unit gathers search terms and social media topics from the internet. Specifically, it uses web crawlers and APIs to obtain data from search engines and social media platforms. For example, it uses search engine APIs to periodically retrieve data on how often specific keywords are searched and stores it in a database. It also uses social media APIs to collect data on how popular specific hashtags and topics are. The data collection unit collects this data in real time and has an algorithm that prioritizes data collection for rapidly trending keywords and topics. For example, if a particular keyword is trending, it prioritizes collecting posts and comments related to that keyword and stores them in the database. Furthermore, the data collection unit has a function to filter out spam and bot posts to ensure data reliability. This allows the data collection unit to efficiently collect useful information from the vast amount of data on the internet and provide it to the analysis unit. Because the data collection unit can flexibly set the frequency and target of data collection, it is also possible to collect data tailored to specific campaigns and events. For example, when a new product is launched, it can intensively collect keywords and topics related to that product to help in formulating marketing strategies.

[0076] The analysis department analyzes the data collected by the data collection department. Specifically, the analysis department uses natural language processing (NLP) techniques and machine learning algorithms to classify, aggregate, and analyze the collected data. For example, it performs text mining on collected search terms and social media posts to extract frequently occurring keywords and phrases. This allows for an understanding of current trends and consumer interests. Furthermore, the analysis department performs sentiment analysis, classifying the sentiment of consumer posts and comments as positive, negative, or neutral. This allows for a quantitative evaluation of consumer reactions to specific topics. Based on this data, the analysis department can analyze consumer trends and needs and understand the actions of competitors. For example, it can analyze consumer evaluations and opinions on specific products to reveal their strengths and weaknesses. It can also analyze posts about competitors' products and services to evaluate their strategies and market position. Based on these analysis results, the analysis department provides insights to propose marketing strategies and product development directions. Furthermore, the analysis department can compare historical and current data to detect changes in trends and the emergence of new trends. This allows companies to quickly grasp market trends and take appropriate action.

[0077] The Visualization Unit visualizes the analysis results obtained by the Analysis Unit. Specifically, the Visualization Unit uses tools and libraries for visually representing data to create graphs, charts, dashboards, and more. For example, it can display search term trends using line graphs and bar graphs, allowing for an intuitive understanding of fluctuations in the search frequency of specific keywords. It can also display consumer trends using pie charts and heatmaps, enabling users to quickly grasp which topics are attracting the most interest. Furthermore, it can display competitor trends using radar charts and bubble charts, allowing for visual comparisons with competitors. The Visualization Unit makes these visual representations interactive, enabling users to analyze the data in detail. For example, users can filter data by selecting a specific period or region to view detailed trends. The Visualization Unit also updates analysis results in real time, providing information based on the latest data. This allows users to always grasp the latest trend information and make quick decisions. In addition, the Visualization Unit can output analysis results in report format and share them with stakeholders. This facilitates strategic planning and decision-making based on the analysis results.

[0078] The Insights Department provides insights based on information visualized by the Visualization Department. Specifically, the Insights Department provides insights to optimize marketing strategies and determine the direction of product development. For example, based on consumer trends, it predicts which products and services will see increased demand in the future and optimizes marketing strategies. It also determines the direction of its own product development based on the actions of competitors and proposes strategies to strengthen its competitiveness. The Insights Department provides these insights in the form of reports and presentations to help management and marketing personnel make quick decisions. Furthermore, the Insights Department collects feedback from users and continuously improves the accuracy and usefulness of the insights it provides. For example, it analyzes the results of marketing campaigns, evaluates which strategies were effective, and incorporates these findings into future campaigns. The Insights Department also proposes action plans based on the insights and provides support for implementing specific measures. In this way, the Insights Department can play a crucial role in enabling companies to respond quickly to market changes and maintain their competitiveness.

[0079] The news article collection unit collects news articles. The news article collection unit collects news articles from, for example, specific news sites. The news article collection unit can collect news articles using, for example, RSS feeds. The news article collection unit can also collect news articles using, for example, the API of a specific news site. The news article collection unit can also collect news articles related to, for example, specific keywords. This allows the news article collection unit to understand consumer trends and the competitive landscape. Some or all of the above-described processes in the news article collection unit may be performed using, for example, AI, or not using AI. For example, the news article collection unit can input news articles collected from a specific news site into a generating AI and have the generating AI perform analysis of the news articles.

[0080] The blog post collection unit collects blog posts. The blog post collection unit collects blog posts from, for example, a specific blog platform. The blog post collection unit can collect blog posts using, for example, an RSS feed. The blog post collection unit can also collect blog posts using, for example, the API of a specific blog platform. The blog post collection unit can also collect blog posts related to, for example, specific keywords. This allows the blog post collection unit to understand consumer trends and the competitive landscape. Some or all of the above processing in the blog post collection unit may be performed using, for example, AI, or not using AI. For example, the blog post collection unit can input blog posts collected from a specific blog platform into a generating AI and have the generating AI perform analysis of the blog posts.

[0081] The forum collection unit collects forum data. For example, the forum collection unit collects forum data from a specific forum site. The forum collection unit can collect forum data using an API, for example. The forum collection unit can also collect forum data related to a specific keyword, for example. The forum collection unit can also collect forum data related to a specific topic, for example. This allows the forum collection unit to understand consumer trends and competitive situations. Some or all of the above processing in the forum collection unit may be performed using AI, for example, or not using AI. For example, the forum collection unit can input forum data collected from a specific forum site into a generating AI and have the generating AI perform analysis of the forum data.

[0082] The review site collection unit collects data from review sites. The review site collection unit can, for example, collect review site data from specific review sites. The review site collection unit can, for example, collect review site data using APIs. The review site collection unit can also, for example, collect review site data related to specific keywords. The review site collection unit can also, for example, collect review site data related to specific products. This allows the review site collection unit to understand consumer trends and competitive situations. Some or all of the above processing in the review site collection unit may be performed using AI, for example, or without AI. For example, the review site collection unit can input review site data collected from specific review sites into a generating AI and have the generating AI perform analysis of the review site data.

[0083] The analysis unit can analyze the collected data to understand consumer trends and the competitive landscape. For example, the analysis unit can analyze consumer trends based on the collected data. The analysis unit can also analyze the actions of competitors based on the collected data. The analysis unit can also analyze consumer needs based on the collected data. The analysis unit can also analyze consumer dissatisfaction based on the collected data. In this way, the analysis unit can understand consumer trends and the competitive landscape. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform an analysis of consumer trends and the competitive landscape.

[0084] The service provider can provide insights for marketing strategies and product development based on the analysis results. For example, the service provider can provide insights to optimize marketing strategies and determine the direction of product development. For example, the service provider can provide insights to optimize marketing strategies based on consumer trends. For example, the service provider can also provide insights to determine the direction of product development based on the actions of competitors. For example, the service provider can also provide insights to optimize marketing strategies based on consumer needs. This allows the service provider to support rapid decision-making by companies and individuals. Some or all of the above-described processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the analysis results into a generating AI and have the generating AI perform the task of providing insights for marketing strategies and product development.

[0085] The data collection unit can estimate the user's emotions and adjust the types of data collected based on the estimated emotions. For example, if the user is excited, the data collection unit may prioritize collecting entertainment-related data. If the user is calm, the data collection unit may also prioritize collecting business-related data. If the user is stressed, the data collection unit may also prioritize collecting relaxation and health-related data. This allows the data collection unit to collect more appropriate data 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 processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the types of data to be collected.

[0086] The data collection unit can change the frequency of data collection based on specific time periods or events. For example, the collection unit can increase the frequency of collecting relevant data before and after major events or holidays. It can also increase the frequency of collection during times when social media activity increases, such as at night or on weekends. It can also increase the frequency of collection of relevant data around the time of a new product launch. This allows the collection unit to collect data more effectively based on specific time periods or events. Some or all of the above processing in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can have a generating AI execute an algorithm to change the collection frequency based on specific time periods or events.

[0087] The data collection unit can prioritize the collection of highly relevant data by referring to the user's past search history during the collection process. For example, the data collection unit can prioritize the collection of data related to keywords the user has searched for in the past. The data collection unit can also prioritize the collection of data related to websites the user has visited in the past. The data collection unit can also prioritize the collection of data related to topics the user has shown interest in in the past. In this way, the data collection unit can prioritize the collection of highly relevant data by referring to the user's past search 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 search history into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0088] 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 excited, the data collection unit may prioritize collecting the latest trend information. For example, if the user is relaxed, the data collection unit may prioritize collecting long-term trend information. For example, if the user is stressed, the data collection unit may prioritize collecting relaxation and health-related information. This allows the data collection unit to prioritize collecting more appropriate data 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 or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of data to collect.

[0089] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during the collection process. For example, if the user is in a specific region, the data collection unit can prioritize the collection of news and topics related to that region. For example, if the user is traveling, the data collection unit can prioritize the collection of information related to the travel destination. For example, if the user is participating in a specific event, the data collection unit can prioritize the collection of information related to that event. In this way, the data collection unit can prioritize the collection of highly relevant data by considering 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 into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0090] The data collection unit can analyze the user's social media activity and collect relevant data during the collection process. For example, the data collection unit can analyze the content of posts from accounts the user follows and collect relevant data. The data collection unit can also analyze the activities of groups and communities the user participates in and collect relevant data. The data collection unit can also analyze content shared by the user and collect relevant data. 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.

[0091] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is excited, the analysis unit may use a simplified algorithm to provide results quickly. For example, if the user is relaxed, the analysis unit may also use a complex algorithm to perform a detailed analysis. For example, if the user is stressed, the analysis unit may prioritize analyzing information that helps reduce stress. This allows the analysis unit to provide more appropriate analysis results 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 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 analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the analysis algorithm.

[0092] 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 can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. The analysis unit can also apply multiple analysis methods to high-importance data and a single analysis method to low-importance data. For example, the analysis unit can perform real-time analysis on high-importance data and batch processing analysis on low-importance data. This allows the analysis unit to perform more effective analysis based on the importance of the data. 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 importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0093] The analysis unit can apply different analysis methods depending on the data category during analysis. For example, the analysis unit can apply natural language processing to text data and image recognition to image data. For example, the analysis unit can apply sentiment analysis to social media data and topic modeling to news article data. For example, the analysis unit can apply evaluation analysis to review site data and community detection to forum data. This allows the analysis unit to provide more appropriate analysis results depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis methods.

[0094] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is excited, the analysis unit can provide a visually stimulating display method. For example, if the user is relaxed, the analysis unit can also provide a calm display method. For example, if the user is stressed, the analysis unit can also provide a simple and highly visible display method. This allows the analysis unit to provide a more appropriate display based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0095] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data and postpone the analysis of older data. The analysis unit may also prioritize the analysis of data related to a specific event or campaign. For example, the analysis unit may prioritize the analysis of data with high urgency and postpone the analysis of data with low urgency. This allows the analysis unit to perform analysis more quickly based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit may input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0096] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant data and postpone the analysis of less relevant data. The analysis unit may also prioritize the analysis of data related to a specific topic. For example, the analysis unit may prioritize the analysis of data related to user interests. This allows the analysis unit to perform more effective analysis based on the relevance of the data. 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 may input the relevance of the data into a generating AI and have the generating AI adjust the order of analysis.

[0097] The visualization unit can estimate the user's emotions and adjust the visualization's presentation based on the estimated emotions. For example, if the user is excited, the visualization unit can provide visually stimulating graphs or charts. If the user is relaxed, the visualization unit can also provide graphs or charts with calming colors. If the user is stressed, the visualization unit can also provide simple and highly visible graphs or charts. This allows the visualization unit to provide more appropriate visualizations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the visualization unit may be performed using AI, or not using AI. For example, the visualization unit can input user emotion data into the generative AI and have the generative AI adjust the visualization's presentation.

[0098] The visualization unit can adjust the level of detail of the visualization based on the importance of the data during visualization. For example, the visualization unit can provide detailed graphs and charts for highly important data and simplified graphs and charts for less important data. The visualization unit can also apply multiple visualization methods to highly important data and a single visualization method to less important data. For example, the visualization unit can provide interactive visualizations for highly important data and static visualizations for less important data. This allows the visualization unit to perform more effective visualizations based on the importance of the data. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the visualization.

[0099] The visualization unit can apply different visualization methods depending on the data category during visualization. For example, the visualization unit can use word clouds for text data and bar graphs or line graphs for numerical data. For example, the visualization unit can use network graphs for social media data and timelines for news article data. For example, the visualization unit can use rating distribution graphs for review site data and heatmaps for forum data. This allows the visualization unit to perform more appropriate visualizations depending on the data category. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the data category into a generating AI and have the generating AI apply different visualization methods.

[0100] The visualization unit can estimate the user's emotions and adjust the length of the visualization based on the estimated emotions. For example, if the user is excited, the visualization unit can provide a short, concise visualization. If the user is relaxed, the visualization unit can also provide a longer visualization with detailed explanations. If the user is stressed, the visualization unit can also provide a short, simple, and highly visible visualization. This allows the visualization unit to provide more appropriate visualizations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI, or not. For example, the visualization unit can input user emotion data into a generative AI and have the generative AI adjust the length of the visualization.

[0101] The visualization unit can determine visualization priorities based on the data submission date during visualization. For example, the visualization unit may prioritize the visualization of the most recent data and postpone older data. The visualization unit may also prioritize the visualization of data related to a specific event or campaign. For example, the visualization unit may prioritize the visualization of urgent data and postpone less urgent data. This allows the visualization unit to perform visualizations more quickly based on the data submission date. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the data submission date into a generating AI and have the generating AI determine the visualization priorities.

[0102] The visualization unit can adjust the order of visualization based on the relevance of the data during visualization. For example, the visualization unit can prioritize the visualization of highly relevant data and postpone the visualization of less relevant data. The visualization unit can also prioritize the visualization of data related to a specific topic. For example, the visualization unit can prioritize the visualization of data related to the user's interests. This allows the visualization unit to perform more effective visualization based on the relevance of the data. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the visualization order.

[0103] The service provider can estimate the user's emotions and adjust the way the insights are presented based on the estimated emotions. For example, if the user is excited, the service provider can provide visually stimulating insights. If the user is relaxed, the service provider can also provide calmly presented insights. If the user is stressed, the service provider can also provide simple and easily understandable insights. This allows the service provider to provide more appropriate insights 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 processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the way the insights are presented.

[0104] The delivery unit can adjust the level of detail provided based on the importance of the insights at the time of delivery. For example, the delivery unit can provide detailed explanations for high-importance insights and simplified explanations for low-importance insights. For example, the delivery unit can provide multi-perspective analysis for high-importance insights and a single-perspective analysis for low-importance insights. For example, the delivery unit can provide interactive elements for high-importance insights and static content for low-importance insights. This allows the delivery unit to provide more effective insights based on their importance. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the importance of the insights into a generating AI and have the generating AI adjust the level of detail of the delivery.

[0105] The delivery unit can apply different delivery methods depending on the category of the insight at the time of delivery. For example, the delivery unit may use graphs and charts for marketing-related insights and prototypes and mockups for product development-related insights. For example, the delivery unit may use timelines for consumer trend-related insights and comparison tables for competitive landscape-related insights. For example, the delivery unit may use survey results for needs-related insights and reviews and comments for dissatisfaction-related insights. This allows the delivery unit to provide more appropriate insights depending on the category of the insight. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the category of the insight into a generating AI and have the generating AI apply different delivery methods.

[0106] The service provider can estimate the user's emotions and prioritize the insights it provides based on those emotions. For example, if the user is excited, the service provider might prioritize insights on the latest trends. If the user is relaxed, the service provider might prioritize insights on long-term trends. If the user is stressed, the service provider might prioritize insights that help reduce stress. This allows the service provider to prioritize more relevant insights 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 processing described above in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the priority of insights.

[0107] The delivery unit can prioritize the delivery of insights based on when they were submitted. For example, the delivery unit might prioritize the most recent insights and postpone older ones. The delivery unit might also prioritize insights related to specific events or campaigns. For example, the delivery unit might prioritize urgent insights and postpone less urgent ones. This allows the delivery unit to deliver insights more quickly based on when they were submitted. Some or all of the above processes in the delivery unit may be performed using AI, for example, or not. For example, the delivery unit could input the submission dates of insights into a generating AI and have the generating AI determine the delivery priorities.

[0108] The delivery unit can adjust the order of delivery based on the relevance of the insights at the time of delivery. For example, the delivery unit can prioritize the delivery of highly relevant insights and postpone the delivery of less relevant insights. The delivery unit can also prioritize the delivery of insights related to a specific topic. The delivery unit can also prioritize the delivery of insights related to user interests. This enables the delivery unit to deliver insights more effectively based on their relevance. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the relevance of the insights into a generating AI and have the generating AI adjust the order of delivery.

[0109] The news article collection unit can estimate the user's emotions and adjust the types of news articles it collects based on the estimated emotions. For example, if the user is excited, the news article collection unit may prioritize collecting entertainment-related news articles. For example, if the user is calm, the news article collection unit may prioritize collecting business-related news articles. For example, if the user is stressed, the news article collection unit may prioritize collecting relaxation or health-related news articles. This allows the news article collection unit to collect more appropriate news articles 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 news article collection unit may be performed using AI, or not using AI. For example, the news article collection unit can input user emotion data into a generative AI and have the generative AI adjust the types of news articles to collect.

[0110] The news article collection unit can change the collection frequency of news articles based on specific times of day or events. For example, the news article collection unit can increase the collection frequency of relevant news articles before and after major events or holidays. It can also increase the collection frequency during times when news updates are more frequent, such as at night or on weekends. It can also increase the collection frequency of relevant news articles around the time of new product launches. This allows the news article collection unit to collect news articles more effectively based on specific times of day or events. Some or all of the above processing in the news article collection unit may be performed using AI, for example, or not. For example, the news article collection unit can have a generating AI execute an algorithm to change the collection frequency based on specific times of day or events.

[0111] The news article collection unit can estimate the user's emotions and determine the priority of news articles to collect based on the estimated emotions. For example, if the user is excited, the news article collection unit may prioritize collecting news articles about the latest trends. For example, if the user is relaxed, the news article collection unit may also prioritize collecting news articles about long-term trends. For example, if the user is stressed, the news article collection unit may also prioritize collecting news articles that help reduce stress. This allows the news article collection unit to prioritize collecting more appropriate news articles 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 news article collection unit may be performed using AI or not using AI. For example, the news article collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of news articles to collect.

[0112] The news article collection unit can prioritize collecting highly relevant articles by considering the user's geographical location information when collecting news articles. For example, if the user is in a specific region, the news article collection unit can prioritize collecting news articles related to that region. For example, if the user is traveling, the news article collection unit can also prioritize collecting news articles related to the travel destination. For example, if the user is participating in a specific event, the news article collection unit can also prioritize collecting news articles related to that event. In this way, the news article collection unit can prioritize collecting highly relevant news articles by considering the user's geographical location information. Some or all of the above processing in the news article collection unit may be performed using AI, for example, or without AI. For example, the news article collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the priority collection of highly relevant news articles.

[0113] The blog post collection unit can estimate the user's emotions and adjust the types of blog posts it collects based on the estimated emotions. For example, if the user is excited, the blog post collection unit may prioritize collecting entertainment-related blog posts. For example, if the user is calm, the blog post collection unit may also prioritize collecting business-related blog posts. For example, if the user is stressed, the blog post collection unit may also prioritize collecting relaxation and health-related blog posts. This allows the blog post collection unit to collect more appropriate blog posts 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the blog post collection unit may be performed using AI or not. For example, the blog post collection unit can input user emotion data into a generative AI and have the generative AI adjust the types of blog posts to collect.

[0114] The blog post collection unit can change the collection frequency of blog posts based on specific times of day or events. For example, the blog post collection unit can increase the collection frequency of relevant blog posts before and after major events or holidays. It can also increase the collection frequency during times when blog updates increase, such as at night or on weekends. It can also increase the collection frequency of relevant blog posts around the time of a new product launch. This allows the blog post collection unit to collect blog posts more effectively based on specific times of day or events. Some or all of the above processing in the blog post collection unit may be performed using AI, for example, or not. For example, the blog post collection unit can have a generating AI execute an algorithm to change the collection frequency based on specific times of day or events.

[0115] The blog post collection unit can estimate the user's emotions and determine the priority of blog posts to collect based on the estimated emotions. For example, if the user is excited, the blog post collection unit may prioritize collecting blog posts about the latest trends. For example, if the user is relaxed, the blog post collection unit may also prioritize collecting blog posts about long-term trends. For example, if the user is stressed, the blog post collection unit may also prioritize collecting blog posts that help reduce stress. This allows the blog post collection unit to prioritize collecting more appropriate blog posts 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the blog post collection unit may be performed using AI or not. For example, the blog post collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of blog posts to collect.

[0116] The blog post collection unit can prioritize collecting blog posts that are highly relevant, taking into account the user's geographical location information. For example, if the user is in a specific region, the blog post collection unit can prioritize collecting blog posts related to that region. For example, if the user is traveling, the blog post collection unit can prioritize collecting blog posts related to their travel destination. For example, if the user is participating in a specific event, the blog post collection unit can prioritize collecting blog posts related to that event. In this way, the blog post collection unit can prioritize collecting blog posts that are highly relevant by taking into account the user's geographical location information. Some or all of the above processing in the blog post collection unit may be performed using AI, for example, or without AI. For example, the blog post collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the priority collection of highly relevant blog posts.

[0117] The forum collection unit can estimate the user's emotions and adjust the types of forum data to collect based on the estimated emotions. For example, if the user is excited, the forum collection unit may prioritize collecting entertainment-related forum data. For example, if the user is calm, the forum collection unit may prioritize collecting business-related forum data. For example, if the user is stressed, the forum collection unit may prioritize collecting relaxation or health-related forum data. This allows the forum collection unit to collect more appropriate forum data 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the forum collection unit may be performed using AI or not using AI. For example, the forum collection unit can input user emotion data into a generative AI and have the generative AI adjust the types of forum data to collect.

[0118] The forum data collection unit can vary the collection frequency based on specific time periods or events when collecting forum data. For example, the forum data collection unit can increase the collection frequency of relevant forum data before and after major events or holidays. It can also increase the collection frequency during times when forum activity increases, such as at night or on weekends. For example, it can increase the collection frequency of relevant forum data around the time of a new product launch. This allows the forum data collection unit to collect forum data more effectively based on specific time periods or events. Some or all of the above processing in the forum data collection unit may be performed using AI, for example, or not. For example, the forum data collection unit can have a generating AI execute an algorithm to vary the collection frequency based on specific time periods or events.

[0119] The forum collection unit can estimate the user's emotions and determine the priority of forum data to collect based on the estimated user emotions. For example, if the user is excited, the forum collection unit may prioritize collecting forum data on the latest trends. For example, if the user is relaxed, the forum collection unit may also prioritize collecting forum data on long-term trends. For example, if the user is stressed, the forum collection unit may also prioritize collecting forum data that helps reduce stress. This allows the forum collection unit to prioritize collecting more appropriate forum data 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 forum collection unit may be performed using AI or not using AI. For example, the forum collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of forum data to collect.

[0120] The forum collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting forum data. For example, if a user is in a specific region, the forum collection unit will prioritize the collection of forum data related to that region. For example, if a user is traveling, the forum collection unit can also prioritize the collection of forum data related to their travel destination. For example, if a user is participating in a specific event, the forum collection unit can also prioritize the collection of forum data related to that event. In this way, the forum collection unit can prioritize the collection of highly relevant forum data by considering the user's geographical location. Some or all of the above processing in the forum collection unit may be performed using AI, for example, or without AI. For example, the forum collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the priority collection of highly relevant forum data.

[0121] The review site collection unit can estimate the user's emotions and adjust the types of review site data to collect based on the estimated user emotions. For example, if the user is excited, the review site collection unit may prioritize collecting entertainment-related reviews. For example, if the user is calm, the review site collection unit may also prioritize collecting business-related reviews. For example, if the user is stressed, the review site collection unit may also prioritize collecting relaxation and health-related reviews. This allows the review site collection unit to collect more appropriate review site data 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the review site collection unit may be performed using AI or not using AI. For example, the review site collection unit can input user emotion data into a generative AI and have the generative AI adjust the types of review site data to collect.

[0122] The review site collection unit can change the collection frequency of review site data based on specific times of day or events. For example, the review site collection unit can increase the collection frequency of relevant reviews before and after major events or holidays. It can also increase the collection frequency during times when reviews are more frequently posted, such as at night or on weekends. It can also increase the collection frequency of relevant reviews around the time of a new product launch. This allows the review site collection unit to collect review site data more effectively based on specific times of day or events. Some or all of the above processing in the review site collection unit may be performed using AI, for example, or not. For example, the review site collection unit can have a generating AI execute an algorithm to change the collection frequency based on specific times of day or events.

[0123] The review site collection unit can estimate the user's emotions and determine the priority of review site data to collect based on the estimated user emotions. For example, if the user is excited, the review site collection unit may prioritize collecting reviews on the latest trends. For example, if the user is relaxed, the review site collection unit may also prioritize collecting reviews on long-term trends. For example, if the user is stressed, the review site collection unit may also prioritize collecting reviews that help reduce stress. This allows the review site collection unit to prioritize collecting more appropriate review site data 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 review site collection unit may be performed using AI or not using AI. For example, the review site collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of review site data to collect.

[0124] The review site collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting review site data. For example, if the user is in a specific region, the review site collection unit can prioritize the collection of reviews related to that region. For example, if the user is traveling, the review site collection unit can prioritize the collection of reviews related to the travel destination. For example, if the user is participating in a specific event, the review site collection unit can prioritize the collection of reviews related to that event. In this way, the review site collection unit can prioritize the collection of highly relevant review site data by considering the user's geographical location. Some or all of the above processing in the review site collection unit may be performed using AI, for example, or without AI. For example, the review site collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the priority collection of highly relevant review site data.

[0125] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0126] The trend analysis assistant can further estimate the user's emotions and adjust how the analysis results are displayed based on those estimated emotions. For example, if the user is excited, it can provide a visually stimulating display. If the user is relaxed, it can provide a calming display. If the user is stressed, it can provide a simple and easy-to-read display. This allows for more appropriate displays based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust how the analysis results are displayed.

[0127] The trend analysis assistant can further prioritize the collection of highly relevant data by considering the user's geographical location. For example, if the user is in a specific region, it can prioritize the collection of news and topics related to that region. If the user is traveling, it can also prioritize the collection of information related to their travel destination. If the user is attending a specific event, it can also prioritize the collection of information related to that event. In this way, by considering the user's geographical location, it is possible to prioritize the collection of highly relevant data. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0128] The trend analysis assistant can further prioritize the collection of highly relevant data by referring to the user's past search history. For example, it can prioritize the collection of data related to keywords the user has searched for in the past. It can also prioritize the collection of data related to websites the user has visited in the past. It can also prioritize the collection of data related to topics the user has shown interest in in the past. In this way, highly relevant data can be prioritized by referring to the user's past search history. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's past search history into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0129] The trend analysis assistant can further estimate the user's emotions and adjust the types of data collected based on those estimated emotions. For example, if the user is excited, it can prioritize collecting entertainment-related data. If the user is calm, it can prioritize collecting business-related data. If the user is stressed, it can prioritize collecting relaxation and health-related data. This allows for the collection of more appropriate data based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the collection unit may be performed using AI or not. For example, the collection unit can input the user's emotion data into the generative AI and have the generative AI adjust the types of data to be collected.

[0130] The trend analysis assistant can further estimate the user's emotions and adjust the way the insights are presented based on those estimated emotions. For example, if the user is excited, it can provide visually stimulating insights. If the user is relaxed, it can provide calmly presented insights. If the user is stressed, it can provide simple and highly visible insights. This allows for the provision of more relevant insights based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the delivery unit may be performed using AI or not. For example, the delivery unit can input user emotion data into a generative AI and have the generative AI adjust the way the insights are presented.

[0131] The trend analysis assistant can further vary the data collection frequency in the data collection unit based on specific time periods or events. For example, it can increase the collection frequency of relevant data before and after major events or holidays. It can also increase the collection frequency during times when social media activity is high, such as at night or on weekends. It can also increase the collection frequency of relevant data around the time of a new product launch. This enables more effective data collection based on specific time periods or events. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can have a generating AI execute an algorithm to vary the collection frequency based on specific time periods or events.

[0132] The trend analysis assistant can further adjust the level of detail of the analysis in the analysis unit based on the importance of the data. For example, it can perform detailed analysis on high-importance data and simplified analysis on low-importance data. It can also apply multiple analysis methods to high-importance data and a single analysis method to low-importance data. High-importance data can be analyzed in real time, while low-importance data can be analyzed in batch processing. This enables more effective analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. 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.

[0133] The trend analysis assistant can further apply different visualization methods in its visualization unit depending on the data category. For example, it can use word clouds for text data and bar graphs or line graphs for numerical data. It can also use network graphs for social media data and timelines for news article data. It can use rating distribution graphs for review site data and heatmaps for forum data. This allows for more appropriate visualization depending on the data category. Some or all of the above processing in the visualization unit may be performed using AI or not. For example, the visualization unit can input data categories into a generating AI and have the generating AI apply different visualization methods.

[0134] The trend analysis assistant can further adjust the level of detail provided in the delivery section based on the importance of the insights. For example, it can provide detailed explanations for high-importance insights and simplified explanations for low-importance insights. It can also provide multi-perspective analysis for high-importance insights and a single-perspective analysis for low-importance insights. It can also provide interactive elements for high-importance insights and static content for low-importance insights. This enables the delivery of more effective insights based on their importance. Some or all of the above processing in the delivery section may be performed using AI or not. For example, the delivery section can input the importance of the insights into a generating AI and have the generating AI adjust the level of detail of the delivery.

[0135] The trend analysis assistant can further estimate the user's emotions in the delivery unit and prioritize the insights to be provided based on those estimated emotions. For example, if the user is excited, insights on the latest trends may be prioritized. If the user is relaxed, insights on long-term trends may be prioritized. If the user is stressed, insights that help reduce stress may be prioritized. This allows for the prioritization of more relevant insights based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input user emotion data into a generative AI and have the generative AI determine the priority of insights.

[0136] The following briefly describes the processing flow for example form 2.

[0137] Step 1: The data collection unit gathers search terms from the internet and topics from social media. For example, it collects how often specific keywords are searched and what topics are trending on social media. Furthermore, if a particular keyword or hashtag is trending, it can prioritize the collection of related data. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it can analyze trends, consumer needs, and the activities of competitors based on the collected data. Step 3: The visualization unit visualizes the analysis results obtained by the analysis unit. For example, it intuitively displays search term trends, consumer trends, competitor activity, and consumer needs using graphs and dashboards. Step 4: The delivery unit provides insights based on the information visualized by the visualization unit. For example, it provides insights to optimize marketing strategies or determine the direction of product development.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the collection unit, analysis unit, visualization unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects search terms from the internet and topics from social media using the camera 42 and microphone 38B of the smart device 14. The analysis unit analyzes the collected data using, for example, the specific processing unit 290 of the data processing unit 12. The visualization unit displays the analysis results in graphs or dashboards using, for example, the display 40A of the smart device 14. The provision unit generates insights using, for example, the specific processing unit 290 of the data processing unit 12 and provides them to the user through the output device 40 of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0142] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the data collection unit, analysis unit, visualization unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects search terms from the internet and topics from social media using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12. The visualization unit displays the analysis results in graphs or dashboards using the display of the smart glasses 214. The provision unit generates insights using the identification processing unit 290 of the data processing unit 12 and provides them to the user through the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0158] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the collection unit, analysis unit, visualization unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects search terms on the internet and topics on social media using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The visualization unit displays the analysis results in graphs or dashboards using the display 343 of the headset terminal 314. The provision unit generates insights using the specific processing unit 290 of the data processing unit 12 and provides them to the user through the speaker 240 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0174] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.).

[0187] 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.

[0188] 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.

[0189] 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.

[0190] Each of the multiple elements described above, including the data collection unit, analysis unit, visualization unit, and provision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects search terms from the internet and topics from social media using the camera 42 and microphone 238 of the robot 414. The analysis unit analyzes the collected data using, for example, the specific processing unit 290 of the data processing unit 12. The visualization unit displays the analysis results in graphs or dashboards using, for example, the display of the robot 414. The provision unit generates insights using, for example, the specific processing unit 290 of the data processing unit 12 and provides them to the user through the speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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."

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] (Note 1) The data collection department collects internet search terms and social media topics, An analysis unit analyzes the data collected by the aforementioned collection unit, A visualization unit that visualizes the analysis results obtained by the aforementioned analysis unit, The system includes a providing unit that provides insights based on the information visualized by the visualization unit. A system characterized by the following features. (Note 2) It has a news article collection department that collects news articles. The system described in Appendix 1, characterized by the features described herein. (Note 3) It has a blog post collection unit that collects blog posts. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a forum data collection unit that collects forum data. The system described in Appendix 1, characterized by the features described herein. (Note 5) It includes a review site collection unit that collects data from review sites. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, The collected data is analyzed to understand consumer trends and the competitive landscape. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, We provide insights into marketing strategies and product development based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, the frequency of data collection can be changed based on specific time periods or events. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data by referencing the user's past search history. The system described in Appendix 1, characterized by the features described herein. (Note 11) 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 12) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the system analyzes the user's social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, 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 16) The aforementioned analysis unit, During analysis, different analytical methods are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, 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 20) The aforementioned visualization unit, It estimates the user's emotions and adjusts the visualization's presentation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned visualization unit, When visualizing, adjust the level of detail of the visualization based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned visualization unit, When visualizing data, different visualization methods are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned visualization unit, It estimates the user's emotions and adjusts the length of the visualization based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned visualization unit, When visualizing data, prioritize visualizations based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned visualization unit, When visualizing, adjust the order of visualizations based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, We estimate the user's emotions and adjust how the insights we provide are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the insights, we will adjust the level of detail based on their importance. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing insights, different delivery methods will be applied depending on the category of the insights. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, We estimate user emotions and prioritize the insights we provide based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing insights, we will prioritize their delivery based on when the insights were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When delivering, we adjust the delivery order based on the relevance of the insights. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned news article collection department, It estimates the user's sentiment and adjusts the types of news articles collected based on that estimated sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned news article collection department, When collecting news articles, change the collection frequency based on specific time periods or events. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned news article collection department, It estimates user sentiment and determines the priority of news articles to collect based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned news article collection department, When collecting news articles, the system prioritizes collecting articles that are highly relevant, taking into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned blog post collection unit, We estimate user sentiment and adjust the types of blog posts we collect based on that estimated sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned blog post collection unit, When collecting blog posts, change the collection frequency based on specific time periods or events. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned blog post collection unit, It estimates user sentiment and determines the priority of blog posts to collect based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned blog post collection unit, When collecting blog posts, the system prioritizes collecting posts that are highly relevant, taking into account the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned forum collection unit, We estimate user sentiment and adjust the types of forum data collected based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned forum collection unit, When collecting forum data, change the collection frequency based on specific time periods or events. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned forum collection unit, It estimates user sentiment and prioritizes the forum data to collect based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 43) The aforementioned forum collection unit, When collecting forum data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 44) The aforementioned review site collection unit, We estimate user sentiment and adjust the types of review site data collected based on the estimated user sentiment. The system described in Appendix 5, characterized by the features described herein. (Note 45) The aforementioned review site collection unit, When collecting review site data, change the collection frequency based on specific time periods or events. The system described in Appendix 5, characterized by the features described herein. (Note 46) The aforementioned review site collection unit, It estimates user sentiment and prioritizes the review site data to collect based on the estimated user sentiment. The system described in Appendix 5, characterized by the features described herein. (Note 47) The aforementioned review site collection unit, When collecting review site data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 5, characterized by the features described herein. [Explanation of Symbols]

[0210] 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 internet search terms and social media topics, An analysis unit analyzes the data collected by the aforementioned collection unit, A visualization unit that visualizes the analysis results obtained by the aforementioned analysis unit, The system includes a providing unit that provides insights based on the information visualized by the visualization unit. A system characterized by the following features.

2. It has a news article collection department that collects news articles. The system according to feature 1.

3. It has a blog post collection unit that collects blog posts. The system according to feature 1.

4. It includes a forum data collection unit that collects forum data. The system according to feature 1.

5. It includes a review site collection unit that collects data from review sites. The system according to feature 1.

6. The aforementioned analysis unit, The collected data is analyzed to understand consumer trends and the competitive landscape. The system according to feature 1.

7. The aforementioned supply unit is, We provide insights into marketing strategies and product development based on the analysis results. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is During data collection, the frequency of data collection can be changed based on specific time periods or events. The system according to feature 1.

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