system

Abstract

The system according to this embodiment aims to efficiently process large amounts of market data and quickly identify trends and opportunities. [Solution] The system according to the embodiment comprises a collection unit, an identification unit, and a presentation unit. The collection unit collects market data. The identification unit identifies market trends and opportunities based on the data collected by the collection unit. The presentation unit visually presents the market trends and opportunities identified by the identification unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to efficiently process a large amount of market data and quickly identify trends and opportunities.

[0005] The system according to an embodiment aims to efficiently process a large amount of market data and quickly identify trends and opportunities.

Means for Solving the Problems

[0006] The system according to an embodiment includes a collection unit, a specification unit, and a presentation unit. The collection unit collects data related to the market. The specification unit identifies market trends and opportunities based on the data collected by the collection unit. The presentation unit visually presents the market trends and opportunities identified by the specification unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently process large amounts of market data and quickly identify trends and opportunities. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI assistant for market research according to an embodiment of the present invention is a system that processes large amounts of data at high speed and identifies market trends and opportunities. This system first collects market data, then identifies market trends and opportunities based on the collected data, and further presents the identified market trends and opportunities visually. This mechanism allows for the collection and analysis of competitor activities and customer feedback, and the presentation of research results on a visual dashboard. For example, when collecting market data, data is collected from publicly available information on the internet, social media, news articles, blogs, etc. For example, by collecting the content of social media posts and news articles and having the AI ​​analyze them, market trends can be grasped. Next, market trends and opportunities are identified based on the collected data. The identification unit identifies market trends and opportunities using machine learning algorithms. For example, it can predict future market trends based on past data and find new business opportunities. Furthermore, the identified market trends and opportunities are presented visually. The presentation unit visually presents market trends and opportunities using a user-customizable dashboard. For example, graphs and charts can be used to grasp market trends at a glance. The identification unit also has a function to compare market trends and opportunities in different timeframes. This allows for the comparison of past and present data to predict future market trends. Furthermore, the presentation unit includes a function to filter market trends and opportunities based on user-defined parameters. This allows users to extract only the information they are interested in and conduct market research efficiently. This mechanism enables high-speed processing of large amounts of data and the identification of market trends and opportunities. In addition, by collecting and analyzing competitor activity and customer feedback and presenting the research results on a visual dashboard, users can conduct market research efficiently. For example, when deciding on the market launch timing of a new product, the optimal timing can be found based on competitor activity and customer feedback. In this way, the AI ​​assistant supporting market research can process large amounts of data at high speed and identify market trends and opportunities.

[0029] The AI ​​assistant for market research according to this embodiment comprises a collection unit, an identification unit, and a presentation unit. The collection unit collects market-related data. The collection unit can collect data from, for example, publicly available information on the internet, social media, news articles, blogs, etc. The collection unit can collect data using, for example, web scraping technology. The collection unit can also obtain data using APIs. For example, the collection unit can collect posts on social media and store them as text data. The collection unit can also collect news articles and analyze the content of the articles. The collection unit can also collect blog posts and analyze the content of the posts. The identification unit identifies market trends and opportunities based on the data collected by the collection unit. The identification unit identifies market trends and opportunities using, for example, machine learning algorithms. The identification unit can predict future market trends based on historical data. The identification unit can also find new business opportunities. The identification unit can identify market trends using, for example, clustering algorithms. The identification unit can also identify market opportunities using classification algorithms. The identification unit can also predict market trends using regression analysis. The presentation unit visually presents market trends and opportunities identified by the identification unit. The presentation unit visually presents market trends and opportunities using, for example, a user-customizable dashboard. The presentation unit can, for example, use graphs and charts to allow users to grasp market trends at a glance. The presentation unit has the ability to compare market trends and opportunities across different timeframes. The presentation unit can, for example, compare historical and current data to predict future market trends. The presentation unit has the ability to filter market trends and opportunities based on user-defined parameters. The presentation unit can, for example, extract only the information of interest to the user, enabling efficient market research. As a result, the AI ​​assistant for market research according to the embodiment can efficiently collect, identify, and visually present market data.

[0030] The data collection unit collects market-related data. This unit can collect data from publicly available information on the internet, social media, news articles, blogs, and so on. Specifically, it uses web scraping techniques to automatically extract data from specific websites. Web scraping is a technique that analyzes HTML structure and extracts necessary information, and is often implemented using libraries such as Python's BeautifulSoup and Scrapy. The data collection unit can also obtain data using APIs. For example, it can use APIs from X (formerly Twitter®) and Facebook® to collect posts related to specific keywords and save them as text data. This allows for a real-time understanding of user opinions and sentiments on social media. Furthermore, the data collection unit can collect news articles and analyze their content. RSS feeds and news APIs are commonly used to collect news articles. The collected news articles are analyzed using natural language processing techniques to analyze the article's topic and sentiment. Blog posts are similarly collected, and by analyzing their content, it's possible to understand user opinions and trends on specific themes. This allows the data collection unit to gather a wide range of data from diverse sources and gain a comprehensive understanding of market trends. Furthermore, the data collection unit can flexibly set the frequency and target of data collection. For example, by intensifying data collection during specific events or campaign periods, more detailed market analysis becomes possible. In addition, the data collection unit can centrally manage the collected data and integrate it with other systems and departments. This enables the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The identification unit identifies market trends and opportunities based on data collected by the collection unit. For example, the identification unit uses machine learning algorithms to identify market trends and opportunities. Specifically, it analyzes text data using natural language processing techniques and performs topic modeling and sentiment analysis. Topic modeling is a technique that extracts key topics from text data using algorithms such as LDA (Latent Dirichlet Allocation). This allows for an understanding of key concerns and trends in the market. Sentiment analysis is a technique that classifies positive, negative, and neutral sentiments from text data, allowing for an understanding of user opinions and emotional tendencies. The identification unit can also predict future market trends based on historical data. For example, it uses time series analysis to predict future trends from past data. It uses algorithms such as ARIMA models and LSTM (Long Short-Term Memory) networks to learn data patterns and predict future trends. Furthermore, the identification unit can also discover new business opportunities. It uses clustering algorithms to group similar data points and identify market segments. For example, K-means clustering can be used to identify customer segments based on customer purchasing behavior and preferences. Classification algorithms can also be used to identify market opportunities. For example, decision trees and random forests can be used to identify success factors under specific conditions and uncover new business opportunities. Regression analysis can also be used to predict market trends. For example, linear regression and polynomial regression can be used to quantitatively evaluate the impact of specific variables on market trends. This allows certain departments to quickly and accurately identify market trends and opportunities based on collected data, which can then be used to formulate business strategies.

[0032] The presentation section visually presents market trends and opportunities identified by the specific section. For example, the presentation section visually presents market trends and opportunities using a user-customizable dashboard. Specifically, it displays collected data and analysis results as graphs and charts through the user interface. For example, line graphs, bar graphs, pie charts, and heatmaps are used to allow users to intuitively grasp data trends and distributions. The presentation section includes the ability to compare market trends and opportunities across different timeframes. For example, comparing data from the past year with data from the past month allows users to understand short-term and long-term trends. This enables users to analyze market trends from multiple perspectives and make appropriate decisions. The presentation section includes the ability to filter market trends and opportunities based on user-defined parameters. For example, displaying only data related to specific regions or industries allows users to efficiently extract only the information they are interested in. Furthermore, the presentation section includes interactive features, allowing users to manipulate data and perform detailed analysis. For example, clicking on a specific data point on a graph displays detailed information about that data, or different filter conditions can be applied to change the displayed data. This allows the presentation section to visually grasp market trends and opportunities, supporting quick and accurate decision-making. Furthermore, the presentation section includes a report generation function, allowing users to output analysis results as reports. For example, reports can be generated in PDF or Excel format and shared with stakeholders. This enables the presentation section to effectively utilize market research results to aid in the planning and execution of business strategies.

[0033] The identification unit includes an analysis processing unit that analyzes the trends of competitors based on the data collected by the collection unit. The analysis processing unit can, for example, analyze the sales data of competitors. The analysis processing unit can also, for example, analyze the marketing strategies of competitors. The analysis processing unit can also, for example, analyze the pricing information of competitors. This allows the identification unit to identify more detailed market trends and opportunities by analyzing the trends of competitors. Some or all of the above processing in the analysis processing unit may be performed using AI, for example, or without AI. For example, the analysis processing unit can input competitor sales data into AI, and the AI ​​can analyze the sales data to identify the trends of competitors.

[0034] The presentation unit can visually present the results of its analysis of competitors' activities. For example, the presentation unit can visually present competitors' activities using graphs and charts. The presentation unit can also visually present competitors' activities using a dashboard. For example, the presentation unit can make it possible to grasp competitors' activities at a glance. In this way, by visually presenting the results of its analysis of competitors' activities, the presentation unit allows users to grasp market trends at a glance. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can visually present competitors' activities using graphs and charts generated by AI.

[0035] The analytical processing unit can analyze customer feedback in addition to competitor trends. For example, the analytical processing unit can analyze customer reviews. For example, the analytical processing unit can analyze survey results. For example, the analytical processing unit can analyze comments on social media. This allows the analytical processing unit to identify more detailed market trends and opportunities by analyzing customer feedback. Some or all of the processing described above in the analytical processing unit may be performed using AI, for example, or not using AI. For example, the analytical processing unit can input customer reviews into an AI, which can then analyze the reviews to identify customer feedback.

[0036] The presentation unit can visually present the results of the analysis of customer feedback. The presentation unit can visually present customer feedback using graphs and charts, for example. The presentation unit can also visually present customer feedback using a dashboard, for example. The presentation unit can make customer feedback easily understandable at a glance. In this way, by visually presenting the results of the analysis of customer feedback, the presentation unit allows users to grasp market trends at a glance. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit can visually present customer feedback using graphs and charts generated by AI.

[0037] The analysis processing unit includes an identification processing unit that identifies market trends and opportunities based on the analysis results of competitor trends and customer feedback. The identification processing unit can, for example, identify market trends by integrating competitor trends and customer feedback. The identification processing unit can also, for example, identify market opportunities by integrating competitor trends and customer feedback. The identification processing unit can also, for example, predict market trends based on competitor trends and customer feedback. This allows the analysis processing unit to identify market trends and opportunities more accurately. Some or all of the above processing in the identification processing unit may be performed using AI, for example, or without AI. For example, the identification processing unit can input competitor trends and customer feedback into AI, which can then identify market trends and opportunities.

[0038] The data collection unit can collect data from at least one of the following sources: publicly available information on the internet, social media, news articles, and blogs. For example, the data collection unit can collect news articles and analyze their content. For example, the data collection unit can collect posts on social media and save them as text data. For example, the data collection unit can collect blog posts and analyze their content. This allows the data collection unit to conduct more comprehensive market research by collecting data from diverse data sources. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input news articles into an AI, which can then analyze the content of the articles to identify market trends.

[0039] The identification unit can use machine learning algorithms to identify market trends and opportunities. For example, the identification unit can use clustering algorithms to identify market trends. The identification unit can also use classification algorithms to identify market opportunities. The identification unit can also use regression analysis to predict market trends. This allows the identification unit to identify market trends and opportunities more accurately by using machine learning algorithms. Some or all of the above processes in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input collected data into AI, which can then use machine learning algorithms to identify market trends and opportunities.

[0040] The presentation unit can visually present market trends and opportunities using a user-customizable dashboard. For example, the presentation unit can visually present market trends using graphs and charts. The presentation unit can also visually present market trends and opportunities using a dashboard. For example, the presentation unit can add user-customizable widgets. The presentation unit can also visually present market trends and opportunities using filtering functions. This makes it easier for users to visually grasp market trends and opportunities using a customizable dashboard. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not. For example, the presentation unit can visually present market trends and opportunities using graphs and charts generated by AI.

[0041] The specific unit may have the function of comparing market trends and opportunities across different timeframes. For example, the specific unit may compare historical and current data to predict future market trends. For example, the specific unit may compare market trends and opportunities across timeframes such as the past month, the past year, or the past five years. The specific unit may also predict market trends based on data across different timeframes. This makes it easier for the specific unit to predict future market trends by comparing market trends and opportunities across different timeframes. Some or all of the processing described above in the specific unit may be performed using AI, for example, or not using AI. For example, the specific unit may input historical and current data into AI, which can then compare and predict market trends and opportunities across different timeframes.

[0042] The presentation unit may have a function to filter market trends and opportunities based on user-defined parameters. For example, the presentation unit may filter market trends and opportunities based on specific keywords. The presentation unit may also filter market trends and opportunities based on a date range. The presentation unit may also filter market trends and opportunities based on a data source. This allows the presentation unit to extract only the information of interest to the user and conduct market research efficiently. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit may input user-defined parameters into an AI, which can then filter market trends and opportunities.

[0043] The data collection unit can evaluate the reliability of the data to be collected and prioritize the collection of reliable data. For example, the data collection unit can evaluate the reliability of data sources and prioritize the collection of data from reliable sources. For example, the data collection unit can also check the consistency of data and prioritize the collection of consistent data. For example, the data collection unit can evaluate the timeliness of data and prioritize the collection of the most recent data. This allows the data collection unit to conduct more accurate market research by prioritizing the collection of reliable data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the reliability of data sources into AI, and the AI ​​can prioritize the collection of reliable data.

[0044] The data collection unit can customize the types of data it collects based on the user's industry and interests. For example, if the user belongs to the financial industry, the data collection unit will prioritize collecting financial-related data. For example, if the user is interested in the technology industry, the data collection unit can prioritize collecting technology-related data. For example, if the user is interested in the health industry, the data collection unit can prioritize collecting health-related data. This allows the data collection unit to collect more relevant data by customizing the data based on the user's industry and interests. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's industry and interests into the AI ​​and customize the types of data the AI ​​collects.

[0045] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. For example, if the user is traveling, the data collection unit can prioritize the collection of data related to the travel destination. For example, if the user is in a specific city, the data collection unit can prioritize the collection of market data related to that city. In this way, the data collection unit can collect more 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 AI, which can then prioritize the collection of highly relevant data.

[0046] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user uses a specific hashtag, the data collection unit can collect data related to that hashtag. For example, if a user posts about a specific topic, the data collection unit can also collect data related to that topic. For example, if a user belongs to a specific group, the data collection unit can also collect data related to that group. This allows the data collection unit to collect more 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 into AI, and the AI ​​can collect relevant data.

[0047] The specific unit can predict future market trends by comparing past and present data at a specific time. For example, the specific unit can predict future sales trends by comparing past sales data with current sales data. The specific unit can also predict future customer needs by comparing past customer feedback with current feedback. The specific unit can also predict future market share by comparing past market share data with current market share data. This makes it easier for the specific unit to predict future market trends by comparing past and present data. Some or all of the above processing in the specific unit may be performed using AI, for example, or without AI. For example, the specific unit can input past and present data into AI, and the AI ​​can predict future market trends.

[0048] The identification unit can identify trends and opportunities by integrating information from different data sources at the time of identification. For example, the identification unit can identify trends by integrating social media data and news article data. The identification unit can also identify opportunities by integrating blog data and market research data. The identification unit can also identify trends and opportunities by integrating customer reviews and product evaluation data. This allows the identification unit to identify more accurate trends and opportunities by integrating information from different data sources. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input information from different data sources into AI, which can then identify trends and opportunities.

[0049] The identification unit can identify trends and opportunities by considering industry-specific factors of the user at the time of identification. For example, if the user belongs to the financial industry, the identification unit can identify trends and opportunities in the financial market. For example, if the user belongs to the technology industry, the identification unit can also identify trends and opportunities in the technology market. For example, if the user belongs to the health industry, the identification unit can also identify trends and opportunities in the health market. This allows the identification unit to identify more relevant trends and opportunities by considering industry-specific factors of the user. Some or all of the above processing in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input industry-specific factors of the user into the AI, and the AI ​​can identify trends and opportunities.

[0050] The identification unit can identify trends and opportunities by referring to the user's past behavioral history at the time of identification. For example, the identification unit can identify current trends based on trends the user has been interested in in the past. The identification unit can also identify future opportunities based on events the user has participated in in the past. The identification unit can also identify current market opportunities based on products the user has purchased in the past. In this way, the identification unit can identify more relevant trends and opportunities by referring to the user's past behavioral history. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the user's past behavioral history into AI, which can then identify trends and opportunities.

[0051] The presentation unit can select the optimal display method by referring to the user's past browsing history when presenting information. For example, the presentation unit can select the optimal display method based on the graph format the user has previously preferred to use. The presentation unit can also display relevant information based on the types of data the user has previously viewed. The presentation unit can also select the optimal display method based on the customization settings the user has previously used. In this way, the presentation unit can select a more appropriate display method by referring to the user's past browsing history. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's past browsing history into AI, which can then select the optimal display method.

[0052] The presentation unit can provide display methods that are compatible with different devices at the time of presentation. For example, the presentation unit can provide a display method optimized for smartphones. The presentation unit can also provide a display method optimized for tablets. The presentation unit can also provide a display method optimized for desktops. By providing display methods that are compatible with different devices, the presentation unit makes it easier for users to view information on any device. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input display methods compatible with different devices into AI, and the AI ​​can provide the optimal display method.

[0053] The presentation unit can prioritize displaying highly relevant information by considering the user's geographical location. For example, if the user is in a specific region, the presentation unit can prioritize displaying market information related to that region. For example, if the user is traveling, the presentation unit can prioritize displaying market information related to the travel destination. For example, if the user is in a specific city, the presentation unit can prioritize displaying market information related to that city. In this way, the presentation unit can provide more relevant information by considering the user's geographical location. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's geographical location information into AI, which can then prioritize displaying highly relevant information.

[0054] The presentation unit can analyze the user's social media activity and display relevant information at the time of presentation. For example, if the user is using a specific hashtag, the presentation unit can display information related to that hashtag. For example, if the user is posting about a specific topic, the presentation unit can also display information related to that topic. For example, if the user is a member of a specific group, the presentation unit can also display information related to that group. In this way, the presentation unit can provide more relevant information by analyzing the user's social media activity. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's social media activity into AI, and the AI ​​can display relevant information.

[0055] The analytical processing unit can integrate information from different data sources during analysis to analyze competitor trends and customer feedback. For example, the analytical processing unit can integrate social media data and news article data to analyze competitor trends. The analytical processing unit can also integrate blog data and market research data to analyze customer feedback. The analytical processing unit can also integrate customer reviews and product evaluation data to analyze competitor trends and customer feedback. This allows the analytical processing unit to analyze competitor trends and customer feedback more accurately by integrating information from different data sources. Some or all of the above processing in the analytical processing unit may be performed using AI, for example, or without AI. For example, the analytical processing unit can input information from different data sources into AI, which can then analyze competitor trends and customer feedback.

[0056] The analytical processing unit can predict competitor trends and customer feedback by comparing historical and current data during analysis. For example, the analytical processing unit can compare historical and current sales data to predict future competitor trends. The analytical processing unit can also predict future customer needs by comparing historical and current customer feedback. The analytical processing unit can also predict future market share of competitors by comparing historical and current market share data. This makes it easier for the analytical processing unit to predict future competitor trends and customer feedback by comparing historical and current data. Some or all of the above processing in the analytical processing unit may be performed using AI, for example, or without AI. For example, the analytical processing unit can input historical and current data into AI, which can then predict future competitor trends and customer feedback.

[0057] The analysis processing unit can analyze competitor trends and customer feedback while considering factors specific to the user's industry. For example, if the user belongs to the financial industry, the analysis processing unit can analyze competitor trends and customer feedback in the financial market. If the user belongs to the technology industry, the analysis processing unit can also analyze competitor trends and customer feedback in the technology market. If the user belongs to the health industry, the analysis processing unit can also analyze competitor trends and customer feedback in the health market. This allows the analysis processing unit to analyze competitor trends and customer feedback that are more relevant by considering factors specific to the user's industry. Some or all of the processing described above in the analysis processing unit may be performed using AI, for example, or without AI. For example, the analysis processing unit can input industry-specific factors of the user into AI, and the AI ​​can analyze competitor trends and customer feedback.

[0058] The analysis processing unit can analyze competitor trends and customer feedback by referring to the user's past behavior history during analysis. For example, the analysis processing unit can analyze current trends based on the trends of competitors that the user has been interested in in the past. The analysis processing unit can also analyze current customer feedback based on customer feedback that the user has collected in the past. The analysis processing unit can also analyze competitor trends and customer feedback based on events that the user has participated in in the past. In this way, the analysis processing unit can analyze competitor trends and customer feedback that are more relevant by referring to the user's past behavior history. Some or all of the above processing in the analysis processing unit may be performed using AI, for example, or without using AI. For example, the analysis processing unit can input the user's past behavior history into AI, and the AI ​​can analyze competitor trends and customer feedback.

[0059] The identification processing unit can identify market trends and opportunities by integrating information from different data sources during the identification process. For example, the identification processing unit can identify market trends by integrating social media data and news article data. The identification processing unit can also identify market opportunities by integrating blog data and market research data. The identification processing unit can also identify market trends and opportunities by integrating customer reviews and product evaluation data. This allows the identification processing unit to identify market trends and opportunities more accurately by integrating information from different data sources. Some or all of the above processing in the identification processing unit may be performed using AI, for example, or without AI. For example, the identification processing unit can input information from different data sources into AI, which can then identify market trends and opportunities.

[0060] A specific processing unit can predict market trends and opportunities by comparing historical and current data during a specific processing step. For example, the specific processing unit can compare historical and current sales data to predict future market trends. The specific processing unit can also predict future customer needs by comparing historical and current customer feedback. The specific processing unit can also predict future market opportunities by comparing historical and current market share data. This makes it easier for the specific processing unit to predict future market trends and opportunities by comparing historical and current data. Some or all of the processing described above in the specific processing unit may be performed using AI, for example, or without AI. For example, the specific processing unit can input historical and current data into AI, which can then predict future market trends and opportunities.

[0061] The specific processing unit can identify market trends and opportunities by considering factors specific to the user's industry during the specific processing. For example, if the user belongs to the financial industry, the specific processing unit can identify trends and opportunities in the financial market. For example, if the user belongs to the technology industry, the specific processing unit can also identify trends and opportunities in the technology market. For example, if the user belongs to the health industry, the specific processing unit can also identify trends and opportunities in the health market. This allows the specific processing unit to identify more relevant market trends and opportunities by considering factors specific to the user's industry. Some or all of the processing described above in the specific processing unit may be performed using AI, for example, or without AI. For example, the specific processing unit can input industry-specific factors of the user into the AI, and the AI ​​can identify market trends and opportunities.

[0062] The identification processing unit can identify market trends and opportunities by referring to the user's past behavioral history during the identification process. For example, the identification processing unit can identify current trends based on market trends the user has been interested in in the past. The identification processing unit can also identify future market opportunities based on events the user has participated in in the past. The identification processing unit can also identify current market opportunities based on products the user has purchased in the past. In this way, the identification processing unit can identify more relevant market trends and opportunities by referring to the user's past behavioral history. Some or all of the above processing in the identification processing unit may be performed using AI, for example, or without AI. For example, the identification processing unit can input the user's past behavioral history into AI, which can then identify market trends and opportunities.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The data collection unit can evaluate the reliability of the data to be collected and prioritize the collection of reliable data. For example, the data collection unit can evaluate the reliability of data sources and prioritize the collection of data from reliable sources. For example, the data collection unit can also check the consistency of data and prioritize the collection of consistent data. For example, the data collection unit can evaluate the timeliness of data and prioritize the collection of the most recent data. This allows the data collection unit to conduct more accurate market research by prioritizing the collection of reliable data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the reliability of data sources into AI, and the AI ​​can prioritize the collection of reliable data.

[0065] The data collection unit can customize the types of data it collects based on the user's industry and interests. For example, if the user belongs to the financial industry, the data collection unit will prioritize collecting financial-related data. For example, if the user is interested in the technology industry, the data collection unit can prioritize collecting technology-related data. For example, if the user is interested in the health industry, the data collection unit can prioritize collecting health-related data. This allows the data collection unit to collect more relevant data by customizing the data based on the user's industry and interests. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's industry and interests into the AI ​​and customize the types of data the AI ​​collects.

[0066] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. For example, if the user is traveling, the data collection unit can prioritize the collection of data related to the travel destination. For example, if the user is in a specific city, the data collection unit can prioritize the collection of market data related to that city. In this way, the data collection unit can collect more 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 AI, which can then prioritize the collection of highly relevant data.

[0067] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user uses a specific hashtag, the data collection unit can collect data related to that hashtag. For example, if a user posts about a specific topic, the data collection unit can also collect data related to that topic. For example, if a user belongs to a specific group, the data collection unit can also collect data related to that group. This allows the data collection unit to collect more 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 into AI, and the AI ​​can collect relevant data.

[0068] The specific unit can predict future market trends by comparing past and present data at a specific time. For example, the specific unit can predict future sales trends by comparing past sales data with current sales data. The specific unit can also predict future customer needs by comparing past customer feedback with current feedback. The specific unit can also predict future market share by comparing past market share data with current market share data. This makes it easier for the specific unit to predict future market trends by comparing past and present data. Some or all of the above processing in the specific unit may be performed using AI, for example, or without AI. For example, the specific unit can input past and present data into AI, and the AI ​​can predict future market trends.

[0069] The identification unit can identify trends and opportunities by integrating information from different data sources at the time of identification. For example, the identification unit can identify trends by integrating social media data and news article data. The identification unit can also identify opportunities by integrating blog data and market research data. The identification unit can also identify trends and opportunities by integrating customer reviews and product evaluation data. This allows the identification unit to identify more accurate trends and opportunities by integrating information from different data sources. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input information from different data sources into AI, which can then identify trends and opportunities.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The data collection unit collects market-related data. The data collection unit can collect data from publicly available information on the internet, social media, news articles, blogs, etc. The data collection unit uses web scraping techniques and APIs to collect data. For example, it can collect posts from social media and save them as text data. It can also collect news articles and blog posts and analyze their content. Step 2: The Identification Unit identifies market trends and opportunities based on the data collected by the Collection Unit. The Identification Unit uses machine learning algorithms to identify market trends and opportunities. For example, it predicts future market trends based on historical data and finds new business opportunities. Clustering algorithms, classification algorithms, and regression analysis can be used to identify market trends and opportunities. Step 3: The presentation section visually presents the market trends and opportunities identified by the specific section. The presentation section visually presents market trends and opportunities using a user-customizable dashboard. It includes graphs and charts to allow users to grasp market trends at a glance and to compare market trends and opportunities across different timeframes. It allows users to compare historical and current data to predict future market trends. It also includes a function to filter market trends and opportunities based on user-defined parameters.

[0072] (Example of form 2) An AI assistant for market research according to an embodiment of the present invention is a system that processes large amounts of data at high speed and identifies market trends and opportunities. This system first collects market data, then identifies market trends and opportunities based on the collected data, and further presents the identified market trends and opportunities visually. This mechanism allows for the collection and analysis of competitor activities and customer feedback, and the presentation of research results on a visual dashboard. For example, when collecting market data, data is collected from publicly available information on the internet, social media, news articles, blogs, etc. For example, by collecting the content of social media posts and news articles and having the AI ​​analyze them, market trends can be grasped. Next, market trends and opportunities are identified based on the collected data. The identification unit identifies market trends and opportunities using machine learning algorithms. For example, it can predict future market trends based on past data and find new business opportunities. Furthermore, the identified market trends and opportunities are presented visually. The presentation unit visually presents market trends and opportunities using a user-customizable dashboard. For example, graphs and charts can be used to grasp market trends at a glance. The identification unit also has a function to compare market trends and opportunities in different timeframes. This allows for the comparison of past and present data to predict future market trends. Furthermore, the presentation unit includes a function to filter market trends and opportunities based on user-defined parameters. This allows users to extract only the information they are interested in and conduct market research efficiently. This mechanism enables high-speed processing of large amounts of data and the identification of market trends and opportunities. In addition, by collecting and analyzing competitor activity and customer feedback and presenting the research results on a visual dashboard, users can conduct market research efficiently. For example, when deciding on the market launch timing of a new product, the optimal timing can be found based on competitor activity and customer feedback. In this way, the AI ​​assistant supporting market research can process large amounts of data at high speed and identify market trends and opportunities.

[0073] The AI ​​assistant for market research according to this embodiment comprises a collection unit, an identification unit, and a presentation unit. The collection unit collects market-related data. The collection unit can collect data from, for example, publicly available information on the internet, social media, news articles, blogs, etc. The collection unit can collect data using, for example, web scraping technology. The collection unit can also obtain data using APIs. For example, the collection unit can collect posts on social media and store them as text data. The collection unit can also collect news articles and analyze the content of the articles. The collection unit can also collect blog posts and analyze the content of the posts. The identification unit identifies market trends and opportunities based on the data collected by the collection unit. The identification unit identifies market trends and opportunities using, for example, machine learning algorithms. The identification unit can predict future market trends based on historical data. The identification unit can also find new business opportunities. The identification unit can identify market trends using, for example, clustering algorithms. The identification unit can also identify market opportunities using classification algorithms. The identification unit can also predict market trends using regression analysis. The presentation unit visually presents market trends and opportunities identified by the identification unit. The presentation unit visually presents market trends and opportunities using, for example, a user-customizable dashboard. The presentation unit can, for example, use graphs and charts to allow users to grasp market trends at a glance. The presentation unit has the ability to compare market trends and opportunities across different timeframes. The presentation unit can, for example, compare historical and current data to predict future market trends. The presentation unit has the ability to filter market trends and opportunities based on user-defined parameters. The presentation unit can, for example, extract only the information of interest to the user, enabling efficient market research. As a result, the AI ​​assistant for market research according to the embodiment can efficiently collect, identify, and visually present market data.

[0074] The data collection unit collects market-related data. This unit can collect data from publicly available information on the internet, social media, news articles, blogs, and so on. Specifically, it uses web scraping techniques to automatically extract data from specific websites. Web scraping is a technique that analyzes HTML structure and extracts necessary information, and is often implemented using libraries such as Python's BeautifulSoup or Scrapy. The data collection unit can also obtain data using APIs. For example, it can use APIs from X (formerly Twitter) or Facebook to collect posts related to specific keywords and save them as text data. This allows for a real-time understanding of user opinions and sentiments on social media. Furthermore, the data collection unit can collect news articles and analyze their content. RSS feeds and news APIs are commonly used for collecting news articles. The collected news articles are analyzed using natural language processing techniques to analyze their topics and sentiment. Blog posts are similarly collected, and by analyzing their content, it's possible to understand user opinions and trends on specific themes. This allows the data collection unit to gather a wide range of data from diverse sources and gain a comprehensive understanding of market trends. Furthermore, the data collection unit can flexibly set the frequency and target of data collection. For example, by intensifying data collection during specific events or campaign periods, more detailed market analysis becomes possible. In addition, the data collection unit can centrally manage the collected data and integrate it with other systems and departments. This enables the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0075] The identification unit identifies market trends and opportunities based on data collected by the collection unit. For example, the identification unit uses machine learning algorithms to identify market trends and opportunities. Specifically, it analyzes text data using natural language processing techniques and performs topic modeling and sentiment analysis. Topic modeling is a technique that extracts key topics from text data using algorithms such as LDA (Latent Dirichlet Allocation). This allows for an understanding of key concerns and trends in the market. Sentiment analysis is a technique that classifies positive, negative, and neutral sentiments from text data, allowing for an understanding of user opinions and emotional tendencies. The identification unit can also predict future market trends based on historical data. For example, it uses time series analysis to predict future trends from past data. It uses algorithms such as ARIMA models and LSTM (Long Short-Term Memory) networks to learn data patterns and predict future trends. Furthermore, the identification unit can also discover new business opportunities. It uses clustering algorithms to group similar data points and identify market segments. For example, K-means clustering can be used to identify customer segments based on customer purchasing behavior and preferences. Classification algorithms can also be used to identify market opportunities. For example, decision trees and random forests can be used to identify success factors under specific conditions and uncover new business opportunities. Regression analysis can also be used to predict market trends. For example, linear regression and polynomial regression can be used to quantitatively evaluate the impact of specific variables on market trends. This allows certain departments to quickly and accurately identify market trends and opportunities based on collected data, which can then be used to formulate business strategies.

[0076] The presentation section visually presents market trends and opportunities identified by the specific section. For example, the presentation section visually presents market trends and opportunities using a user-customizable dashboard. Specifically, it displays collected data and analysis results as graphs and charts through the user interface. For example, line graphs, bar graphs, pie charts, and heatmaps are used to allow users to intuitively grasp data trends and distributions. The presentation section includes the ability to compare market trends and opportunities across different timeframes. For example, comparing data from the past year with data from the past month allows users to understand short-term and long-term trends. This enables users to analyze market trends from multiple perspectives and make appropriate decisions. The presentation section includes the ability to filter market trends and opportunities based on user-defined parameters. For example, displaying only data related to specific regions or industries allows users to efficiently extract only the information they are interested in. Furthermore, the presentation section includes interactive features, allowing users to manipulate data and perform detailed analysis. For example, clicking on a specific data point on a graph displays detailed information about that data, or different filter conditions can be applied to change the displayed data. This allows the presentation section to visually grasp market trends and opportunities, supporting quick and accurate decision-making. Furthermore, the presentation section includes a report generation function, allowing users to output analysis results as reports. For example, reports can be generated in PDF or Excel format and shared with stakeholders. This enables the presentation section to effectively utilize market research results to aid in the planning and execution of business strategies.

[0077] The identification unit includes an analysis processing unit that analyzes the trends of competitors based on the data collected by the collection unit. The analysis processing unit can, for example, analyze the sales data of competitors. The analysis processing unit can also, for example, analyze the marketing strategies of competitors. The analysis processing unit can also, for example, analyze the pricing information of competitors. This allows the identification unit to identify more detailed market trends and opportunities by analyzing the trends of competitors. Some or all of the above processing in the analysis processing unit may be performed using AI, for example, or without AI. For example, the analysis processing unit can input competitor sales data into AI, and the AI ​​can analyze the sales data to identify the trends of competitors.

[0078] The presentation unit can visually present the results of its analysis of competitors' activities. For example, the presentation unit can visually present competitors' activities using graphs and charts. The presentation unit can also visually present competitors' activities using a dashboard. For example, the presentation unit can make it possible to grasp competitors' activities at a glance. In this way, by visually presenting the results of its analysis of competitors' activities, the presentation unit allows users to grasp market trends at a glance. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can visually present competitors' activities using graphs and charts generated by AI.

[0079] The analytical processing unit can analyze customer feedback in addition to competitor trends. For example, the analytical processing unit can analyze customer reviews. For example, the analytical processing unit can analyze survey results. For example, the analytical processing unit can analyze comments on social media. This allows the analytical processing unit to identify more detailed market trends and opportunities by analyzing customer feedback. Some or all of the processing described above in the analytical processing unit may be performed using AI, for example, or not using AI. For example, the analytical processing unit can input customer reviews into an AI, which can then analyze the reviews to identify customer feedback.

[0080] The presentation unit can visually present the results of the analysis of customer feedback. The presentation unit can visually present customer feedback using graphs and charts, for example. The presentation unit can also visually present customer feedback using a dashboard, for example. The presentation unit can make customer feedback easily understandable at a glance. In this way, by visually presenting the results of the analysis of customer feedback, the presentation unit allows users to grasp market trends at a glance. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit can visually present customer feedback using graphs and charts generated by AI.

[0081] The analysis processing unit includes an identification processing unit that identifies market trends and opportunities based on the analysis results of competitor trends and customer feedback. The identification processing unit can, for example, identify market trends by integrating competitor trends and customer feedback. The identification processing unit can also, for example, identify market opportunities by integrating competitor trends and customer feedback. The identification processing unit can also, for example, predict market trends based on competitor trends and customer feedback. This allows the analysis processing unit to identify market trends and opportunities more accurately. Some or all of the above processing in the identification processing unit may be performed using AI, for example, or without AI. For example, the identification processing unit can input competitor trends and customer feedback into AI, which can then identify market trends and opportunities.

[0082] The data collection unit can collect data from at least one of the following sources: publicly available information on the internet, social media, news articles, and blogs. For example, the data collection unit can collect news articles and analyze their content. For example, the data collection unit can collect posts on social media and save them as text data. For example, the data collection unit can collect blog posts and analyze their content. This allows the data collection unit to conduct more comprehensive market research by collecting data from diverse data sources. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input news articles into an AI, which can then analyze the content of the articles to identify market trends.

[0083] The identification unit can use machine learning algorithms to identify market trends and opportunities. For example, the identification unit can use clustering algorithms to identify market trends. The identification unit can also use classification algorithms to identify market opportunities. The identification unit can also use regression analysis to predict market trends. This allows the identification unit to identify market trends and opportunities more accurately by using machine learning algorithms. Some or all of the above processes in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input collected data into AI, which can then use machine learning algorithms to identify market trends and opportunities.

[0084] The presentation unit can visually present market trends and opportunities using a user-customizable dashboard. For example, the presentation unit can visually present market trends using graphs and charts. The presentation unit can also visually present market trends and opportunities using a dashboard. For example, the presentation unit can add user-customizable widgets. The presentation unit can also visually present market trends and opportunities using filtering functions. This makes it easier for users to visually grasp market trends and opportunities using a customizable dashboard. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not. For example, the presentation unit can visually present market trends and opportunities using graphs and charts generated by AI.

[0085] The specific unit may have the function of comparing market trends and opportunities across different timeframes. For example, the specific unit may compare historical and current data to predict future market trends. For example, the specific unit may compare market trends and opportunities across timeframes such as the past month, the past year, or the past five years. The specific unit may also predict market trends based on data across different timeframes. This makes it easier for the specific unit to predict future market trends by comparing market trends and opportunities across different timeframes. Some or all of the processing described above in the specific unit may be performed using AI, for example, or not using AI. For example, the specific unit may input historical and current data into AI, which can then compare and predict market trends and opportunities across different timeframes.

[0086] The presentation unit may have a function to filter market trends and opportunities based on user-defined parameters. For example, the presentation unit may filter market trends and opportunities based on specific keywords. The presentation unit may also filter market trends and opportunities based on a date range. The presentation unit may also filter market trends and opportunities based on a data source. This allows the presentation unit to extract only the information of interest to the user and conduct market research efficiently. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit may input user-defined parameters into an AI, which can then filter market trends and opportunities.

[0087] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can temporarily stop data collection and resume it when the user is relaxed. For example, if the user is concentrating, the data collection unit can also collect data quickly to avoid interrupting the user's work. For example, if the user is tired, the data collection unit can delay data collection and resume it after the user has rested. In this way, the data collection unit can collect data at a more appropriate time by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user facial expression data into an AI, which can estimate the user's emotions and adjust the timing of data collection.

[0088] The data collection unit can evaluate the reliability of the data to be collected and prioritize the collection of reliable data. For example, the data collection unit can evaluate the reliability of data sources and prioritize the collection of data from reliable sources. For example, the data collection unit can also check the consistency of data and prioritize the collection of consistent data. For example, the data collection unit can evaluate the timeliness of data and prioritize the collection of the most recent data. This allows the data collection unit to conduct more accurate market research by prioritizing the collection of reliable data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the reliability of data sources into AI, and the AI ​​can prioritize the collection of reliable data.

[0089] The data collection unit can customize the types of data it collects based on the user's industry and interests. For example, if the user belongs to the financial industry, the data collection unit will prioritize collecting financial-related data. For example, if the user is interested in the technology industry, the data collection unit can prioritize collecting technology-related data. For example, if the user is interested in the health industry, the data collection unit can prioritize collecting health-related data. This allows the data collection unit to collect more relevant data by customizing the data based on the user's industry and interests. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's industry and interests into the AI ​​and customize the types of data the AI ​​collects.

[0090] 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 data. For example, if the user is relaxed, the data collection unit may prioritize collecting detailed analytical data. For example, if the user is stressed, the data collection unit may prioritize collecting concise and to-the-point data. In this way, the data collection unit can collect more appropriate data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user facial expression data into an AI, which can estimate the user's emotions and determine the priority of data.

[0091] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. For example, if the user is traveling, the data collection unit can prioritize the collection of data related to the travel destination. For example, if the user is in a specific city, the data collection unit can prioritize the collection of market data related to that city. In this way, the data collection unit can collect more 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 AI, which can then prioritize the collection of highly relevant data.

[0092] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user uses a specific hashtag, the data collection unit can collect data related to that hashtag. For example, if a user posts about a specific topic, the data collection unit can also collect data related to that topic. For example, if a user belongs to a specific group, the data collection unit can also collect data related to that group. This allows the data collection unit to collect more 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 into AI, and the AI ​​can collect relevant data.

[0093] The identification unit can estimate the user's emotions and adjust the method of identifying trends and opportunities based on the estimated user emotions. For example, if the user is excited, the identification unit may highlight and identify the latest trends. For example, if the user is relaxed, the identification unit may also perform a detailed analysis and identify trends and opportunities. For example, if the user is stressed, the identification unit may also identify concise and to-the-point trends and opportunities. This allows the identification unit to identify more appropriate trends and opportunities by adjusting the method of identifying trends and opportunities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI or not using AI. For example, the identification unit can input user facial expression data into AI, which can estimate the user's emotions and adjust the method of identifying trends and opportunities.

[0094] The specific unit can predict future market trends by comparing past and present data at a specific time. For example, the specific unit can predict future sales trends by comparing past sales data with current sales data. The specific unit can also predict future customer needs by comparing past customer feedback with current feedback. The specific unit can also predict future market share by comparing past market share data with current market share data. This makes it easier for the specific unit to predict future market trends by comparing past and present data. Some or all of the above processing in the specific unit may be performed using AI, for example, or without AI. For example, the specific unit can input past and present data into AI, and the AI ​​can predict future market trends.

[0095] The identification unit can identify trends and opportunities by integrating information from different data sources at the time of identification. For example, the identification unit can identify trends by integrating social media data and news article data. The identification unit can also identify opportunities by integrating blog data and market research data. The identification unit can also identify trends and opportunities by integrating customer reviews and product evaluation data. This allows the identification unit to identify more accurate trends and opportunities by integrating information from different data sources. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input information from different data sources into AI, which can then identify trends and opportunities.

[0096] The identification unit can estimate the user's emotions and determine the priority of trends and opportunities to identify based on the estimated user emotions. For example, if the user is excited, the identification unit may prioritize identifying the latest trends. If the user is relaxed, the identification unit may also perform a detailed analysis and determine priorities. If the user is stressed, the identification unit may also prioritize identifying concise and to-the-point trends and opportunities. This allows the identification unit to identify more appropriate trends and opportunities by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the identification unit may be performed using AI or not using AI. For example, the identification unit can input user facial expression data into an AI, which can estimate the user's emotions and determine the priority of trends and opportunities.

[0097] The identification unit can identify trends and opportunities by considering industry-specific factors of the user at the time of identification. For example, if the user belongs to the financial industry, the identification unit can identify trends and opportunities in the financial market. For example, if the user belongs to the technology industry, the identification unit can also identify trends and opportunities in the technology market. For example, if the user belongs to the health industry, the identification unit can also identify trends and opportunities in the health market. This allows the identification unit to identify more relevant trends and opportunities by considering industry-specific factors of the user. Some or all of the above processing in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input industry-specific factors of the user into the AI, and the AI ​​can identify trends and opportunities.

[0098] The identification unit can identify trends and opportunities by referring to the user's past behavioral history at the time of identification. For example, the identification unit can identify current trends based on trends the user has been interested in in the past. The identification unit can also identify future opportunities based on events the user has participated in in the past. The identification unit can also identify current market opportunities based on products the user has purchased in the past. In this way, the identification unit can identify more relevant trends and opportunities by referring to the user's past behavioral history. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the user's past behavioral history into AI, which can then identify trends and opportunities.

[0099] The presentation unit can estimate the user's emotions and adjust the visual presentation method based on the estimated emotions. For example, if the user is tense, the presentation unit can provide a simple and highly visible graph. For example, if the user is relaxed, the presentation unit can also provide a chart containing detailed information. For example, if the user is excited, the presentation unit can provide a dashboard with visually stimulating effects. This allows the presentation unit to provide more appropriate information by adjusting the visual presentation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit can input user facial expression data into AI, which can estimate the user's emotions and adjust the visual presentation method.

[0100] The presentation unit can select the optimal display method by referring to the user's past browsing history when presenting information. For example, the presentation unit can select the optimal display method based on the graph format the user has previously preferred to use. The presentation unit can also display relevant information based on the types of data the user has previously viewed. The presentation unit can also select the optimal display method based on the customization settings the user has previously used. In this way, the presentation unit can select a more appropriate display method by referring to the user's past browsing history. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's past browsing history into AI, which can then select the optimal display method.

[0101] The presentation unit can provide display methods that are compatible with different devices at the time of presentation. For example, the presentation unit can provide a display method optimized for smartphones. The presentation unit can also provide a display method optimized for tablets. The presentation unit can also provide a display method optimized for desktops. By providing display methods that are compatible with different devices, the presentation unit makes it easier for users to view information on any device. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input display methods compatible with different devices into AI, and the AI ​​can provide the optimal display method.

[0102] The presentation unit can estimate the user's emotions and determine the priority of the information to be presented based on the estimated emotions. For example, if the user is excited, the presentation unit may prioritize displaying the latest trending information. For example, if the user is relaxed, the presentation unit may prioritize displaying detailed analytical information. For example, if the user is stressed, the presentation unit may prioritize displaying concise and to-the-point information. In this way, the presentation unit can provide more appropriate information by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI, or not using AI. For example, the presentation unit can input user facial expression data into AI, and the AI ​​can estimate the user's emotions and determine the priority of information.

[0103] The presentation unit can prioritize displaying highly relevant information by considering the user's geographical location. For example, if the user is in a specific region, the presentation unit can prioritize displaying market information related to that region. For example, if the user is traveling, the presentation unit can prioritize displaying market information related to the travel destination. For example, if the user is in a specific city, the presentation unit can prioritize displaying market information related to that city. In this way, the presentation unit can provide more relevant information by considering the user's geographical location. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's geographical location information into AI, which can then prioritize displaying highly relevant information.

[0104] The presentation unit can analyze the user's social media activity and display relevant information at the time of presentation. For example, if the user is using a specific hashtag, the presentation unit can display information related to that hashtag. For example, if the user is posting about a specific topic, the presentation unit can also display information related to that topic. For example, if the user is a member of a specific group, the presentation unit can also display information related to that group. In this way, the presentation unit can provide more relevant information by analyzing the user's social media activity. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's social media activity into AI, and the AI ​​can display relevant information.

[0105] The analysis processing unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is excited, the analysis processing unit may highlight the most recent data in its analysis. For example, if the user is relaxed, the analysis processing unit may perform a detailed analysis and provide the results. For example, if the user is stressed, the analysis processing unit may provide a concise and to-the-point analysis result. In this way, the analysis processing unit can provide more appropriate analysis results by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis processing unit may be performed using AI or not using AI. For example, the analysis processing unit can input the user's facial expression data into an AI, which can estimate the user's emotions and adjust the analysis method.

[0106] The analytical processing unit can integrate information from different data sources during analysis to analyze competitor trends and customer feedback. For example, the analytical processing unit can integrate social media data and news article data to analyze competitor trends. The analytical processing unit can also integrate blog data and market research data to analyze customer feedback. The analytical processing unit can also integrate customer reviews and product evaluation data to analyze competitor trends and customer feedback. This allows the analytical processing unit to analyze competitor trends and customer feedback more accurately by integrating information from different data sources. Some or all of the above processing in the analytical processing unit may be performed using AI, for example, or without AI. For example, the analytical processing unit can input information from different data sources into AI, which can then analyze competitor trends and customer feedback.

[0107] The analytical processing unit can predict competitor trends and customer feedback by comparing historical and current data during analysis. For example, the analytical processing unit can compare historical and current sales data to predict future competitor trends. The analytical processing unit can also predict future customer needs by comparing historical and current customer feedback. The analytical processing unit can also predict future market share of competitors by comparing historical and current market share data. This makes it easier for the analytical processing unit to predict future competitor trends and customer feedback by comparing historical and current data. Some or all of the above processing in the analytical processing unit may be performed using AI, for example, or without AI. For example, the analytical processing unit can input historical and current data into AI, which can then predict future competitor trends and customer feedback.

[0108] The analysis processing unit can estimate the user's emotions and prioritize analysis results based on the estimated emotions. For example, if the user is excited, the analysis processing unit may prioritize analyzing the latest competitor trends. If the user is relaxed, the analysis processing unit may also prioritize analyzing detailed customer feedback. If the user is stressed, the analysis processing unit may also prioritize providing concise and to-the-point analysis results. This allows the analysis processing unit to provide more appropriate analysis results by prioritizing analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis processing unit may be performed using AI or not. For example, the analysis processing unit can input user facial expression data into an AI, which can estimate the user's emotions and determine the priority of analysis results.

[0109] The analysis processing unit can analyze competitor trends and customer feedback while considering factors specific to the user's industry. For example, if the user belongs to the financial industry, the analysis processing unit can analyze competitor trends and customer feedback in the financial market. If the user belongs to the technology industry, the analysis processing unit can also analyze competitor trends and customer feedback in the technology market. If the user belongs to the health industry, the analysis processing unit can also analyze competitor trends and customer feedback in the health market. This allows the analysis processing unit to analyze competitor trends and customer feedback that are more relevant by considering factors specific to the user's industry. Some or all of the processing described above in the analysis processing unit may be performed using AI, for example, or without AI. For example, the analysis processing unit can input industry-specific factors of the user into AI, and the AI ​​can analyze competitor trends and customer feedback.

[0110] The analysis processing unit can analyze competitor trends and customer feedback by referring to the user's past behavior history during analysis. For example, the analysis processing unit can analyze current trends based on the trends of competitors that the user has been interested in in the past. The analysis processing unit can also analyze current customer feedback based on customer feedback that the user has collected in the past. The analysis processing unit can also analyze competitor trends and customer feedback based on events that the user has participated in in the past. In this way, the analysis processing unit can analyze competitor trends and customer feedback that are more relevant by referring to the user's past behavior history. Some or all of the above processing in the analysis processing unit may be performed using AI, for example, or without using AI. For example, the analysis processing unit can input the user's past behavior history into AI, and the AI ​​can analyze competitor trends and customer feedback.

[0111] The identification processing unit can estimate the user's emotions and adjust the method of identification based on the estimated user emotions. For example, if the user is excited, the identification processing unit may highlight and identify the latest market trends. For example, if the user is relaxed, the identification processing unit may perform a detailed analysis and then perform identification. For example, if the user is stressed, the identification processing unit may perform a concise and to-the-point identification. This allows the identification processing unit to perform more appropriate identification by adjusting the method of identification according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the identification processing unit may be performed using AI or not using AI. For example, the identification processing unit can input user facial expression data into an AI, which can estimate the user's emotions and adjust the method of identification.

[0112] The identification processing unit can identify market trends and opportunities by integrating information from different data sources during the identification process. For example, the identification processing unit can identify market trends by integrating social media data and news article data. The identification processing unit can also identify market opportunities by integrating blog data and market research data. The identification processing unit can also identify market trends and opportunities by integrating customer reviews and product evaluation data. This allows the identification processing unit to identify market trends and opportunities more accurately by integrating information from different data sources. Some or all of the above processing in the identification processing unit may be performed using AI, for example, or without AI. For example, the identification processing unit can input information from different data sources into AI, which can then identify market trends and opportunities.

[0113] A specific processing unit can predict market trends and opportunities by comparing historical and current data during a specific processing step. For example, the specific processing unit can compare historical and current sales data to predict future market trends. The specific processing unit can also predict future customer needs by comparing historical and current customer feedback. The specific processing unit can also predict future market opportunities by comparing historical and current market share data. This makes it easier for the specific processing unit to predict future market trends and opportunities by comparing historical and current data. Some or all of the processing described above in the specific processing unit may be performed using AI, for example, or without AI. For example, the specific processing unit can input historical and current data into AI, which can then predict future market trends and opportunities.

[0114] The specific processing unit can estimate the user's emotions and determine the priority of specific processing based on the estimated user emotions. For example, if the user is excited, the specific processing unit may prioritize identifying the latest market trends. If the user is relaxed, the specific processing unit may also perform a detailed analysis and determine its priorities. If the user is stressed, the specific processing unit may also prioritize identifying concise and to-the-point market trends and opportunities. This allows the specific processing unit to identify more appropriate market trends and opportunities by prioritizing specific processing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 specific processing unit may be performed using AI or not using AI. For example, the specific processing unit can input user facial expression data into an AI, which can estimate the user's emotions and determine the priority of specific processing.

[0115] The specific processing unit can identify market trends and opportunities by considering factors specific to the user's industry during the specific processing. For example, if the user belongs to the financial industry, the specific processing unit can identify trends and opportunities in the financial market. For example, if the user belongs to the technology industry, the specific processing unit can also identify trends and opportunities in the technology market. For example, if the user belongs to the health industry, the specific processing unit can also identify trends and opportunities in the health market. This allows the specific processing unit to identify more relevant market trends and opportunities by considering factors specific to the user's industry. Some or all of the processing described above in the specific processing unit may be performed using AI, for example, or without AI. For example, the specific processing unit can input industry-specific factors of the user into the AI, and the AI ​​can identify market trends and opportunities.

[0116] The identification processing unit can identify market trends and opportunities by referring to the user's past behavioral history during the identification process. For example, the identification processing unit can identify current trends based on market trends the user has been interested in in the past. The identification processing unit can also identify future market opportunities based on events the user has participated in in the past. The identification processing unit can also identify current market opportunities based on products the user has purchased in the past. In this way, the identification processing unit can identify more relevant market trends and opportunities by referring to the user's past behavioral history. Some or all of the above processing in the identification processing unit may be performed using AI, for example, or without AI. For example, the identification processing unit can input the user's past behavioral history into AI, which can then identify market trends and opportunities.

[0117] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0118] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can temporarily stop data collection and resume it when the user is relaxed. For example, if the user is concentrating, the data collection unit can also collect data quickly to avoid interrupting the user's work. For example, if the user is tired, the data collection unit can delay data collection and resume it after the user has rested. In this way, the data collection unit can collect data at a more appropriate time by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user facial expression data into an AI, which can estimate the user's emotions and adjust the timing of data collection.

[0119] The data collection unit can evaluate the reliability of the data to be collected and prioritize the collection of reliable data. For example, the data collection unit can evaluate the reliability of data sources and prioritize the collection of data from reliable sources. For example, the data collection unit can also check the consistency of data and prioritize the collection of consistent data. For example, the data collection unit can evaluate the timeliness of data and prioritize the collection of the most recent data. This allows the data collection unit to conduct more accurate market research by prioritizing the collection of reliable data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the reliability of data sources into AI, and the AI ​​can prioritize the collection of reliable data.

[0120] The data collection unit can customize the types of data it collects based on the user's industry and interests. For example, if the user belongs to the financial industry, the data collection unit will prioritize collecting financial-related data. For example, if the user is interested in the technology industry, the data collection unit can prioritize collecting technology-related data. For example, if the user is interested in the health industry, the data collection unit can prioritize collecting health-related data. This allows the data collection unit to collect more relevant data by customizing the data based on the user's industry and interests. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's industry and interests into the AI ​​and customize the types of data the AI ​​collects.

[0121] 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 data. For example, if the user is relaxed, the data collection unit may prioritize collecting detailed analytical data. For example, if the user is stressed, the data collection unit may prioritize collecting concise and to-the-point data. In this way, the data collection unit can collect more appropriate data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user facial expression data into an AI, which can estimate the user's emotions and determine the priority of data.

[0122] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. For example, if the user is traveling, the data collection unit can prioritize the collection of data related to the travel destination. For example, if the user is in a specific city, the data collection unit can prioritize the collection of market data related to that city. In this way, the data collection unit can collect more 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 AI, which can then prioritize the collection of highly relevant data.

[0123] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user uses a specific hashtag, the data collection unit can collect data related to that hashtag. For example, if a user posts about a specific topic, the data collection unit can also collect data related to that topic. For example, if a user belongs to a specific group, the data collection unit can also collect data related to that group. This allows the data collection unit to collect more 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 into AI, and the AI ​​can collect relevant data.

[0124] The identification unit can estimate the user's emotions and adjust the method of identifying trends and opportunities based on the estimated user emotions. For example, if the user is excited, the identification unit may highlight and identify the latest trends. For example, if the user is relaxed, the identification unit may also perform a detailed analysis and identify trends and opportunities. For example, if the user is stressed, the identification unit may also identify concise and to-the-point trends and opportunities. This allows the identification unit to identify more appropriate trends and opportunities by adjusting the method of identifying trends and opportunities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI or not using AI. For example, the identification unit can input user facial expression data into AI, which can estimate the user's emotions and adjust the method of identifying trends and opportunities.

[0125] The specific unit can predict future market trends by comparing past and present data at a specific time. For example, the specific unit can predict future sales trends by comparing past sales data with current sales data. The specific unit can also predict future customer needs by comparing past customer feedback with current feedback. The specific unit can also predict future market share by comparing past market share data with current market share data. This makes it easier for the specific unit to predict future market trends by comparing past and present data. Some or all of the above processing in the specific unit may be performed using AI, for example, or without AI. For example, the specific unit can input past and present data into AI, and the AI ​​can predict future market trends.

[0126] The identification unit can identify trends and opportunities by integrating information from different data sources at the time of identification. For example, the identification unit can identify trends by integrating social media data and news article data. The identification unit can also identify opportunities by integrating blog data and market research data. The identification unit can also identify trends and opportunities by integrating customer reviews and product evaluation data. This allows the identification unit to identify more accurate trends and opportunities by integrating information from different data sources. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input information from different data sources into AI, which can then identify trends and opportunities.

[0127] The identification unit can estimate the user's emotions and determine the priority of trends and opportunities to identify based on the estimated user emotions. For example, if the user is excited, the identification unit may prioritize identifying the latest trends. If the user is relaxed, the identification unit may also perform a detailed analysis and determine priorities. If the user is stressed, the identification unit may also prioritize identifying concise and to-the-point trends and opportunities. This allows the identification unit to identify more appropriate trends and opportunities by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the identification unit may be performed using AI or not using AI. For example, the identification unit can input user facial expression data into an AI, which can estimate the user's emotions and determine the priority of trends and opportunities.

[0128] The following briefly describes the processing flow for example form 2.

[0129] Step 1: The data collection unit collects market-related data. The data collection unit can collect data from publicly available information on the internet, social media, news articles, blogs, etc. The data collection unit uses web scraping techniques and APIs to collect data. For example, it can collect posts from social media and save them as text data. It can also collect news articles and blog posts and analyze their content. Step 2: The Identification Unit identifies market trends and opportunities based on the data collected by the Collection Unit. The Identification Unit uses machine learning algorithms to identify market trends and opportunities. For example, it predicts future market trends based on historical data and finds new business opportunities. Clustering algorithms, classification algorithms, and regression analysis can be used to identify market trends and opportunities. Step 3: The presentation section visually presents the market trends and opportunities identified by the specific section. The presentation section visually presents market trends and opportunities using a user-customizable dashboard. It includes graphs and charts to allow users to grasp market trends at a glance and to compare market trends and opportunities across different timeframes. It allows users to compare historical and current data to predict future market trends. It also includes a function to filter market trends and opportunities based on user-defined parameters.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] For example, the data collection unit is implemented in either the data processing unit 12 or the smart device 14. For example, the identification processing unit 290 of the data processing unit 12 collects data from publicly available information on the internet, social media, news articles, blogs, etc. For example, the control unit 46A of the smart device 14 collects the content of social media posts and news articles and stores it as text data. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and identifies market trends and opportunities using machine learning algorithms. The presentation unit is implemented, for example, by the control unit 46A of the smart device 14, and visually presents market trends and opportunities using a user-customizable dashboard. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0134] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] For example, the data collection unit is implemented in either the data processing unit 12 or the smart glasses 214. For example, the identification processing unit 290 of the data processing unit 12 collects data from publicly available information on the internet, social media, news articles, blogs, etc. For example, the control unit 46A of the smart glasses 214 collects the content of social media posts and news articles and stores it as text data. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and identifies market trends and opportunities using machine learning algorithms. The presentation unit is implemented, for example, by the control unit 46A of the smart glasses 214, and visually presents market trends and opportunities using a user-customizable dashboard. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0150] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In 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.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 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.

[0165] For example, the data collection unit is implemented in either the data processing unit 12 or the headset terminal 314. For example, the identification processing unit 290 of the data processing unit 12 collects data from publicly available information on the internet, social networking services (SNS), news articles, blogs, etc. For example, the control unit 46A of the headset terminal 314 collects the content of posts on SNS and news articles and stores it as text data. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and uses a machine learning algorithm to identify market trends and opportunities. The presentation unit is implemented, for example, by the control unit 46A of the headset terminal 314, and visually presents market trends and opportunities using a user-customizable dashboard. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0166] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] For example, the data collection unit is implemented in either the data processing unit 12 or the robot 414. For example, the identification processing unit 290 of the data processing unit 12 collects data from publicly available information on the internet, social media, news articles, blogs, etc. For example, the control unit 46A of the robot 414 collects the content of social media posts and news articles and stores it as text data. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and uses a machine learning algorithm to identify market trends and opportunities. The presentation unit is implemented, for example, by the control unit 46A of the robot 414, and visually presents market trends and opportunities using a user-customizable dashboard. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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."

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] (Note 1) A data collection department that collects market data, Based on the data collected by the aforementioned collection unit, an identification unit identifies market trends and opportunities, The system includes a display unit that visually presents the market trends and opportunities identified by the specified unit. A system characterized by the following features. (Note 2) The specified part is, The system includes an analysis processing unit that analyzes the trends of competitors based on the data collected by the aforementioned data collection unit. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned display unit is, The results of the analysis of the trends of the aforementioned competitors are presented visually. The system described in Appendix 2, characterized by the features described herein. (Note 4) The aforementioned analytical processing unit In addition to the trends of the aforementioned competitors, we will analyze customer feedback. The system described in Appendix 2, characterized by the features described herein. (Note 5) The aforementioned display unit is, The results of the analysis of the aforementioned customer feedback are presented visually. The system described in Appendix 4, characterized by the features described herein. (Note 6) The aforementioned analytical processing unit The system includes an identification processing unit that identifies market trends and opportunities based on the analysis results of the aforementioned competitors' activities and customer feedback. The system described in Appendix 4, characterized by the features described herein. (Note 7) The aforementioned collection unit is The aforementioned data is collected from at least one of the following sources: publicly available information on the internet, social media, news articles, or blogs. The system described in Appendix 1, characterized by the features described herein. (Note 8) The specified part is, Identifying market trends and opportunities using machine learning algorithms The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned display unit is, The system visually presents market trends and opportunities using a user-customizable dashboard. The system described in Appendix 1, characterized by the features described herein. (Note 10) The specified part is, Features the ability to compare market trends and opportunities across different timeframes. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned display unit is, It features the ability to filter market trends and opportunities based on user-defined parameters. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is The data to be collected is evaluated for reliability, and reliable data is prioritized for collection. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is Customize the types of data collected based on the user's industry and interests. The system described in Appendix 1, characterized by the features described herein. (Note 15) 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 16) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The specified part is, We estimate user sentiment and adjust how we identify trends and opportunities based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The specified part is, At specific points in time, past and present data are compared to predict future market trends. The system described in Appendix 1, characterized by the features described herein. (Note 20) The specified part is, At specific times, integrate information from different data sources to identify trends and opportunities. The system described in Appendix 1, characterized by the features described herein. (Note 21) The specified part is, Infer user sentiment and prioritize trends and opportunities based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The specified part is, Identify trends and opportunities by considering industry-specific factors for the user at the appropriate time. The system described in Appendix 1, characterized by the features described herein. (Note 23) The specified part is, At specific times, we identify trends and opportunities by referring to the user's past behavior history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is, It estimates the user's emotions and adjusts the visual presentation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is, When presenting content, the system selects the optimal display method by referring to the user's past browsing history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is, When presenting, provide display methods that are compatible with different devices. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is, It estimates the user's emotions and determines the priority of the information presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is, When presenting information, the system prioritizes displaying the most relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is, When presenting the information, the system analyzes the user's social media activity and displays relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned analytical processing unit We estimate the user's emotions and adjust the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned analytical processing unit During analysis, we integrate information from different data sources to analyze competitor trends and customer feedback. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned analytical processing unit During analysis, historical and current data are compared to predict competitor trends and customer feedback. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned analytical processing unit It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned analytical processing unit During the analysis, we consider industry-specific factors for the user and analyze competitor trends and customer feedback. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned analytical processing unit During analysis, we refer to users' past behavioral history to analyze competitor trends and customer feedback. The system described in Appendix 1, characterized by the features described herein. (Note 36) The specified processing unit is, It estimates the user's emotions and adjusts the method of specific processing based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The specified processing unit is, During specific processing, information from different data sources is integrated to identify market trends and opportunities. The system described in Appendix 1, characterized by the features described herein. (Note 38) The specified processing unit is, During specific processing, historical and current data are compared to predict market trends and opportunities. The system described in Appendix 1, characterized by the features described herein. (Note 39) The specified processing unit is, It estimates the user's emotions and determines the priority of specific processes based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 40) The specified processing unit is, During specific processing, we identify market trends and opportunities by considering factors specific to the user's industry. The system described in Appendix 1, characterized by the features described herein. (Note 41) The specified processing unit is, During specific processing, market trends and opportunities are identified by referencing the user's past behavior history. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0202] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection department that collects market data, Based on the data collected by the aforementioned collection unit, an identification unit identifies market trends and opportunities, The system includes a display unit that visually presents the market trends and opportunities identified by the specified unit. A system characterized by the following features.

2. The specified part is, The system includes an analysis processing unit that analyzes the trends of competitors based on the data collected by the aforementioned data collection unit. The system according to feature 1.

3. The aforementioned display unit is, The results of the analysis of the trends of the aforementioned competitors are presented visually. The system according to feature 2.

4. The aforementioned analytical processing unit In addition to the trends of the aforementioned competitors, we will analyze customer feedback. The system according to feature 2.

5. The aforementioned display unit is, The results of the analysis of the aforementioned customer feedback are presented visually. The system according to feature 4.

6. The aforementioned analytical processing unit The system includes an identification processing unit that identifies market trends and opportunities based on the analysis results of the aforementioned competitors' activities and customer feedback. The system according to feature 4.

7. The aforementioned collection unit is The aforementioned data is collected from at least one of the following sources: publicly available information on the internet, social media, news articles, or blogs. The system according to feature 1.

8. The specified part is, Identifying market trends and opportunities using machine learning algorithms The system according to feature 1.

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