system

Abstract

The system according to this embodiment aims to analyze publicly available data in real time and support rapid decision-making. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit acquires publicly available data from the web in real time. The analysis unit analyzes the data acquired by the collection unit and performs trend prediction. The provision unit visually displays the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it takes a lot of time to collect and analyze market research data, and it is difficult to make decisions according to the situation.

[0005] The system according to the embodiment aims to analyze public data in real time and support rapid decision-making.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit acquires public data on the web in real time. The analysis unit analyzes the data acquired by the collection unit and performs trend prediction. The provision unit visually displays the analysis result obtained by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can analyze publicly available data in real time and support rapid decision-making. [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) The market research support system according to an embodiment of the present invention is a system that uses AI to collect and analyze market research data in real time, supporting rapid decision-making. This market research support system uses AI to acquire publicly available data from the web in real time, performing automatic acquisition through data linkage. Next, it analyzes the acquired data using natural language processing and machine learning to predict trends. The analysis results are displayed on a visually easy-to-understand dashboard and also include a notification function to mobile devices. This enables corporate management and market research personnel to conduct effective market analysis in a short time, realizing data-driven immediate responses. For example, the market research support system uses AI to acquire publicly available data from the web in real time. In this process, it utilizes data linkage to perform automatic data acquisition. For example, it can collect data from news sites and social networking services to grasp the latest market trends. This significantly reduces the time required for data collection. Next, the market research support system analyzes the acquired data using natural language processing and machine learning. The AI ​​analyzes the collected data and predicts future trends. For example, it can analyze the frequency and relevance of specific keywords to predict future market trends. This allows companies to respond quickly to market changes. The analysis results are displayed on a visually intuitive dashboard. The dashboard is designed for intuitive understanding by management and market researchers, allowing them to grasp key information at a glance. It also features mobile device notification capabilities, enabling real-time information delivery. This allows companies to rapidly formulate strategies and build effective market adaptation strategies. This system enhances companies' ability to respond immediately through digital transformation and build strategies that avoid missing market opportunities. For example, it can handle various practical cases such as pre-market analysis of new products, market evaluation of competitor products, and analysis of current consumer trends. This allows companies to shift from reactive to proactive and maintain a competitive advantage. In this way, the market research support system supports companies in making rapid decisions and maintaining a competitive advantage.

[0029] The market research support system according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit acquires publicly available data from the web in real time. For example, the data collection unit can collect data from news sites and social networking services to grasp the latest market trends. The data collection unit can also automatically acquire data by utilizing data linkage. For example, it can acquire data from a database using an API. The analysis unit analyzes the data acquired by the data collection unit and makes trend predictions. The analysis unit analyzes the data using natural language processing and machine learning. For example, it analyzes the frequency and relevance of specific keywords to predict future market trends. The analysis unit can use machine learning algorithms such as neural networks and support vector machines. The data provision unit visually displays the analysis results obtained by the analysis unit. The data provision unit displays the analysis results on a visually easy-to-understand dashboard. The data provision unit can also be equipped with a notification function for mobile devices. For example, it can provide information in real time using push notifications or email notifications. As a result, the market research support system according to this embodiment can collect and analyze market research data in real time and support rapid decision-making.

[0030] The data collection unit acquires publicly available data from the web in real time. Specifically, it collects data from news sites and social networking services to understand the latest market trends. The data collection unit automatically collects news articles, blog posts, and SNS posts using web scraping technology. For example, it uses libraries such as Python's BeautifulSoup and Scrapy to analyze the HTML structure of web pages and extract necessary information. The data collection unit can also acquire data from databases using APIs. For example, it can use SNS APIs to collect posts related to specific keywords or news APIs to acquire the latest news articles. Furthermore, the data collection unit can automate data acquisition by leveraging data integration. For example, it can use RSS feeds to periodically acquire updates from news sites and store them in a database. This allows the data collection unit to collect a wide range of data from diverse sources in real time and quickly grasp the latest market trends. The collected data is centrally stored in a database and managed so that the analysis unit can access it. The data collection unit can also perform preprocessing such as data deduplication and noise reduction to ensure data quality. This allows the data collection unit to collect data efficiently and accurately, improving the overall performance of the system.

[0031] The analysis unit analyzes the data acquired by the collection unit and performs trend predictions. The analysis unit utilizes natural language processing and machine learning to analyze the data. Specifically, it preprocesses the collected text data, performing processes such as tokenization, stop word removal, and stemming. Next, it analyzes the frequency and relevance of specific keywords to predict future market trends. For example, by extracting important keywords from text data and analyzing the changes in their frequency over time, it can determine whether a particular trend is rising or falling. The analysis unit can use machine learning algorithms such as neural networks and support vector machines. For example, it can use a neural network to extract features from text data and build a trend prediction model. It can also use a support vector machine to classify the collected data and identify data related to specific trends. Furthermore, the analysis unit can use clustering algorithms to group similar data and clarify trend patterns. This allows the analysis unit to quickly and accurately analyze the collected data and predict future market trends. The analysis results are sent to the delivery unit and displayed visually. The analysis unit can also utilize historical data and statistical information to perform long-term trend forecasts and risk assessments. This allows the analysis unit to handle not only real-time trend forecasts but also long-term market analysis, improving the overall reliability and usefulness of the system.

[0032] The service provider visually displays the analysis results obtained by the analysis provider. Specifically, it displays the analysis results on a visually easy-to-understand dashboard. The service provider uses tools and libraries specialized in data visualization to create graphs, charts, heatmaps, and more. For example, it uses JavaScript® libraries such as D3.js and Chart.js to create interactive graphs and charts, allowing users to intuitively understand the data. The service provider can also update analysis results in real time, ensuring that the latest information is always displayed. For example, graphs and charts on the dashboard are automatically updated in response to data updates from the collection and analysis providers. Furthermore, the service provider can also provide notification capabilities for mobile devices. For example, it can use push notifications or email notifications to notify users in real time of important market trends and changes in trends. This allows users to obtain the latest information anytime, anywhere, and make quick decisions. The service provider also considers the ease of use of the user interface, adopting an intuitive and easy-to-operate design. For example, it can use drag-and-drop functionality to allow users to freely customize the dashboard layout. The service provider can also collect user feedback and continuously improve the functionality and design of the dashboard. This allows the service provider to provide users with visually clear and easy-to-understand information, supporting them in making quick decisions.

[0033] The data collection unit can automatically acquire data by utilizing data integration. For example, the data collection unit can acquire data from a database using an API. The data collection unit can improve the efficiency of data collection by utilizing data integration. For example, the data collection unit can acquire data from multiple data sources simultaneously and integrate the data. By automating data acquisition, the data collection unit can significantly reduce the time and effort compared to manual data collection. Thus, utilizing data integration improves the efficiency of data collection.

[0034] The analysis unit can analyze data and predict trends using natural language processing and machine learning. For example, the analysis unit can analyze the frequency and relevance of specific keywords to predict future market trends. The analysis unit can analyze text data using natural language processing techniques. For example, it can perform morphological analysis, grammatical analysis, and semantic analysis. The analysis unit can learn data patterns and predict trends using machine learning algorithms. For example, it can use neural networks and support vector machines. As a result, the accuracy of trend prediction is improved by utilizing natural language processing and machine learning.

[0035] The service provider can display analysis results in a visually clear and easy-to-understand dashboard. For example, the service provider can use graphs and charts to visually display analysis results. Based on usability test results and design guidelines, the service provider can provide a visually clear and easy-to-understand display method. The service provider is designed to allow users to understand analysis results at a glance. For example, important information is highlighted to allow users to understand it intuitively. This makes understanding analysis results easier by displaying them in a visually clear and easy-to-understand dashboard.

[0036] The service provider can include a notification function for mobile devices. For example, it can provide information in real time using push notifications or email notifications. By including a notification function for mobile devices, users can receive information anytime, anywhere. The service provider can use the notification function to instantly convey important information to users. For example, it can provide real-time notifications of urgent market trends or important trend forecasts. This means that providing a notification function for mobile devices enables real-time information delivery.

[0037] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can prioritize collecting data from data sources that the user has frequently collected in the past. The data collection unit can optimize the data to be collected at specific time periods based on the user's past data collection history. The data collection unit can analyze the user's past data collection patterns and propose efficient collection methods. This enables efficient data collection by analyzing past data collection history.

[0038] The data collection unit can filter data based on the user's current areas of interest during collection. For example, the collection unit prioritizes collecting data related to topics the user is currently interested in. The collection unit can filter out unnecessary data based on the user's current areas of interest. The collection unit can automatically add new data sources related to the user's areas of interest. This allows for the collection of highly relevant data by filtering data based on the user's areas of interest.

[0039] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data related to the area where the user is currently located. The data collection unit can filter out unnecessary data based on the user's geographical location information. The data collection unit can automatically add new data sources related to the user's location information. This allows for the priority collection of highly relevant data by collecting data based on the user's geographical location information.

[0040] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, the data collection unit can prioritize collecting data related to topics that users are interested in on social media. The data collection unit can filter out unnecessary data based on users' social media activity. The data collection unit can automatically add new data sources related to users' social media activity. This allows for the collection of highly relevant data by collecting data based on users' social media activity.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on important data and a simplified analysis on less important data. The analysis unit can determine the priority of the analysis based on the importance of the data. The analysis unit can apply multiple analysis algorithms to important data. This allows for efficient analysis by adjusting the level of detail based on the importance of the data.

[0042] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, it can apply a natural language processing algorithm to text data and a statistical analysis algorithm to numerical data. It can also apply an image analysis algorithm to image data and a speech analysis algorithm to speech data. The analysis unit can automatically select the optimal analysis algorithm according to the data category. This improves analysis accuracy by applying the most suitable analysis algorithm according to the data category.

[0043] The analysis unit can determine the priority of analysis based on the data collection period. For example, the analysis unit can prioritize the analysis of the most recent data and postpone the analysis of older data. The analysis unit can also adjust the level of detail of the analysis based on the data collection period. The analysis unit can efficiently analyze data collected around the same time together. This enables efficient analysis by determining the priority of analysis based on the data collection period.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data and postpone the analysis of less relevant data. The analysis unit can also adjust the level of detail of the analysis based on the relevance of the data. The analysis unit can analyze highly relevant data together, enabling efficient analysis. As a result, efficient analysis becomes possible by adjusting the order of analysis based on the relevance of the data.

[0045] The service provider can select the optimal display method by referring to the user's past operation history at the time of provision. For example, the service provider can prioritize providing display methods that the user has previously preferred. The service provider can suggest the optimal display method based on the user's past operation history. The service provider can provide a customized display method based on the user's operation history. In this way, the optimal display method can be provided by referring to the user's past operation history.

[0046] The information provider can customize the displayed information based on the user's current areas of interest at the time of delivery. For example, the provider can prioritize displaying information related to topics the user is currently interested in. The provider can filter out unnecessary information based on the user's current areas of interest. The provider can automatically add new information related to the user's areas of interest. This allows for the provision of more relevant information by customizing the information based on the user's areas of interest.

[0047] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. If the user is using a tablet, the service provider can provide a display method optimized for a larger screen. If the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This improves visibility by providing the optimal display method based on the user's device information.

[0048] The service provider can analyze the user's social media activity and provide relevant information at the time of delivery. For example, the service provider can prioritize providing information related to topics the user has shown interest in on social media. The service provider can filter out unnecessary information based on the user's social media activity. The service provider can automatically add new information related to the user's social media activity. This allows the service provider to deliver highly relevant information based on the user's social media activity.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, it can prioritize collecting data from data sources that the user has frequently collected in the past. It can also optimize the data collected during specific time periods based on the user's past data collection history. By analyzing the user's past data collection patterns, it can propose efficient collection methods. This enables efficient data collection by analyzing past data collection history.

[0051] The data collection unit can filter data based on the user's current areas of interest during collection. For example, it can prioritize collecting data related to topics the user is currently interested in. It can also filter out unnecessary data based on the user's current areas of interest. New data sources related to the user's areas of interest can be automatically added. This allows for the collection of highly relevant data by filtering data based on the user's areas of interest.

[0052] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on important data and a simplified analysis on less important data. The analysis priority can be determined based on the importance of the data. Multiple analysis algorithms can be applied to important data. This allows for efficient analysis by adjusting the level of detail based on the importance of the data.

[0053] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, a natural language processing algorithm can be applied to text data, a statistical analysis algorithm to numerical data, an image analysis algorithm to image data, and a speech analysis algorithm to audio data. The optimal analysis algorithm can be automatically selected according to the data category. This improves analysis accuracy by applying the most suitable analysis algorithm for each data category.

[0054] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, it can prioritize providing the display method that the user has previously preferred. It can suggest the optimal display method based on the user's past operation history. It can provide a customized display method based on the user's operation history. In this way, the optimal display method can be provided by referring to the user's past operation history.

[0055] The information delivery system can customize the displayed content based on the user's current areas of interest at the time of delivery. For example, it can prioritize displaying information related to topics the user is currently interested in. It can also filter out unnecessary information based on the user's current areas of interest. New information related to the user's areas of interest can be automatically added. In this way, by customizing information based on the user's areas of interest, it can provide more relevant information.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit acquires publicly available data from the web in real time. For example, it can collect data from news sites and social networking services to understand the latest market trends. The data collection unit can also automate data acquisition by utilizing data integration. For example, it can use APIs to retrieve data from databases. Step 2: The analysis unit analyzes the data acquired by the data collection unit and makes trend predictions. The analysis unit uses natural language processing and machine learning to analyze the data. For example, it analyzes the frequency and relevance of specific keywords to predict future market trends. The analysis unit can use machine learning algorithms such as neural networks and support vector machines. Step 3: The service provider visually displays the analysis results obtained by the analysis unit. The service provider displays the analysis results on a visually easy-to-understand dashboard. The service provider can also include a notification function for mobile devices. For example, it can provide information in real time using push notifications or email notifications.

[0058] (Example of form 2) The market research support system according to an embodiment of the present invention is a system that uses AI to collect and analyze market research data in real time, supporting rapid decision-making. This market research support system uses AI to acquire publicly available data from the web in real time, performing automatic acquisition through data linkage. Next, it analyzes the acquired data using natural language processing and machine learning to predict trends. The analysis results are displayed on a visually easy-to-understand dashboard and also include a notification function to mobile devices. This enables corporate management and market research personnel to conduct effective market analysis in a short time, realizing data-driven immediate responses. For example, the market research support system uses AI to acquire publicly available data from the web in real time. In this process, it utilizes data linkage to perform automatic data acquisition. For example, it can collect data from news sites and social networking services to grasp the latest market trends. This significantly reduces the time required for data collection. Next, the market research support system analyzes the acquired data using natural language processing and machine learning. The AI ​​analyzes the collected data and predicts future trends. For example, it can analyze the frequency and relevance of specific keywords to predict future market trends. This allows companies to respond quickly to market changes. The analysis results are displayed on a visually intuitive dashboard. The dashboard is designed for intuitive understanding by management and market researchers, allowing them to grasp key information at a glance. It also features mobile device notification capabilities, enabling real-time information delivery. This allows companies to rapidly formulate strategies and build effective market adaptation strategies. This system enhances companies' ability to respond immediately through digital transformation and build strategies that avoid missing market opportunities. For example, it can handle various practical cases such as pre-market analysis of new products, market evaluation of competitor products, and analysis of current consumer trends. This allows companies to shift from reactive to proactive and maintain a competitive advantage. In this way, the market research support system supports companies in making rapid decisions and maintaining a competitive advantage.

[0059] The market research support system according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit acquires publicly available data from the web in real time. For example, the data collection unit can collect data from news sites and social networking services to grasp the latest market trends. The data collection unit can also automatically acquire data by utilizing data linkage. For example, it can acquire data from a database using an API. The analysis unit analyzes the data acquired by the data collection unit and makes trend predictions. The analysis unit analyzes the data using natural language processing and machine learning. For example, it analyzes the frequency and relevance of specific keywords to predict future market trends. The analysis unit can use machine learning algorithms such as neural networks and support vector machines. The data provision unit visually displays the analysis results obtained by the analysis unit. The data provision unit displays the analysis results on a visually easy-to-understand dashboard. The data provision unit can also be equipped with a notification function for mobile devices. For example, it can provide information in real time using push notifications or email notifications. As a result, the market research support system according to this embodiment can collect and analyze market research data in real time and support rapid decision-making.

[0060] The data collection unit acquires publicly available data from the web in real time. Specifically, it collects data from news sites and social networking services to understand the latest market trends. The data collection unit automatically collects news articles, blog posts, and SNS posts using web scraping technology. For example, it uses libraries such as Python's BeautifulSoup and Scrapy to analyze the HTML structure of web pages and extract necessary information. The data collection unit can also acquire data from databases using APIs. For example, it can use SNS APIs to collect posts related to specific keywords or news APIs to acquire the latest news articles. Furthermore, the data collection unit can automate data acquisition by leveraging data integration. For example, it can use RSS feeds to periodically acquire updates from news sites and store them in a database. This allows the data collection unit to collect a wide range of data from diverse sources in real time and quickly grasp the latest market trends. The collected data is centrally stored in a database and managed so that the analysis unit can access it. The data collection unit can also perform preprocessing such as data deduplication and noise reduction to ensure data quality. This allows the data collection unit to collect data efficiently and accurately, improving the overall performance of the system.

[0061] The analysis unit analyzes the data acquired by the collection unit and performs trend predictions. The analysis unit utilizes natural language processing and machine learning to analyze the data. Specifically, it preprocesses the collected text data, performing processes such as tokenization, stop word removal, and stemming. Next, it analyzes the frequency and relevance of specific keywords to predict future market trends. For example, by extracting important keywords from text data and analyzing the changes in their frequency over time, it can determine whether a particular trend is rising or falling. The analysis unit can use machine learning algorithms such as neural networks and support vector machines. For example, it can use a neural network to extract features from text data and build a trend prediction model. It can also use a support vector machine to classify the collected data and identify data related to specific trends. Furthermore, the analysis unit can use clustering algorithms to group similar data and clarify trend patterns. This allows the analysis unit to quickly and accurately analyze the collected data and predict future market trends. The analysis results are sent to the delivery unit and displayed visually. The analysis unit can also utilize historical data and statistical information to perform long-term trend forecasts and risk assessments. This allows the analysis unit to handle not only real-time trend forecasts but also long-term market analysis, improving the overall reliability and usefulness of the system.

[0062] The service provider visually displays the analysis results obtained by the analysis provider. Specifically, it displays the analysis results on a visually easy-to-understand dashboard. The service provider uses tools and libraries specialized in data visualization to create graphs, charts, heatmaps, and more. For example, it uses JavaScript libraries such as D3.js and Chart.js to create interactive graphs and charts, allowing users to intuitively understand the data. The service provider can also update the analysis results in real time, ensuring that the latest information is always displayed. For example, graphs and charts on the dashboard are automatically updated in response to data updates from the collection and analysis providers. Furthermore, the service provider can also provide notification capabilities for mobile devices. For example, it can use push notifications and email notifications to inform users in real time of important market trends and changes in trends. This allows users to obtain the latest information anytime, anywhere, and make quick decisions. The service provider also considers the ease of use of the user interface, adopting an intuitive and easy-to-operate design. For example, it can use drag-and-drop functionality to allow users to freely customize the dashboard layout. The service provider can also collect user feedback and continuously improve the functionality and design of the dashboard. This allows the service provider to provide users with visually clear and easy-to-understand information, supporting them in making quick decisions.

[0063] The data collection unit can automatically acquire data by utilizing data integration. For example, the data collection unit can acquire data from a database using an API. The data collection unit can improve the efficiency of data collection by utilizing data integration. For example, the data collection unit can acquire data from multiple data sources simultaneously and integrate the data. By automating data acquisition, the data collection unit can significantly reduce the time and effort compared to manual data collection. Thus, utilizing data integration improves the efficiency of data collection.

[0064] The analysis unit can analyze data and predict trends using natural language processing and machine learning. For example, the analysis unit can analyze the frequency and relevance of specific keywords to predict future market trends. The analysis unit can analyze text data using natural language processing techniques. For example, it can perform morphological analysis, grammatical analysis, and semantic analysis. The analysis unit can learn data patterns and predict trends using machine learning algorithms. For example, it can use neural networks and support vector machines. As a result, the accuracy of trend prediction is improved by utilizing natural language processing and machine learning.

[0065] The service provider can display analysis results in a visually clear and easy-to-understand dashboard. For example, the service provider can use graphs and charts to visually display analysis results. Based on usability test results and design guidelines, the service provider can provide a visually clear and easy-to-understand display method. The service provider is designed to allow users to understand analysis results at a glance. For example, important information is highlighted to allow users to understand it intuitively. This makes understanding analysis results easier by displaying them in a visually clear and easy-to-understand dashboard.

[0066] The service provider can include a notification function for mobile devices. For example, it can provide information in real time using push notifications or email notifications. By including a notification function for mobile devices, users can receive information anytime, anywhere. The service provider can use the notification function to instantly convey important information to users. For example, it can provide real-time notifications of urgent market trends or important trend forecasts. This means that providing a notification function for mobile devices enables real-time information delivery.

[0067] 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 reduce the frequency of data collection and collect only important data. If the user is relaxed, the data collection unit can increase the frequency of data collection and collect more detailed data. If the user is in a hurry, the data collection unit can prioritize the collection of important data in real time. This allows for more appropriate data collection 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0068] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can prioritize collecting data from data sources that the user has frequently collected in the past. The data collection unit can optimize the data to be collected at specific time periods based on the user's past data collection history. The data collection unit can analyze the user's past data collection patterns and propose efficient collection methods. This enables efficient data collection by analyzing past data collection history.

[0069] The data collection unit can filter data based on the user's current areas of interest during collection. For example, the collection unit prioritizes collecting data related to topics the user is currently interested in. The collection unit can filter out unnecessary data based on the user's current areas of interest. The collection unit can automatically add new data sources related to the user's areas of interest. This allows for the collection of highly relevant data by filtering data based on the user's areas of interest.

[0070] The data collection unit can estimate the user's emotions and prioritize the data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting only important data. If the user is relaxed, the data collection unit can prioritize collecting detailed data. If the user is in a hurry, the data collection unit can prioritize collecting important data in real time. This allows for the priority collection of important data by prioritizing data 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0071] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data related to the area where the user is currently located. The data collection unit can filter out unnecessary data based on the user's geographical location information. The data collection unit can automatically add new data sources related to the user's location information. This allows for the priority collection of highly relevant data by collecting data based on the user's geographical location information.

[0072] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, the data collection unit can prioritize collecting data related to topics that users are interested in on social media. The data collection unit can filter out unnecessary data based on users' social media activity. The data collection unit can automatically add new data sources related to users' social media activity. This allows for the collection of highly relevant data by collecting data based on users' social media activity.

[0073] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0074] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on important data and a simplified analysis on less important data. The analysis unit can determine the priority of the analysis based on the importance of the data. The analysis unit can apply multiple analysis algorithms to important data. This allows for efficient analysis by adjusting the level of detail based on the importance of the data.

[0075] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, it can apply a natural language processing algorithm to text data and a statistical analysis algorithm to numerical data. It can also apply an image analysis algorithm to image data and a speech analysis algorithm to speech data. The analysis unit can automatically select the optimal analysis algorithm according to the data category. This improves analysis accuracy by applying the most suitable analysis algorithm according to the data category.

[0076] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. If the user is excited, the analysis unit can provide a visually stimulating analysis. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. 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.

[0077] The analysis unit can determine the priority of analysis based on the data collection period. For example, the analysis unit can prioritize the analysis of the most recent data and postpone the analysis of older data. The analysis unit can also adjust the level of detail of the analysis based on the data collection period. The analysis unit can efficiently analyze data collected around the same time together. This enables efficient analysis by determining the priority of analysis based on the data collection period.

[0078] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data and postpone the analysis of less relevant data. The analysis unit can also adjust the level of detail of the analysis based on the relevance of the data. The analysis unit can analyze highly relevant data together, enabling efficient analysis. As a result, efficient analysis becomes possible by adjusting the order of analysis based on the relevance of the data.

[0079] The information provider can estimate the user's emotions and adjust the way the information is displayed based on those emotions. For example, if the user is nervous, the provider can provide a simple and highly visible display. If the user is relaxed, the provider can provide a display that includes detailed information. If the user is in a hurry, the provider can provide a display that gets straight to the point. By adjusting the way information is displayed according to the user's emotions, more appropriate information can be provided. 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.

[0080] The service provider can select the optimal display method by referring to the user's past operation history at the time of provision. For example, the service provider can prioritize providing display methods that the user has previously preferred. The service provider can suggest the optimal display method based on the user's past operation history. The service provider can provide a customized display method based on the user's operation history. In this way, the optimal display method can be provided by referring to the user's past operation history.

[0081] The information provider can customize the displayed information based on the user's current areas of interest at the time of delivery. For example, the provider can prioritize displaying information related to topics the user is currently interested in. The provider can filter out unnecessary information based on the user's current areas of interest. The provider can automatically add new information related to the user's areas of interest. This allows for the provision of more relevant information by customizing the information based on the user's areas of interest.

[0082] The service provider can estimate the user's emotions and prioritize the information to be provided based on those emotions. For example, if the user is stressed, the service provider will prioritize providing only essential information. If the user is relaxed, the service provider can prioritize providing detailed information. If the user is in a hurry, the service provider can prioritize providing essential information in real time. This allows for the priority of important information to be provided 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. If the user is using a tablet, the service provider can provide a display method optimized for a larger screen. If the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This improves visibility by providing the optimal display method based on the user's device information.

[0084] The service provider can analyze the user's social media activity and provide relevant information at the time of delivery. For example, the service provider can prioritize providing information related to topics the user has shown interest in on social media. The service provider can filter out unnecessary information based on the user's social media activity. The service provider can automatically add new information related to the user's social media activity. This allows the service provider to deliver highly relevant information based on the user's social media activity.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is stressed, the frequency of data collection can be reduced, and only important data can be collected. If the user is relaxed, the frequency of data collection can be increased, and more detailed data can be collected. If the user is in a hurry, important data can be collected preferentially in real time. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions.

[0087] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is nervous, it can provide simple and easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, it can provide more appropriate analysis results.

[0088] The information provider can estimate the user's emotions and adjust how the information is displayed based on those emotions. For example, if the user is stressed, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that gets straight to the point can be provided. By adjusting how information is displayed according to the user's emotions, it becomes possible to provide more appropriate information.

[0089] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, it can prioritize collecting data from data sources that the user has frequently collected in the past. It can also optimize the data collected during specific time periods based on the user's past data collection history. By analyzing the user's past data collection patterns, it can propose efficient collection methods. This enables efficient data collection by analyzing past data collection history.

[0090] The data collection unit can filter data based on the user's current areas of interest during collection. For example, it can prioritize collecting data related to topics the user is currently interested in. It can also filter out unnecessary data based on the user's current areas of interest. New data sources related to the user's areas of interest can be automatically added. This allows for the collection of highly relevant data by filtering data based on the user's areas of interest.

[0091] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on important data and a simplified analysis on less important data. The analysis priority can be determined based on the importance of the data. Multiple analysis algorithms can be applied to important data. This allows for efficient analysis by adjusting the level of detail based on the importance of the data.

[0092] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, a natural language processing algorithm can be applied to text data, a statistical analysis algorithm to numerical data, an image analysis algorithm to image data, and a speech analysis algorithm to audio data. The optimal analysis algorithm can be automatically selected according to the data category. This improves analysis accuracy by applying the most suitable analysis algorithm for each data category.

[0093] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, it can prioritize providing the display method that the user has previously preferred. It can suggest the optimal display method based on the user's past operation history. It can provide a customized display method based on the user's operation history. In this way, the optimal display method can be provided by referring to the user's past operation history.

[0094] The information delivery system can customize the displayed content based on the user's current areas of interest at the time of delivery. For example, it can prioritize displaying information related to topics the user is currently interested in. It can also filter out unnecessary information based on the user's current areas of interest. New information related to the user's areas of interest can be automatically added. In this way, by customizing information based on the user's areas of interest, it can provide more relevant information.

[0095] The information delivery unit can estimate the user's emotions and prioritize the information to be delivered based on those emotions. For example, if the user is stressed, only important information will be prioritized. If the user is relaxed, detailed information can be prioritized. If the user is in a hurry, important information can be prioritized and delivered in real time. In this way, by prioritizing information according to the user's emotions, important information can be delivered preferentially.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The data collection unit acquires publicly available data from the web in real time. For example, it can collect data from news sites and social networking services to understand the latest market trends. The data collection unit can also automate data acquisition by utilizing data integration. For example, it can use APIs to retrieve data from databases. Step 2: The analysis unit analyzes the data acquired by the data collection unit and makes trend predictions. The analysis unit uses natural language processing and machine learning to analyze the data. For example, it analyzes the frequency and relevance of specific keywords to predict future market trends. The analysis unit can use machine learning algorithms such as neural networks and support vector machines. Step 3: The service provider visually displays the analysis results obtained by the analysis unit. The service provider displays the analysis results on a visually easy-to-understand dashboard. The service provider can also include a notification function for mobile devices. For example, it can provide information in real time using push notifications or email notifications.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit acquires publicly available data from the web in real time using the camera 42 and communication I / F 44 of the smart device 14, and performs data linkage using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the data using natural language processing and machine learning to predict trends. The data provision unit is implemented in the control unit 46A of the smart device 14, and displays the analysis results on a visually easy-to-understand dashboard and has a notification function to mobile devices. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit acquires publicly available data from the web in real time using the camera 42 and communication I / F 44 of the smart glasses 214, and performs data linkage using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the data using natural language processing and machine learning to predict trends. The data provision unit is implemented in the control unit 46A of the smart glasses 214, and displays the analysis results on a visually easy-to-understand dashboard and has a notification function to mobile devices. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the data collection unit acquires publicly available data from the web in real time using the camera 42 and communication I / F 44 of the headset terminal 314, and performs data linkage using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the data using natural language processing and machine learning to predict trends. The data provision unit is implemented in the control unit 46A of the headset terminal 314, and displays the analysis results on a visually easy-to-understand dashboard and has a notification function to mobile devices. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] 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.

[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 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.

[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 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).

[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] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the data collection unit acquires publicly available data from the web in real time using the camera 42 and communication I / F 44 of the robot 414, and performs data linkage using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the data using natural language processing and machine learning to predict trends. The data provision unit is implemented in the control unit 46A of the robot 414, and displays the analysis results on a visually easy-to-understand dashboard and has a notification function to mobile devices. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] (Note 1) A data collection unit that acquires publicly available data from the web in real time, An analysis unit analyzes the data acquired by the aforementioned collection unit and performs trend prediction, The system includes a providing unit that visually displays the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Utilize data integration to automatically acquire data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We analyze data and predict trends using natural language processing and machine learning. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Display the analysis results on a visually easy-to-understand dashboard. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Features a notification function for mobile devices. The system described in Appendix 1, characterized by the features described herein. (Note 6) 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 7) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) 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 10) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) 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 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing the service, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the service, the displayed information will be customized based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing the service, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0170] 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 unit that acquires publicly available data from the web in real time, An analysis unit analyzes the data acquired by the aforementioned collection unit and performs trend prediction, The system includes a providing unit that visually displays the analysis results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Data integration is used to automatically acquire data. The system according to feature 1.

3. The aforementioned analysis unit, We analyze data and predict trends using natural language processing and machine learning. The system according to feature 1.

4. The aforementioned supply unit is, Display the analysis results on a visually easy-to-understand dashboard. The system according to feature 1.

5. The aforementioned supply unit is, Features a notification function for mobile devices. The system according to feature 1.

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

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

8. The aforementioned collection unit is When collecting data, filtering is performed based on the user's current areas of interest. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data based on the user's geographical location. The system according to feature 1.

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