system

Abstract

The system according to this embodiment aims to analyze business data and quickly provide insights useful for management decisions. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates insights based on the analysis results obtained by the analysis unit. The provision unit provides the insights generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the analysis of business data and the provision of insights based on the results are not performed quickly, which may lead to a delay in business decisions and a decline in competitiveness.

[0005] The system according to the embodiment aims to analyze business data and quickly provide insights useful for business decisions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates insights based on the analysis results obtained by the analysis unit. The provision unit provides the insights generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze business data and quickly provide insights useful for management decisions. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI agent system according to an embodiment of the present invention is a system in which AI analyzes business data in real time and provides insights useful for management decisions. This AI agent system integrates and analyzes diverse data in real time, providing valuable insights for companies. The AI ​​agent system can work in conjunction with BI tools and can also analyze image data. The AI ​​agent system supports corporate decision-making through a chat-based interface that allows for intuitive operation through natural language queries. The AI ​​agent system plays an important role in various companies and industries. For the AI ​​agent system, it is important to clearly define the appropriate target and approach it strategically in order to increase product demand and expand market share. The AI ​​agent system significantly reduces the effort required for data collection, organization, analysis, and presentation, as data analysis is a complex and time-consuming task. The AI ​​agent system can extract meaning from data, thus automating the analysis. This significantly reduces effort and resources. The AI ​​agent system can analyze various types of data using generative AI and generate insights using statistical models. The AI ​​agent system can provide flexible, conversational insights by using a chatbot. The AI ​​agent system allows users to easily obtain insights to their questions when they need them. This enables AI agent systems to help companies make rapid, data-driven decisions and enhance their competitiveness in the market.

[0029] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data. The collection unit can collect, for example, numerical data, text data, image data, etc. The collection unit can also collect image data in cooperation with a BI tool. For example, the collection unit can use a BI tool to collect company performance data and market data. The collection unit can also use image recognition technology to collect image data. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, AI, and extracts patterns and trends. The analysis unit can also analyze the data using statistical models. For example, the analysis unit analyzes the trends in the data using statistical models such as regression analysis and decision trees. The generation unit generates insights based on the analysis results obtained by the analysis unit. The generation unit generates insights using, for example, AI. The generation unit can also generate insights using statistical models. For example, the generation unit generates insights using statistical models such as regression analysis and decision trees. The provision unit provides the insights generated by the generation unit. The service provider delivers insights, for example, through a chat-based interface. The service provider can also deliver interactive insights using a chatbot. For example, the service provider delivers insights when the user inputs queries in natural language. This enables the AI ​​agent system according to the embodiment to collect, analyze, generate, and deliver data.

[0030] The data collection unit collects data. For example, the data collection unit can collect numerical data, text data, and image data. Specifically, numerical data includes company sales data and market growth rates, which are obtained through BI tools. Text data includes news articles, social media posts, and company reports, which are collected using web scraping techniques and APIs. Image data includes product photos and graphs showing market trends, which are collected using image recognition technology. The data collection unit can also collect image data in conjunction with BI tools. For example, it can use BI tools to collect company performance data and market data. BI tools have the ability to connect to company databases and external data sources and automatically acquire the necessary data. The data collection unit can also use image recognition technology to collect image data. Image recognition technology identifies specific objects and patterns within images and extracts them as data. For example, it can identify brand logos from product photos or extract important data points from graphs showing market trends. This allows the data collection unit to efficiently collect a wide range of data from diverse data sources and strengthen the data infrastructure of the entire system. Furthermore, the data collection unit can flexibly adapt to specific situations and conditions by adjusting the frequency and accuracy of data collection. For example, if real-time data collection is required, the data collection frequency can be increased, while conversely, for data collection for periodic report generation, the collection frequency can be set lower. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes the data collected by the data collection department. For example, the analysis department uses AI to analyze the data and extract patterns and trends. Specifically, it uses machine learning algorithms to discover hidden patterns and correlations in the data. For example, it can use clustering algorithms to identify customer segments or anomaly detection algorithms to detect fraudulent activity or anomalous data points. The analysis department can also analyze data using statistical models. For example, it can use statistical models such as regression analysis and decision trees to analyze data trends. Regression analysis can model the relationships between variables and predict future values. Decision trees can hierarchically divide data and clarify the rules for decision-making. This allows the analysis department to understand the mechanisms behind the data and gain concrete insights. Furthermore, the analysis department can also analyze text data using natural language processing techniques. For example, it can perform sentiment analysis on text data to extract positive and negative opinions from customer feedback and social media posts. It can also use topic modeling to identify key topics and themes from large amounts of text data. This allows the analysis department to analyze the collected data from multiple perspectives and gain deep insights. Furthermore, the analytics department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict the future growth of specific markets or products based on past performance data, supporting strategic decision-making. It can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. As a result, the analytics department can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0032] The generation unit generates insights based on the analysis results obtained by the analysis unit. The generation unit can generate insights using, for example, AI. Specifically, it uses natural language generation technology to convert analysis results into easy-to-understand text and reports. For example, it can automatically generate reports on sales forecasts and market trends based on the output of machine learning models. The generation unit can also generate insights using statistical models. For example, it can generate insights using statistical models such as regression analysis and decision trees. Regression analysis can predict future sales and market share fluctuations based on predicted values. Decision trees can clarify decision-making rules and propose concrete action plans based on hierarchical data division. This allows the generation unit to convert analysis results into concrete insights and provide them to users. Furthermore, the generation unit can visually represent insights using visualization technology. For example, it can use graphs and charts to visually show data trends and patterns, enabling users to understand them intuitively. It can also create dashboards and provide insights that are updated in real time. This allows the generation unit to help users make data-driven decisions quickly. Furthermore, the generation unit can collect user feedback and continuously improve the accuracy and usefulness of the generated insights. For example, by users providing ratings and comments on the insights they offer, the generation unit can use that feedback to improve its insight generation algorithms and models. This allows the generation unit to consistently provide high-quality insights based on the latest information and user needs, thereby improving the overall value of the system.

[0033] The Provider unit provides insights generated by the Generator unit. The Provider unit provides insights, for example, through a chat-based interface. Specifically, it provides insights when users enter queries in natural language. The Provider unit can also provide interactive insights using a chatbot. The chatbot can provide relevant insights in real time in response to user queries and provide additional information and details through interaction with the user. For example, if a user enters "What is the sales forecast for this month?", the chatbot will present specific numbers and graphs based on sales forecast data obtained from the Generator unit. Also, if a user asks "What are the market trends?", the chatbot will provide key trends and forecasts based on the analysis of market data. This allows the Provider unit to quickly and easily obtain the information users need. Furthermore, the Provider unit can deliver insights via email and notifications. For example, it can automatically generate periodic reports and send them to users' inboxes. It can also provide real-time notifications of important insights and alerts, enabling users to respond quickly. This allows the Provider unit to help users stay up-to-date and make informed decisions. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and usefulness of the insights provided. For example, users can rate and comment on the insights provided, allowing the service provider to improve the methods and content of insight delivery based on that feedback. This enables the service provider to consistently deliver high-quality insights that meet user needs and improve the overall value of the system.

[0034] The data collection unit can collect image data in conjunction with BI tools. For example, the data collection unit can use BI tools to collect company performance data and market data. The data collection unit can also use image recognition technology to collect image data. For example, the data collection unit can use BI tools to collect company performance data and market data. The data collection unit can also use image recognition technology to collect image data. This makes it possible to collect image data by linking with BI tools. BI tools include, but are not limited to, Tableau and Power BI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data collected using BI tools into a generating AI and have the generating AI perform image data analysis.

[0035] The service provider can provide insights through a chat-based interface. For example, the service provider can provide interactive insights using a chatbot. The service provider can also provide insights by allowing users to input queries in natural language. For example, the service provider can provide insights by allowing users to input queries in natural language. This enables intuitive operation by providing insights through a chat-based interface. Chat-based interfaces include, but are not limited to, chatbots and messaging apps. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input a user query into a generating AI and have the generating AI perform the generation of insights.

[0036] The generation unit can generate insights using statistical models. For example, the generation unit generates insights using statistical models such as regression analysis and decision trees. The generation unit can also generate insights using AI. For example, the generation unit generates insights using statistical models such as regression analysis and decision trees. This improves the accuracy of the insights by using statistical models. Statistical models include, but are not limited to, regression analysis and decision trees. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input a statistical model into a generation AI and have the generation AI perform the generation of insights.

[0037] The service provider can provide interactive insights using a chatbot. For example, the service provider can provide interactive insights using a chatbot. The service provider can also provide insights when the user enters queries in natural language. For example, the service provider can provide insights when the user enters queries in natural language. This makes it possible to provide interactive insights using a chatbot. Chatbots include, but are not limited to, conversational AI and FAQ bots. Some or all of the above-described processes in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input a user query into a generating AI and have the generating AI generate insights.

[0038] The data collection unit can dynamically change the types of data it collects based on a company's current business challenges. For example, when a company launches a new product, the data collection unit can prioritize collecting marketing data. If a company aims to reduce costs, the data collection unit can also focus on collecting financial data. If a company aims to improve customer satisfaction, the data collection unit can also collect customer feedback data. This allows for appropriate data collection by changing the data collected according to the company's business challenges. Current business challenges of a company include, but are not limited to, declining sales or declining customer satisfaction. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the company's business challenge data into a generating AI and have the generating AI change the types of data collected.

[0039] The data collection unit can analyze patterns in past collected data and select the optimal collection method during data collection. For example, the data collection unit can analyze past data collection history and select the most efficient collection method. Based on past data collection patterns, the data collection unit can also optimize the collection frequency and timing. The data collection unit can also determine the priority of data to be collected by referring to past data collection results. This allows for the selection of the optimal collection method by analyzing past data collection patterns. The optimal collection method includes, but is not limited to, collection frequency and collection means. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past collected data into a generating AI and have the generating AI select the optimal collection method.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of companies during data collection. For example, the data collection unit can collect region-specific market data based on the company's location. The data collection unit can also collect data on competitors based on the company's geographical location information. The data collection unit can also collect data on regional economic conditions by considering the company's geographical location information. This allows for the priority collection of highly relevant data by considering the company's geographical location information. The company's geographical location information includes, but is not limited to, countries, regions, and cities. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the company's geographical location information into a generating AI and have the generating AI select highly relevant data.

[0041] The data collection unit can analyze a company's social media activities and collect relevant data during data collection. For example, the data collection unit can analyze a company's social media posts and collect relevant customer feedback data. The data collection unit can also collect marketing data based on a company's social media activities. The data collection unit can also analyze a company's social media activities and collect data on competitors. This allows for the collection of relevant data by analyzing a company's social media activities. Social media activities include, but are not limited to, posts and engagement. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input a company's social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on high-importance data, and a simplified analysis on low-importance data. The analysis unit can also determine the priority of the analysis based on the importance of the data. This allows for efficient analysis by adjusting the level of detail according to the importance of the data. Data importance includes, but is not limited to, business impact and data reliability. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input data importance into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a financial analysis algorithm to financial data. The analysis unit can also apply a marketing analysis algorithm to marketing data. The analysis unit can also apply a text mining algorithm to customer feedback data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Data categories include, but are not limited to, sales data and customer data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI apply the appropriate analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the data collection period during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can also analyze current data while referring to past data. The analysis unit can also adjust the priority of analysis according to the data collection period. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection period. The data collection period includes, but is not limited to, monthly data or quarterly data. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI perform the determination of the analysis priority.

[0045] 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. The analysis unit can also postpone the analysis of less relevant data. The analysis unit can also optimize the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. The relevance of the data includes, but is not limited to, correlation and causation. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI adjust the order of analysis.

[0046] The generation unit can analyze a company's past management decisions to select the optimal insight generation method when generating insights. For example, the generation unit can generate insights by referring to a company's past success stories. The generation unit can also analyze a company's past failure stories and generate insights that include areas for improvement. The generation unit can also select the optimal insight generation method based on a company's past management decisions. This allows the optimal insight generation method to be selected by analyzing a company's past management decisions. A company's past management decisions include, but are not limited to, investment decisions and strategic decisions. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input data on a company's past management decisions into a generation AI and have the generation AI select the optimal insight generation method.

[0047] The generation unit can customize the content of insights based on the company's current business situation when generating insights. For example, the generation unit can customize financial insights based on the company's current financial situation. The generation unit can also customize marketing insights based on the company's current marketing strategy. The generation unit can also customize customer insights based on the company's current customer satisfaction. This allows the generation unit to provide appropriate insights by customizing the content of insights based on the company's current business situation. The company's current business situation includes, but is not limited to, financial situation and market share. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the company's current business situation into a generation AI and have the generation AI perform the customization of the insight content.

[0048] The generation unit can generate optimal insights by considering the geographical location information of a company when generating insights. For example, the generation unit can generate region-specific insights based on the company's location. The generation unit can also generate insights into competitors based on the company's geographical location information. The generation unit can also generate insights into the regional economic situation by considering the company's geographical location information. This allows for the provision of region-specific insights by considering the company's geographical location information. The company's geographical location information includes, but is not limited to, countries, regions, and cities. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the company's geographical location information into a generation AI and have the generation AI perform the generation of optimal insights.

[0049] The generation unit can analyze a company's social media activities and propose insights when generating insights. For example, the generation unit can analyze a company's social media posts and generate relevant insights. The generation unit can also generate marketing insights based on a company's social media activities. The generation unit can also analyze a company's social media activities and generate insights into competitors. This allows the generation unit to provide relevant insights by analyzing a company's social media activities. Social media activities include, but are not limited to, posts and engagement. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input a company's social media data into a generation AI and have the generation AI propose insights.

[0050] The service provider can select the optimal delivery method by referring to a company's past insight usage history when providing insights. For example, the service provider can select the optimal delivery method based on the insight delivery methods the company has used in the past. The service provider can also analyze a company's past insight usage history and select an effective delivery method. The service provider can also customize the delivery method by referring to a company's past insight usage history. This allows the service provider to select the optimal delivery method by referring to a company's past insight usage history. A company's past insight usage history includes, but is not limited to, past report viewing history and feedback history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input a company's past insight usage history data into a generating AI and have the generating AI select the optimal delivery method.

[0051] The service provider can customize the means of delivering insights based on the company's current business situation when providing insights. For example, the service provider can provide financial insights based on the company's current financial situation. The service provider can also provide marketing insights based on the company's current marketing strategy. The service provider can also provide customer insights based on the company's current customer satisfaction. This allows the service provider to deliver appropriate insights by customizing the means of delivering insights based on the company's current business situation. The company's current business situation includes, but is not limited to, financial situation and market share. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the company's current business situation into a generating AI and have the generating AI customize the means of delivering insights.

[0052] The service provider can select the optimal method of providing insights by considering the geographical location information of the company when providing insights. For example, the service provider can provide region-specific insights based on the company's location. The service provider can also provide insights into competitors based on the company's geographical location information. The service provider can also provide insights into the regional economic situation by considering the company's geographical location information. This allows for the provision of region-specific insights by considering the company's geographical location information. The company's geographical location information includes, but is not limited to, countries, regions, and cities. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the company's geographical location information into a generating AI and have the generating AI select the optimal method of providing insights.

[0053] The service provider can analyze a company's social media activities and propose methods for providing insights when delivering those insights. For example, the service provider can analyze a company's social media posts and provide relevant insights. The service provider can also provide marketing insights based on a company's social media activities. The service provider can also analyze a company's social media activities and provide insights into competitors. This allows the service provider to provide relevant insights by analyzing a company's social media activities. Social media activities include, but are not limited to, posts and engagement. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input a company's social media data into a generating AI and have the generating AI propose methods for delivering insights.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The data collection unit can dynamically change the types of data it collects based on a company's current business challenges. For example, when a company launches a new product, the data collection unit can prioritize collecting marketing data. If a company aims to reduce costs, the data collection unit can also focus on collecting financial data. If a company aims to improve customer satisfaction, the data collection unit can also collect customer feedback data. This allows for appropriate data collection by changing the data collected according to the company's business challenges. Current business challenges of a company include, but are not limited to, declining sales or declining customer satisfaction. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the company's business challenge data into a generating AI and have the generating AI change the types of data collected.

[0056] The data collection unit can analyze patterns in past collected data and select the optimal collection method during data collection. For example, the data collection unit can analyze past data collection history and select the most efficient collection method. Based on past data collection patterns, the data collection unit can also optimize the collection frequency and timing. The data collection unit can also determine the priority of data to be collected by referring to past data collection results. This allows for the selection of the optimal collection method by analyzing past data collection patterns. The optimal collection method includes, but is not limited to, collection frequency and collection means. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past collected data into a generating AI and have the generating AI select the optimal collection method.

[0057] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of companies during data collection. For example, the data collection unit can collect region-specific market data based on the company's location. The data collection unit can also collect data on competitors based on the company's geographical location information. The data collection unit can also collect data on regional economic conditions by considering the company's geographical location information. This allows for the priority collection of highly relevant data by considering the company's geographical location information. The company's geographical location information includes, but is not limited to, countries, regions, and cities. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the company's geographical location information into a generating AI and have the generating AI select highly relevant data.

[0058] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on high-importance data, and a simplified analysis on low-importance data. The analysis unit can also determine the priority of the analysis based on the importance of the data. This allows for efficient analysis by adjusting the level of detail according to the importance of the data. Data importance includes, but is not limited to, business impact and data reliability. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input data importance into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0059] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a financial analysis algorithm to financial data. The analysis unit can also apply a marketing analysis algorithm to marketing data. The analysis unit can also apply a text mining algorithm to customer feedback data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Data categories include, but are not limited to, sales data and customer data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI apply the appropriate analysis algorithm.

[0060] The analysis unit can determine the priority of analysis based on the data collection period during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can also analyze current data while referring to past data. The analysis unit can also adjust the priority of analysis according to the data collection period. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection period. The data collection period includes, but is not limited to, monthly data or quarterly data. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI perform the determination of the analysis priority.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects data. The data collection unit can collect numerical data, text data, image data, etc. The data collection unit can also work in conjunction with BI tools to collect company performance data and market data. It can also collect image data using image recognition technology. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis unit uses AI to analyze the data and extract patterns and trends. Furthermore, it can also analyze the data trends using statistical models, such as regression analysis and decision trees. Step 3: The generation unit generates insights based on the analysis results obtained by the analysis unit. The generation unit can generate insights using AI, and it can also generate insights using statistical models, such as regression analysis and decision trees. Step 4: The provider delivers the insights generated by the generator. The provider can deliver insights through a chat-based interface, and can also deliver interactive insights using a chatbot. Insights can be provided by users entering queries in natural language.

[0063] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system in which AI analyzes business data in real time and provides insights useful for management decisions. This AI agent system integrates and analyzes diverse data in real time, providing valuable insights for companies. The AI ​​agent system can work in conjunction with BI tools and can also analyze image data. The AI ​​agent system supports corporate decision-making through a chat-based interface that allows for intuitive operation through natural language queries. The AI ​​agent system plays an important role in various companies and industries. For the AI ​​agent system, it is important to clearly define the appropriate target and approach it strategically in order to increase product demand and expand market share. The AI ​​agent system significantly reduces the effort required for data collection, organization, analysis, and presentation, as data analysis is a complex and time-consuming task. The AI ​​agent system can extract meaning from data, thus automating the analysis. This significantly reduces effort and resources. The AI ​​agent system can analyze various types of data using generative AI and generate insights using statistical models. The AI ​​agent system can provide flexible, conversational insights by using a chatbot. The AI ​​agent system allows users to easily obtain insights to their questions when they need them. This enables AI agent systems to help companies make rapid, data-driven decisions and enhance their competitiveness in the market.

[0064] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data. The collection unit can collect, for example, numerical data, text data, image data, etc. The collection unit can also collect image data in cooperation with a BI tool. For example, the collection unit can use a BI tool to collect company performance data and market data. The collection unit can also use image recognition technology to collect image data. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, AI, and extracts patterns and trends. The analysis unit can also analyze the data using statistical models. For example, the analysis unit analyzes the trends in the data using statistical models such as regression analysis and decision trees. The generation unit generates insights based on the analysis results obtained by the analysis unit. The generation unit generates insights using, for example, AI. The generation unit can also generate insights using statistical models. For example, the generation unit generates insights using statistical models such as regression analysis and decision trees. The provision unit provides the insights generated by the generation unit. The service provider delivers insights, for example, through a chat-based interface. The service provider can also deliver interactive insights using a chatbot. For example, the service provider delivers insights when the user inputs queries in natural language. This enables the AI ​​agent system according to the embodiment to collect, analyze, generate, and deliver data.

[0065] The data collection unit collects data. For example, the data collection unit can collect numerical data, text data, and image data. Specifically, numerical data includes company sales data and market growth rates, which are obtained through BI tools. Text data includes news articles, social media posts, and company reports, which are collected using web scraping techniques and APIs. Image data includes product photos and graphs showing market trends, which are collected using image recognition technology. The data collection unit can also collect image data in conjunction with BI tools. For example, it can use BI tools to collect company performance data and market data. BI tools have the ability to connect to company databases and external data sources and automatically acquire the necessary data. The data collection unit can also use image recognition technology to collect image data. Image recognition technology identifies specific objects and patterns within images and extracts them as data. For example, it can identify brand logos from product photos or extract important data points from graphs showing market trends. This allows the data collection unit to efficiently collect a wide range of data from diverse data sources and strengthen the data infrastructure of the entire system. Furthermore, the data collection unit can flexibly adapt to specific situations and conditions by adjusting the frequency and accuracy of data collection. For example, if real-time data collection is required, the data collection frequency can be increased, while conversely, for data collection for periodic report generation, the collection frequency can be set lower. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0066] The analysis department analyzes the data collected by the data collection department. For example, the analysis department uses AI to analyze the data and extract patterns and trends. Specifically, it uses machine learning algorithms to discover hidden patterns and correlations in the data. For example, it can use clustering algorithms to identify customer segments or anomaly detection algorithms to detect fraudulent activity or anomalous data points. The analysis department can also analyze data using statistical models. For example, it can use statistical models such as regression analysis and decision trees to analyze data trends. Regression analysis can model the relationships between variables and predict future values. Decision trees can hierarchically divide data and clarify the rules for decision-making. This allows the analysis department to understand the mechanisms behind the data and gain concrete insights. Furthermore, the analysis department can also analyze text data using natural language processing techniques. For example, it can perform sentiment analysis on text data to extract positive and negative opinions from customer feedback and social media posts. It can also use topic modeling to identify key topics and themes from large amounts of text data. This allows the analysis department to analyze the collected data from multiple perspectives and gain deep insights. Furthermore, the analytics department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict the future growth of specific markets or products based on past performance data, supporting strategic decision-making. It can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. As a result, the analytics department can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0067] The generation unit generates insights based on the analysis results obtained by the analysis unit. The generation unit can generate insights using, for example, AI. Specifically, it uses natural language generation technology to convert analysis results into easy-to-understand text and reports. For example, it can automatically generate reports on sales forecasts and market trends based on the output of machine learning models. The generation unit can also generate insights using statistical models. For example, it can generate insights using statistical models such as regression analysis and decision trees. Regression analysis can predict future sales and market share fluctuations based on predicted values. Decision trees can clarify decision-making rules and propose concrete action plans based on hierarchical data division. This allows the generation unit to convert analysis results into concrete insights and provide them to users. Furthermore, the generation unit can visually represent insights using visualization technology. For example, it can use graphs and charts to visually show data trends and patterns, enabling users to understand them intuitively. It can also create dashboards and provide insights that are updated in real time. This allows the generation unit to help users make data-driven decisions quickly. Furthermore, the generation unit can collect user feedback and continuously improve the accuracy and usefulness of the generated insights. For example, by users providing ratings and comments on the insights they offer, the generation unit can use that feedback to improve its insight generation algorithms and models. This allows the generation unit to consistently provide high-quality insights based on the latest information and user needs, thereby improving the overall value of the system.

[0068] The Provider unit provides insights generated by the Generator unit. The Provider unit provides insights, for example, through a chat-based interface. Specifically, it provides insights when users enter queries in natural language. The Provider unit can also provide interactive insights using a chatbot. The chatbot can provide relevant insights in real time in response to user queries and provide additional information and details through interaction with the user. For example, if a user enters "What is the sales forecast for this month?", the chatbot will present specific numbers and graphs based on sales forecast data obtained from the Generator unit. Also, if a user asks "What are the market trends?", the chatbot will provide key trends and forecasts based on the analysis of market data. This allows the Provider unit to quickly and easily obtain the information users need. Furthermore, the Provider unit can deliver insights via email and notifications. For example, it can automatically generate periodic reports and send them to users' inboxes. It can also provide real-time notifications of important insights and alerts, enabling users to respond quickly. This allows the Provider unit to help users stay up-to-date and make informed decisions. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and usefulness of the insights provided. For example, users can rate and comment on the insights provided, allowing the service provider to improve the methods and content of insight delivery based on that feedback. This enables the service provider to consistently deliver high-quality insights that meet user needs and improve the overall value of the system.

[0069] The data collection unit can collect image data in conjunction with BI tools. For example, the data collection unit can use BI tools to collect company performance data and market data. The data collection unit can also use image recognition technology to collect image data. For example, the data collection unit can use BI tools to collect company performance data and market data. The data collection unit can also use image recognition technology to collect image data. This makes it possible to collect image data by linking with BI tools. BI tools include, but are not limited to, Tableau and Power BI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data collected using BI tools into a generating AI and have the generating AI perform image data analysis.

[0070] The service provider can provide insights through a chat-based interface. For example, the service provider can provide interactive insights using a chatbot. The service provider can also provide insights by allowing users to input queries in natural language. For example, the service provider can provide insights by allowing users to input queries in natural language. This enables intuitive operation by providing insights through a chat-based interface. Chat-based interfaces include, but are not limited to, chatbots and messaging apps. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input a user query into a generating AI and have the generating AI perform the generation of insights.

[0071] The generation unit can generate insights using statistical models. For example, the generation unit generates insights using statistical models such as regression analysis and decision trees. The generation unit can also generate insights using AI. For example, the generation unit generates insights using statistical models such as regression analysis and decision trees. This improves the accuracy of the insights by using statistical models. Statistical models include, but are not limited to, regression analysis and decision trees. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input a statistical model into a generation AI and have the generation AI perform the generation of insights.

[0072] The service provider can provide interactive insights using a chatbot. For example, the service provider can provide interactive insights using a chatbot. The service provider can also provide insights when the user enters queries in natural language. For example, the service provider can provide insights when the user enters queries in natural language. This makes it possible to provide interactive insights using a chatbot. Chatbots include, but are not limited to, conversational AI and FAQ bots. Some or all of the above-described processes in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input a user query into a generating AI and have the generating AI generate insights.

[0073] 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 to alleviate the burden. If the user is relaxed, the data collection unit can also increase the frequency of data collection to collect more detailed data. If the user is in a hurry, the data collection unit can prioritize collecting only important data. This reduces the burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0074] The data collection unit can dynamically change the types of data it collects based on a company's current business challenges. For example, when a company launches a new product, the data collection unit can prioritize collecting marketing data. If a company aims to reduce costs, the data collection unit can also focus on collecting financial data. If a company aims to improve customer satisfaction, the data collection unit can also collect customer feedback data. This allows for appropriate data collection by changing the data collected according to the company's business challenges. Current business challenges of a company include, but are not limited to, declining sales or declining customer satisfaction. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the company's business challenge data into a generating AI and have the generating AI change the types of data collected.

[0075] The data collection unit can analyze patterns in past collected data and select the optimal collection method during data collection. For example, the data collection unit can analyze past data collection history and select the most efficient collection method. Based on past data collection patterns, the data collection unit can also optimize the collection frequency and timing. The data collection unit can also determine the priority of data to be collected by referring to past data collection results. This allows for the selection of the optimal collection method by analyzing past data collection patterns. The optimal collection method includes, but is not limited to, collection frequency and collection means. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past collected data into a generating AI and have the generating AI select the optimal collection method.

[0076] The data collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting only high-priority data. If the user is relaxed, the data collection unit can also collect detailed data. If the user is in a hurry, the data collection unit can also prioritize data that can be collected quickly. 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, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0077] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of companies during data collection. For example, the data collection unit can collect region-specific market data based on the company's location. The data collection unit can also collect data on competitors based on the company's geographical location information. The data collection unit can also collect data on regional economic conditions by considering the company's geographical location information. This allows for the priority collection of highly relevant data by considering the company's geographical location information. The company's geographical location information includes, but is not limited to, countries, regions, and cities. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the company's geographical location information into a generating AI and have the generating AI select highly relevant data.

[0078] The data collection unit can analyze a company's social media activities and collect relevant data during data collection. For example, the data collection unit can analyze a company's social media posts and collect relevant customer feedback data. The data collection unit can also collect marketing data based on a company's social media activities. The data collection unit can also analyze a company's social media activities and collect data on competitors. This allows for the collection of relevant data by analyzing a company's social media activities. Social media activities include, but are not limited to, posts and engagement. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input a company's social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0079] 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 stressed, the analysis unit can provide a simple and easy-to-understand analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is in a hurry, the analysis unit can also provide a concise analysis result that gets straight to the point. This allows for the provision of highly visual analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on high-importance data, and a simplified analysis on low-importance data. The analysis unit can also determine the priority of the analysis based on the importance of the data. This allows for efficient analysis by adjusting the level of detail according to the importance of the data. Data importance includes, but is not limited to, business impact and data reliability. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input data importance into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0081] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a financial analysis algorithm to financial data. The analysis unit can also apply a marketing analysis algorithm to marketing data. The analysis unit can also apply a text mining algorithm to customer feedback data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Data categories include, but are not limited to, sales data and customer data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI apply the appropriate analysis algorithm.

[0082] 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 stressed, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. If the user is in a hurry, the analysis unit can provide a concise analysis for quick understanding. This allows for the provision of analysis results that can be quickly understood by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The analysis unit can determine the priority of analysis based on the data collection period during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can also analyze current data while referring to past data. The analysis unit can also adjust the priority of analysis according to the data collection period. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection period. The data collection period includes, but is not limited to, monthly data or quarterly data. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI perform the determination of the analysis priority.

[0084] 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. The analysis unit can also postpone the analysis of less relevant data. The analysis unit can also optimize the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. The relevance of the data includes, but is not limited to, correlation and causation. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI adjust the order of analysis.

[0085] The generation unit can estimate the user's emotions and adjust the method of generating insights based on the estimated user emotions. For example, if the user is stressed, the generation unit can generate simple and highly visible insights. If the user is relaxed, the generation unit can also generate detailed insights. If the user is in a hurry, the generation unit can also generate concise and to-the-point insights. This allows for the provision of highly visible insights by adjusting the method of generating insights according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0086] The generation unit can analyze a company's past management decisions to select the optimal insight generation method when generating insights. For example, the generation unit can generate insights by referring to a company's past success stories. The generation unit can also analyze a company's past failure stories and generate insights that include areas for improvement. The generation unit can also select the optimal insight generation method based on a company's past management decisions. This allows the optimal insight generation method to be selected by analyzing a company's past management decisions. A company's past management decisions include, but are not limited to, investment decisions and strategic decisions. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input data on a company's past management decisions into a generation AI and have the generation AI select the optimal insight generation method.

[0087] The generation unit can customize the content of insights based on the company's current business situation when generating insights. For example, the generation unit can customize financial insights based on the company's current financial situation. The generation unit can also customize marketing insights based on the company's current marketing strategy. The generation unit can also customize customer insights based on the company's current customer satisfaction. This allows the generation unit to provide appropriate insights by customizing the content of insights based on the company's current business situation. The company's current business situation includes, but is not limited to, financial situation and market share. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the company's current business situation into a generation AI and have the generation AI perform the customization of the insight content.

[0088] The generation unit can estimate the user's emotions and prioritize insights based on the estimated emotions. For example, if the user is stressed, the generation unit will prioritize providing high-priority insights. If the user is relaxed, the generation unit may also provide detailed insights. If the user is in a hurry, the generation unit may also prioritize insights that can be quickly understood. This allows for the prioritization of important insights by determining the priority of insights according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0089] The generation unit can generate optimal insights by considering the geographical location information of a company when generating insights. For example, the generation unit can generate region-specific insights based on the company's location. The generation unit can also generate insights into competitors based on the company's geographical location information. The generation unit can also generate insights into the regional economic situation by considering the company's geographical location information. This allows for the provision of region-specific insights by considering the company's geographical location information. The company's geographical location information includes, but is not limited to, countries, regions, and cities. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the company's geographical location information into a generation AI and have the generation AI perform the generation of optimal insights.

[0090] The generation unit can analyze a company's social media activities and propose insights when generating insights. For example, the generation unit can analyze a company's social media posts and generate relevant insights. The generation unit can also generate marketing insights based on a company's social media activities. The generation unit can also analyze a company's social media activities and generate insights into competitors. This allows the generation unit to provide relevant insights by analyzing a company's social media activities. Social media activities include, but are not limited to, posts and engagement. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input a company's social media data into a generation AI and have the generation AI propose insights.

[0091] The service provider can estimate the user's emotions and adjust how insights are delivered based on the estimated emotions. For example, if the user is stressed, the service provider can provide simple, easy-to-understand insights. If the user is relaxed, the service provider can also provide detailed insights. If the user is in a hurry, the service provider can provide concise, to-the-point insights. This allows for the delivery of highly visible insights by adjusting how insights are delivered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0092] The service provider can select the optimal delivery method by referring to a company's past insight usage history when providing insights. For example, the service provider can select the optimal delivery method based on the insight delivery methods the company has used in the past. The service provider can also analyze a company's past insight usage history and select an effective delivery method. The service provider can also customize the delivery method by referring to a company's past insight usage history. This allows the service provider to select the optimal delivery method by referring to a company's past insight usage history. A company's past insight usage history includes, but is not limited to, past report viewing history and feedback history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input a company's past insight usage history data into a generating AI and have the generating AI select the optimal delivery method.

[0093] The service provider can customize the means of delivering insights based on the company's current business situation when providing insights. For example, the service provider can provide financial insights based on the company's current financial situation. The service provider can also provide marketing insights based on the company's current marketing strategy. The service provider can also provide customer insights based on the company's current customer satisfaction. This allows the service provider to deliver appropriate insights by customizing the means of delivering insights based on the company's current business situation. The company's current business situation includes, but is not limited to, financial situation and market share. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the company's current business situation into a generating AI and have the generating AI customize the means of delivering insights.

[0094] The service provider can estimate the user's emotions and determine the priority of insights based on the estimated emotions. For example, if the user is stressed, the service provider will prioritize providing high-priority insights. If the user is relaxed, the service provider may also provide detailed insights. If the user is in a hurry, the service provider may also prioritize insights that can be quickly understood. This allows for the priority of providing important insights by determining the priority of insights according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0095] The service provider can select the optimal method of providing insights by considering the geographical location information of the company when providing insights. For example, the service provider can provide region-specific insights based on the company's location. The service provider can also provide insights into competitors based on the company's geographical location information. The service provider can also provide insights into the regional economic situation by considering the company's geographical location information. This allows for the provision of region-specific insights by considering the company's geographical location information. The company's geographical location information includes, but is not limited to, countries, regions, and cities. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the company's geographical location information into a generating AI and have the generating AI select the optimal method of providing insights.

[0096] The service provider can analyze a company's social media activities and propose methods for providing insights when delivering those insights. For example, the service provider can analyze a company's social media posts and provide relevant insights. The service provider can also provide marketing insights based on a company's social media activities. The service provider can also analyze a company's social media activities and provide insights into competitors. This allows the service provider to provide relevant insights by analyzing a company's social media activities. Social media activities include, but are not limited to, posts and engagement. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input a company's social media data into a generating AI and have the generating AI propose methods for delivering insights.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] 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 to alleviate the burden. If the user is relaxed, the data collection unit can also increase the frequency of data collection to collect more detailed data. If the user is in a hurry, the data collection unit can prioritize collecting only important data. This reduces the burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0099] The data collection unit can dynamically change the types of data it collects based on a company's current business challenges. For example, when a company launches a new product, the data collection unit can prioritize collecting marketing data. If a company aims to reduce costs, the data collection unit can also focus on collecting financial data. If a company aims to improve customer satisfaction, the data collection unit can also collect customer feedback data. This allows for appropriate data collection by changing the data collected according to the company's business challenges. Current business challenges of a company include, but are not limited to, declining sales or declining customer satisfaction. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the company's business challenge data into a generating AI and have the generating AI change the types of data collected.

[0100] The data collection unit can analyze patterns in past collected data and select the optimal collection method during data collection. For example, the data collection unit can analyze past data collection history and select the most efficient collection method. Based on past data collection patterns, the data collection unit can also optimize the collection frequency and timing. The data collection unit can also determine the priority of data to be collected by referring to past data collection results. This allows for the selection of the optimal collection method by analyzing past data collection patterns. The optimal collection method includes, but is not limited to, collection frequency and collection means. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past collected data into a generating AI and have the generating AI select the optimal collection method.

[0101] The data collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting only high-priority data. If the user is relaxed, the data collection unit can also collect detailed data. If the user is in a hurry, the data collection unit can also prioritize data that can be collected quickly. 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, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0102] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of companies during data collection. For example, the data collection unit can collect region-specific market data based on the company's location. The data collection unit can also collect data on competitors based on the company's geographical location information. The data collection unit can also collect data on regional economic conditions by considering the company's geographical location information. This allows for the priority collection of highly relevant data by considering the company's geographical location information. The company's geographical location information includes, but is not limited to, countries, regions, and cities. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the company's geographical location information into a generating AI and have the generating AI select highly relevant data.

[0103] 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 stressed, the analysis unit can provide a simple and easy-to-understand analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is in a hurry, the analysis unit can also provide a concise analysis result that gets straight to the point. This allows for the provision of highly visual analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0104] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on high-importance data, and a simplified analysis on low-importance data. The analysis unit can also determine the priority of the analysis based on the importance of the data. This allows for efficient analysis by adjusting the level of detail according to the importance of the data. Data importance includes, but is not limited to, business impact and data reliability. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input data importance into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0105] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a financial analysis algorithm to financial data. The analysis unit can also apply a marketing analysis algorithm to marketing data. The analysis unit can also apply a text mining algorithm to customer feedback data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Data categories include, but are not limited to, sales data and customer data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI apply the appropriate analysis algorithm.

[0106] 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 stressed, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. If the user is in a hurry, the analysis unit can provide a concise analysis for quick understanding. This allows for the provision of analysis results that can be quickly understood by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0107] The analysis unit can determine the priority of analysis based on the data collection period during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can also analyze current data while referring to past data. The analysis unit can also adjust the priority of analysis according to the data collection period. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection period. The data collection period includes, but is not limited to, monthly data or quarterly data. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI perform the determination of the analysis priority.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The data collection unit collects data. The data collection unit can collect numerical data, text data, image data, etc. The data collection unit can also work in conjunction with BI tools to collect company performance data and market data. It can also collect image data using image recognition technology. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis unit uses AI to analyze the data and extract patterns and trends. Furthermore, it can also analyze the data trends using statistical models, such as regression analysis and decision trees. Step 3: The generation unit generates insights based on the analysis results obtained by the analysis unit. The generation unit can generate insights using AI, and it can also generate insights using statistical models, such as regression analysis and decision trees. Step 4: The provider delivers the insights generated by the generator. The provider can deliver insights through a chat-based interface, and can also deliver interactive insights using a chatbot. Insights can be provided by users entering queries in natural language.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects data using the camera 42 and communication I / F 44 of the smart device 14 and integrates the data 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 AI. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates insights using a statistical model. The provision unit is implemented in the control unit 46A of the smart device 14 and provides insights through a chat-based interface. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects data using the camera 42 and communication I / F 44 of the smart glasses 214 and integrates the data using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and analyzes the data using AI. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and generates insights using a statistical model. The provision unit is implemented, for example, in the control unit 46A of the smart glasses 214 and provides insights through a chat-based interface. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects data using the camera 42 and communication I / F 44 of the headset terminal 314 and integrates the data 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 AI. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates insights using a statistical model. The provision unit is implemented in the control unit 46A of the headset terminal 314 and provides insights through a chat-based interface. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects data using the camera 42 and communication I / F 44 of the robot 414 and integrates the data using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the data using AI. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates insights using a statistical model. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides insights through a chat-based interface. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates insights based on the analysis results obtained by the analysis unit, A providing unit that provides insights generated by the generation unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is Integrate with BI tools to collect image data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Providing insights through a chat-based interface. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generating insights using statistical models The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Use a chatbot to provide interactive insights. 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 The types of data collected are dynamically changed based on the company's current business challenges. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, the patterns of past collected data are analyzed to select the optimal collection method. 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 When collecting data, the system prioritizes the collection of highly relevant data, taking into account the geographical location of companies. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, we analyze the company's social media activities and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, prioritize the analysis 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 is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is We estimate user emotions and adjust how insights are generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating insights, the system analyzes the company's past management decisions to select the most suitable method for generating insights. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating insights, customize the content of the insights based on the company's current business situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates user sentiment and prioritizes insights based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating insights, the system takes into account the company's geographical location to generate the most optimal insights. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating insights, we analyze a company's social media activities and propose insights based on that analysis. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, We estimate user sentiment and adjust how we deliver insights based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing insights, the optimal delivery method is selected by referring to the company's past insight usage history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing insights, customize the method of delivering those insights based on the company's current business situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, We estimate user emotions and determine the priority of insight delivery based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing insights, the optimal method of providing insights will be selected, taking into account the company's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing insights, we analyze a company's social media activities and propose methods for delivering those insights. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 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 collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates insights based on the analysis results obtained by the analysis unit, A providing unit that provides insights generated by the generation unit, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is Integrate with BI tools to collect image data. The system according to feature 1.

3. The aforementioned supply unit is, Providing insights through a chat-based interface. The system according to feature 1.

4. The generating unit is Generating insights using statistical models The system according to feature 1.

5. The aforementioned supply unit is, Use a chatbot to provide interactive insights. 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 The types of data collected are dynamically changed based on the company's current business challenges. The system according to feature 1.

8. The aforementioned collection unit is During data collection, patterns in past collected data are analyzed to select the optimal collection method. 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 When collecting data, the system prioritizes the collection of highly relevant data, taking into account the geographical location of companies. The system according to feature 1.

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