system

Abstract

The system according to this embodiment aims to automatically analyze corporate data and generate and display visually appealing presentations. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a display unit. The collection unit collects company data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a presentation based on the data analyzed by the analysis unit. The display unit visually displays the presentation 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 method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that the process of effectively converting enterprise data into a presentation and visually displaying it requires labor and time.

[0005] The system according to the embodiment aims to automatically analyze enterprise data and generate and display a visually attractive presentation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, and a display unit. The data collection unit collects company data. The analysis unit analyzes the data collected by the data collection unit. The generation unit generates a presentation based on the data analyzed by the analysis unit. The display unit visually displays the presentation generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically analyze corporate data and generate and display visually appealing presentations. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards 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) The presentation generation system according to an embodiment of the present invention is a system in which a generating AI utilizes corporate data and automatically converts it into a presentation. This presentation generation system collects corporate data, the generating AI analyzes the collected data, and generates the content of the presentation. The generated presentation is personalized based on the recipient's position and interests. For example, it emphasizes overall sales performance and market trends for management, and displays detailed specific customer data and product sales performance for sales representatives. The generated presentation is displayed in a visually appealing way. For example, it visualizes data using graphs and charts so that recipients can understand it intuitively. It also incorporates interactive elements so that detailed data can be grasped in an instant. For example, clicking on a specific data point displays detailed information. This system allows recipients to intuitively understand information and grasp detailed data in an instant. The generating AI understands individual characteristics and provides an optimal information delivery experience based on them. For example, it customizes the content of the presentation based on topics and positions that the recipient has shown interest in in the past. This allows recipients to quickly grasp the information that is most important to them. As a result, the presentation generation system can automatically convert corporate data into a presentation and display it in a visually appealing way.

[0029] The presentation generation system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a display unit. The collection unit collects company data. Company data includes, but is not limited to, sales data, customer data, and product data. The collection unit can, for example, collect company sales data. The collection unit can also collect customer data. Furthermore, the collection unit can also collect product data. For example, the collection unit includes a system that automatically collects company sales data. Customer data includes customer attribute information and purchase history. Product data includes product specifications and sales quantities. The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, statistical analysis or machine learning algorithms, but is not limited to these examples. For example, the analysis unit can analyze data using statistical analysis. The analysis unit can also analyze data using machine learning algorithms. Furthermore, the analysis unit can also analyze data using data mining techniques. For example, the analysis unit uses regression analysis or clustering as statistical analysis. Machine learning algorithms learn data patterns and perform predictions and classifications. Data mining techniques extract useful information from large amounts of data. The generation unit generates a presentation based on the data analyzed by the analysis unit. Generation is performed using, for example, template-based generation or automatic layout generation, but is not limited to these examples. For example, the generation unit can generate a presentation using template-based generation. The generation unit can also generate a presentation using automatic layout generation. Furthermore, the generation unit can generate a presentation using automatic data placement. For example, as template-based generation, the generation unit embeds data into a pre-prepared template. Automatic layout generation automatically generates the optimal layout according to the content of the data. Automatic data placement places data in the appropriate location based on its type and importance. The display unit visually displays the presentation generated by the generation unit. Display is performed using, for example, graphs and charts, but is not limited to these examples.For example, the display unit can visualize data using graphs. It can also visualize data using charts. Furthermore, it can visualize data using infographics. For example, the display unit can use bar graphs or pie charts as graphs. It can use line graphs or histograms as charts. Infographics display data in a visually appealing way. Thus, the presentation generation system according to this embodiment can automatically convert corporate data into presentations and display them in a visually appealing way.

[0030] The data collection unit collects corporate data. Corporate data includes, but is not limited to, sales data, customer data, and product data. For example, the data collection unit can collect corporate sales data. It can also collect customer data. Furthermore, it can also collect product data. For example, the data collection unit has a system that automatically collects corporate sales data. Customer data includes customer attribute information and purchase history. Product data includes product specifications and sales volume. The data collection unit can utilize APIs and database connections to efficiently collect this data. For example, corporate sales data can be obtained directly from POS systems. Customer data can be collected from CRM systems and marketing platforms. Product data can be obtained from product management systems and inventory management systems. The data collection unit collects this data in real time and stores it in a central database. Furthermore, the data collection unit can perform data validation and cleaning to ensure data quality. For example, it can detect duplicate or missing data and take appropriate action. This allows the data collection unit to provide accurate and reliable data.

[0031] The analysis unit analyzes the data collected by the collection unit. Analysis is performed using, but is not limited to, statistical analysis or machine learning algorithms. For example, the analysis unit can analyze data using statistical analysis. It can also analyze data using machine learning algorithms. Furthermore, the analysis unit can analyze data using data mining techniques. For example, the analysis unit uses regression analysis and clustering as statistical analyses. Machine learning algorithms learn data patterns and perform predictions and classifications. Data mining techniques extract useful information from large amounts of data. The analysis unit can combine these techniques to perform multifaceted analysis of data. For example, it can analyze sales data to understand sales trends and seasonal fluctuations. It can analyze customer data to identify customer segmentation and purchasing behavior patterns. It can analyze product data to optimize product popularity and inventory. Furthermore, the analysis unit can perform automated data analysis using AI. For example, it can use natural language processing techniques to analyze customer reviews and feedback to extract customer sentiment and opinions. This allows the analysis unit to perform multifaceted analysis of corporate data and provide valuable insights.

[0032] The generation unit generates presentations based on data analyzed by the analysis unit. Generation is performed using, for example, template-based generation or automatic layout generation, but is not limited to these examples. For example, the generation unit can generate presentations using template-based generation. It can also generate presentations using automatic layout generation. Furthermore, the generation unit can generate presentations using automatic data placement. For example, as template-based generation, the generation unit embeds data into a pre-prepared template. Automatic layout generation automatically generates the optimal layout according to the content of the data. Automatic data placement places data in the appropriate location based on its type and importance. By combining these technologies, the generation unit can produce visually appealing and informative presentations. For example, it can automatically generate graphs and charts based on sales data and create infographics based on customer data. Furthermore, the generation unit can automatically generate presentation content using AI. For example, it can automatically create explanations and summaries of the data using natural language generation technology. This allows the generation unit to efficiently transform corporate data into presentations and present them in a visually appealing format.

[0033] The display unit visually displays the presentation generated by the generation unit. The display may, but is not limited to, graphs and charts. For example, the display unit can visualize data using graphs. It can also visualize data using charts. Furthermore, the display unit can visualize data using infographics. For example, the display unit may use bar graphs or pie charts as graphs. It may use line graphs or histograms as charts. Infographics present data in a visually appealing way. The display unit can combine these visualization techniques to facilitate data understanding. For example, sales data can be displayed as bar graphs, customer data as pie charts, and product data as infographics to create a visually engaging presentation. Furthermore, the display unit can incorporate interactive elements. For example, users can click on graphs or charts to display more detailed information. The display unit can also update data in real time to display the latest information. This allows the display unit to provide a visually engaging and interactive presentation, facilitating data understanding.

[0034] The data collection unit can collect a company's sales data, customer data, product data, and so on. For example, the data collection unit can collect a company's sales data. For example, the data collection unit can collect monthly sales data and annual sales data. The data collection unit can also collect sales data by product. For example, the data collection unit can collect customer data. For example, the data collection unit can collect customer attribute information and purchase history. The data collection unit can also collect customer behavior data. For example, the data collection unit can collect product data. For example, the data collection unit can collect product specifications and sales quantities. The data collection unit can also collect product inventory data. This allows for the collection of diverse data for a company. Some or all of the above-described processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input a company's sales data into a generation AI, which can then analyze and collect the sales data.

[0035] The analysis unit can analyze the collected data and customize the presentation content based on the recipient's role and interests. For example, the analysis unit can analyze the collected data using statistical analysis. For example, the analysis unit can analyze the data using regression analysis. Furthermore, the analysis unit can analyze the data using clustering. For example, the analysis unit can analyze the collected data using machine learning algorithms. For example, the analysis unit can learn data patterns and perform predictions and classifications. Furthermore, the analysis unit can analyze the data using data mining techniques. For example, the analysis unit can extract useful information from large amounts of data. The analysis unit can customize the presentation content based on the recipient's role and interests. For example, the analysis unit can generate a presentation for management that emphasizes overall sales performance and market trends. Furthermore, the analysis unit can generate a presentation for sales representatives that displays specific customer data and product sales performance in detail. This allows for the provision of customized presentations tailored to the recipient's role and interests. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or without generative AI. For example, the analysis unit can input the collected data into a generating AI, which can then analyze the data and customize the content of the presentation.

[0036] The generation unit can generate presentations for management that emphasize overall sales performance and market trends, and for sales representatives that display detailed customer data and product sales performance, based on the analysis results. For example, the generation unit can generate a presentation for management that emphasizes overall sales performance. For example, the generation unit can visualize sales data in graphs and charts so that management can understand it intuitively. The generation unit can also generate a presentation that emphasizes market trends. For example, the generation unit can display market data in infographics so that management can grasp market trends. The generation unit can generate a presentation for sales representatives that displays detailed customer data. For example, the generation unit can display customer data in tabular format so that sales representatives can grasp customer attribute information and purchase history at a glance. The generation unit can also generate a presentation that displays detailed product sales performance. For example, the generation unit can display product sales volume in graphs so that sales representatives can quickly grasp sales performance. This makes it possible to provide appropriate information according to the recipient's position. Some or all of the above processing in the generation unit may be performed using generation AI, or it may be performed without generation AI. For example, the generation unit can input the analysis results into the generation AI, which can then generate a presentation.

[0037] The display unit can visualize data using graphs and charts so that recipients can understand it intuitively. The display unit can visualize data using graphs, for example. The display unit can visualize sales data using bar graphs, for example. The display unit can also visualize market share using pie charts. The display unit can visualize data using charts, for example. The display unit can visualize sales trends using line graphs, for example. The display unit can also visualize customer distribution using histograms. The display unit can visualize data using infographics, for example. The display unit can display data in a visually appealing way so that recipients can understand it intuitively. This allows data to be displayed in a visually appealing way. Some or all of the above processing in the display unit may be performed using or without a generating AI. For example, the display unit can input data into a generating AI, and the generating AI can visualize the data.

[0038] The display unit can incorporate interactive elements that display detailed information when a specific data point is clicked. For example, the display unit can display detailed information in a pop-up window when a specific data point is clicked. For example, the display unit can display detailed information when a data point in a graph or chart is clicked. The display unit can also display detailed information when a data point in an infographic is clicked. For example, the display unit can display detailed information in a separate window when a specific data point is clicked. For example, the display unit can display detailed information in text format. The display unit can also display detailed information in graphs or charts. This allows for a quick grasp of detailed data. Some or all of the above-described processes in the display unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the display unit can input data points into a generation AI, and the generation AI can display detailed information.

[0039] The data collection unit can analyze a company's past data collection history and select the optimal collection method. For example, the data collection unit can select the most efficient data collection method from past data collection history. For example, the data collection unit can optimize the collection frequency and timing based on past data collection history. For example, the data collection unit can analyze past data collection history and select the optimal collection method to avoid data duplication. This allows the optimal collection method to be selected based on past data collection history. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input past data collection history into a generation AI, and the generation AI can select the optimal collection method.

[0040] The data collection unit can filter data based on a company's current projects and areas of interest during data collection. For example, the data collection unit can prioritize the collection of data related to ongoing projects. For example, the data collection unit can filter and collect highly relevant data based on a company's areas of interest. For example, the data collection unit can select and collect necessary data according to a company's current needs. This allows for the collection of data that meets a company's current needs. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input a company's project information into a generative AI, which can then filter and collect relevant data.

[0041] 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 prioritize the collection of data related to the company's location. For example, the data collection unit can prioritize the collection of data from geographically close areas. For example, the data collection unit can prioritize the collection of highly relevant data based on the company's area of ​​activity. This allows for the collection of highly relevant data based on the geographical location information of companies. Some or all of the above processing in the data collection unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the data collection unit can input the geographical location information of companies into a generating AI, and the generating AI can prioritize the collection of relevant data.

[0042] 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 the content of a company's social media posts and collect relevant data. For example, the data collection unit can collect engagement data on a company's social media. For example, the data collection unit can collect highly relevant data based on a company's social media activities. This allows for the collection of highly relevant data based on a company's social media activities. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input a company's social media data into a generative AI, which can then collect relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during data analysis. For example, the analysis unit can perform a detailed analysis on important data and a simplified analysis on less important data. For example, the analysis unit can determine the priority of the analysis according to the importance of the data. For example, the analysis unit can apply multiple analysis methods to important data to obtain detailed results. This allows the level of detail of the analysis to be adjusted according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI, and the generative AI can adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during data analysis. For example, the analysis unit can apply a sales forecasting algorithm to sales data and a customer segmentation algorithm to customer data. For example, the analysis unit can apply a product lifecycle analysis algorithm to product data. For example, the analysis unit can apply a market trend analysis algorithm to market data. This allows for appropriate analysis according to the data category. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input the data category into a generation AI, and the generation AI can apply an appropriate analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the data submission date during data analysis. For example, the analysis unit can prioritize the analysis of the most recent data and postpone the analysis of older data. For example, the analysis unit can prioritize the analysis of data with approaching submission deadlines. For example, the analysis unit can adjust the analysis schedule based on the submission date. This allows the analysis priority to be determined based on the data submission date. Some or all of the above processes in the analysis unit may be performed using a generation AI, or not. For example, the analysis unit can input the data submission date into the generation AI, and the generation AI can determine the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during data analysis. For example, the analysis unit can prioritize the analysis of highly relevant data and postpone the analysis of less relevant data. For example, the analysis unit can adjust the analysis schedule based on the relevance of the data. For example, the analysis unit can perform detailed analysis on highly relevant data and simplified analysis on less relevant data. This allows the order of analysis to be adjusted based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI, and the generative AI can adjust the order of analysis.

[0047] The generation unit can adjust the level of detail of the presentation based on the importance of the data during the presentation generation process. For example, the generation unit can generate a detailed presentation for important data and a simplified presentation for less important data. For example, the generation unit can determine the priority of the presentation according to the importance of the data. For example, the generation unit can apply multiple visualization techniques to important data to generate a detailed presentation. This allows the level of detail of the presentation to be adjusted according to the importance of the data. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the importance of the data into the generation AI, and the generation AI can adjust the level of detail of the presentation.

[0048] The generation unit can apply different generation algorithms depending on the data category when generating a presentation. For example, the generation unit can apply a sales forecasting algorithm to sales data and a customer segmentation algorithm to customer data. For example, the generation unit can apply a product lifecycle analysis algorithm to product data. For example, the generation unit can apply a market trend analysis algorithm to market data. This makes it possible to generate an appropriate presentation according to the data category. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the data category into the generation AI, and the generation AI can apply an appropriate generation algorithm.

[0049] The generation unit can determine the generation priority based on the data submission timing when generating a presentation. For example, the generation unit can prioritize the inclusion of data with approaching submission deadlines in the presentation. For example, the generation unit can prioritize the inclusion of the latest data in the presentation. For example, the generation unit can adjust the presentation generation schedule based on the submission timing. This allows the generation priority of the presentation to be determined based on the data submission timing. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input the data submission timing into the generation AI, and the generation AI can determine the generation priority.

[0050] The generation unit can adjust the generation order based on the relevance of the data when generating presentations. For example, the generation unit can prioritize reflecting highly relevant data in the presentation and postpone less relevant data. For example, the generation unit can adjust the presentation generation schedule based on the relevance of the data. For example, the generation unit can generate detailed presentations for highly relevant data and simplified presentations for less relevant data. This allows the generation order of presentations to be adjusted based on the relevance of the data. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the relevance of the data into the generation AI, and the generation AI can adjust the generation order.

[0051] The display unit can select the optimal display method by referring to the user's past operation history when displaying information. For example, the display unit can prioritize display methods that the user has previously preferred to use. For example, the display unit can select the optimal display layout from the user's past operation history. For example, the display unit can analyze the user's past operation history and provide the most efficient display method. This allows the display unit to provide the optimal display method based on the user's past operation history. Some or all of the above-described processes in the display unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the display unit can input user operation history data into a generation AI, which can then select the optimal display method.

[0052] The display unit can adjust the level of detail of the display based on the importance of the data during display. For example, the display unit can provide a detailed display for important data and a simplified display for less important data. For example, the display unit can determine the display priority according to the importance of the data. For example, the display unit can apply multiple visualization techniques to important data to provide a detailed display. This allows the level of detail of the display to be adjusted according to the importance of the data. Some or all of the above processing in the display unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the display unit can input the importance of the data into the generation AI, and the generation AI can adjust the level of detail of the display.

[0053] The display unit can select the optimal display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. For example, if the user is using a tablet, the display unit can provide a display method optimized for a larger screen. For example, if the user is using a desktop, the display unit can provide a display method that includes detailed information. This allows the display unit to provide the optimal display method based on the user's device information. Some or all of the above processing in the display unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the display unit can input the user's device information into a generation AI, and the generation AI can select the optimal display method.

[0054] The display unit can adjust the display order based on the relevance of the data during display. For example, the display unit can prioritize the display of highly relevant data and postpone the display of less relevant data. For example, the display unit can adjust the display schedule based on the relevance of the data. For example, the display unit can provide detailed displays for highly relevant data and simplified displays for less relevant data. This allows the display order to be adjusted based on the relevance of the data. Some or all of the above processing in the display unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the display unit can input the relevance of the data into a generation AI, and the generation AI can adjust the display order.

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

[0056] The data collection unit can be equipped with a function to evaluate the reliability of data when collecting corporate data. For example, the data collection unit can calculate a reliability score based on the data source and collection method. It can also check the consistency and integrity of the data and exclude unreliable data. Furthermore, the data collection unit can determine collection priorities based on data reliability. This allows for the priority collection of reliable data, improving the accuracy of presentations.

[0057] The analysis unit can be equipped with anomaly detection capabilities when analyzing collected data. For example, the analysis unit can detect outliers in the data using statistical methods. Furthermore, the analysis unit can learn anomaly patterns using machine learning algorithms and detect anomalies in real time. In addition, the analysis unit can correct or supplement the data based on the anomaly detection results. This ensures data quality and enables the generation of accurate presentations.

[0058] The generation unit can incorporate multilingual data handling capabilities when generating presentations. For example, it can translate collected data into multiple languages ​​and generate presentations corresponding to each language. Furthermore, it can perform appropriate translations, taking into account the cultural background and expression methods of each language. In addition, it can automatically provide presentations in the appropriate language based on the user's language settings. This enables use in international companies and multilingual environments.

[0059] The display unit can incorporate a real-time data update function when displaying generated presentations. For example, the display unit can link with a company's database to retrieve and display the latest data in real time. Furthermore, if data changes occur during the presentation, the display unit can automatically reflect the updated content. Additionally, when a user clicks on a specific data point, the display unit can retrieve and display the latest information for that data. This ensures that the latest information is always provided, deepening the recipient's understanding.

[0060] The display unit can incorporate a text-to-speech function when displaying generated presentations. For example, the display unit can convert text data into speech and read the presentation content aloud. It can also read specific data points based on user instructions. Furthermore, the display unit can adjust the speed and tone of the speech to provide audio output tailored to the user's preferences. This allows for the provision of not only visual but also auditory information, supporting the recipient's understanding.

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

[0062] Step 1: The data collection unit collects company data. This data includes sales data, customer data, and product data. For example, the data collection unit has a system that automatically collects company sales data and can also collect customer data and product data. Customer data includes customer attribute information and purchase history, while product data includes product specifications and sales volume. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis, machine learning algorithms, and data mining techniques. For example, the analysis unit uses regression analysis and clustering as statistical analysis, and learns data patterns using machine learning algorithms to perform predictions and classifications. Data mining techniques are used to extract useful information from large amounts of data. Step 3: The generation unit generates a presentation based on the data analyzed by the analysis unit. Generation is performed using template-based generation, automatic layout generation, and automatic data placement. For example, the generation unit performs template-based generation, which embeds data into pre-prepared templates, and automatic layout generation, which automatically generates the optimal layout according to the content of the data. Step 4: The display unit visually displays the presentation generated by the generation unit. The display is done using graphs, charts, and infographics. For example, the display unit visualizes data using bar graphs, pie charts, line graphs, and histograms, and presents the data in a visually appealing way using infographics.

[0063] (Example of form 2) The presentation generation system according to an embodiment of the present invention is a system in which a generating AI utilizes corporate data and automatically converts it into a presentation. This presentation generation system collects corporate data, the generating AI analyzes the collected data, and generates the content of the presentation. The generated presentation is personalized based on the recipient's position and interests. For example, it emphasizes overall sales performance and market trends for management, and displays detailed specific customer data and product sales performance for sales representatives. The generated presentation is displayed in a visually appealing way. For example, it visualizes data using graphs and charts so that recipients can understand it intuitively. It also incorporates interactive elements so that detailed data can be grasped in an instant. For example, clicking on a specific data point displays detailed information. This system allows recipients to intuitively understand information and grasp detailed data in an instant. The generating AI understands individual characteristics and provides an optimal information delivery experience based on them. For example, it customizes the content of the presentation based on topics and positions that the recipient has shown interest in in the past. This allows recipients to quickly grasp the information that is most important to them. As a result, the presentation generation system can automatically convert corporate data into a presentation and display it in a visually appealing way.

[0064] The presentation generation system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a display unit. The collection unit collects company data. Company data includes, but is not limited to, sales data, customer data, and product data. The collection unit can, for example, collect company sales data. The collection unit can also collect customer data. Furthermore, the collection unit can also collect product data. For example, the collection unit includes a system that automatically collects company sales data. Customer data includes customer attribute information and purchase history. Product data includes product specifications and sales quantities. The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, statistical analysis or machine learning algorithms, but is not limited to these examples. For example, the analysis unit can analyze data using statistical analysis. The analysis unit can also analyze data using machine learning algorithms. Furthermore, the analysis unit can also analyze data using data mining techniques. For example, the analysis unit uses regression analysis or clustering as statistical analysis. Machine learning algorithms learn data patterns and perform predictions and classifications. Data mining techniques extract useful information from large amounts of data. The generation unit generates a presentation based on the data analyzed by the analysis unit. Generation is performed using, for example, template-based generation or automatic layout generation, but is not limited to these examples. For example, the generation unit can generate a presentation using template-based generation. The generation unit can also generate a presentation using automatic layout generation. Furthermore, the generation unit can generate a presentation using automatic data placement. For example, as template-based generation, the generation unit embeds data into a pre-prepared template. Automatic layout generation automatically generates the optimal layout according to the content of the data. Automatic data placement places data in the appropriate location based on its type and importance. The display unit visually displays the presentation generated by the generation unit. Display is performed using, for example, graphs and charts, but is not limited to these examples.For example, the display unit can visualize data using graphs. It can also visualize data using charts. Furthermore, it can visualize data using infographics. For example, the display unit can use bar graphs or pie charts as graphs. It can use line graphs or histograms as charts. Infographics display data in a visually appealing way. Thus, the presentation generation system according to this embodiment can automatically convert corporate data into presentations and display them in a visually appealing way.

[0065] The data collection unit collects corporate data. Corporate data includes, but is not limited to, sales data, customer data, and product data. For example, the data collection unit can collect corporate sales data. It can also collect customer data. Furthermore, it can also collect product data. For example, the data collection unit has a system that automatically collects corporate sales data. Customer data includes customer attribute information and purchase history. Product data includes product specifications and sales volume. The data collection unit can utilize APIs and database connections to efficiently collect this data. For example, corporate sales data can be obtained directly from POS systems. Customer data can be collected from CRM systems and marketing platforms. Product data can be obtained from product management systems and inventory management systems. The data collection unit collects this data in real time and stores it in a central database. Furthermore, the data collection unit can perform data validation and cleaning to ensure data quality. For example, it can detect duplicate or missing data and take appropriate action. This allows the data collection unit to provide accurate and reliable data.

[0066] The analysis unit analyzes the data collected by the collection unit. Analysis is performed using, but is not limited to, statistical analysis or machine learning algorithms. For example, the analysis unit can analyze data using statistical analysis. It can also analyze data using machine learning algorithms. Furthermore, the analysis unit can analyze data using data mining techniques. For example, the analysis unit uses regression analysis and clustering as statistical analyses. Machine learning algorithms learn data patterns and perform predictions and classifications. Data mining techniques extract useful information from large amounts of data. The analysis unit can combine these techniques to perform multifaceted analysis of data. For example, it can analyze sales data to understand sales trends and seasonal fluctuations. It can analyze customer data to identify customer segmentation and purchasing behavior patterns. It can analyze product data to optimize product popularity and inventory. Furthermore, the analysis unit can perform automated data analysis using AI. For example, it can use natural language processing techniques to analyze customer reviews and feedback to extract customer sentiment and opinions. This allows the analysis unit to perform multifaceted analysis of corporate data and provide valuable insights.

[0067] The generation unit generates presentations based on data analyzed by the analysis unit. Generation is performed using, for example, template-based generation or automatic layout generation, but is not limited to these examples. For example, the generation unit can generate presentations using template-based generation. It can also generate presentations using automatic layout generation. Furthermore, the generation unit can generate presentations using automatic data placement. For example, as template-based generation, the generation unit embeds data into a pre-prepared template. Automatic layout generation automatically generates the optimal layout according to the content of the data. Automatic data placement places data in the appropriate location based on its type and importance. By combining these technologies, the generation unit can produce visually appealing and informative presentations. For example, it can automatically generate graphs and charts based on sales data and create infographics based on customer data. Furthermore, the generation unit can automatically generate presentation content using AI. For example, it can automatically create explanations and summaries of the data using natural language generation technology. This allows the generation unit to efficiently transform corporate data into presentations and present them in a visually appealing format.

[0068] The display unit visually displays the presentation generated by the generation unit. The display may, but is not limited to, graphs and charts. For example, the display unit can visualize data using graphs. It can also visualize data using charts. Furthermore, the display unit can visualize data using infographics. For example, the display unit may use bar graphs or pie charts as graphs. It may use line graphs or histograms as charts. Infographics present data in a visually appealing way. The display unit can combine these visualization techniques to facilitate data understanding. For example, sales data can be displayed as bar graphs, customer data as pie charts, and product data as infographics to create a visually engaging presentation. Furthermore, the display unit can incorporate interactive elements. For example, users can click on graphs or charts to display more detailed information. The display unit can also update data in real time to display the latest information. This allows the display unit to provide a visually engaging and interactive presentation, facilitating data understanding.

[0069] The data collection unit can collect a company's sales data, customer data, product data, and so on. For example, the data collection unit can collect a company's sales data. For example, the data collection unit can collect monthly sales data and annual sales data. The data collection unit can also collect sales data by product. For example, the data collection unit can collect customer data. For example, the data collection unit can collect customer attribute information and purchase history. The data collection unit can also collect customer behavior data. For example, the data collection unit can collect product data. For example, the data collection unit can collect product specifications and sales quantities. The data collection unit can also collect product inventory data. This allows for the collection of diverse data for a company. Some or all of the above-described processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input a company's sales data into a generation AI, which can then analyze and collect the sales data.

[0070] The analysis unit can analyze the collected data and customize the presentation content based on the recipient's role and interests. For example, the analysis unit can analyze the collected data using statistical analysis. For example, the analysis unit can analyze the data using regression analysis. Furthermore, the analysis unit can analyze the data using clustering. For example, the analysis unit can analyze the collected data using machine learning algorithms. For example, the analysis unit can learn data patterns and perform predictions and classifications. Furthermore, the analysis unit can analyze the data using data mining techniques. For example, the analysis unit can extract useful information from large amounts of data. The analysis unit can customize the presentation content based on the recipient's role and interests. For example, the analysis unit can generate a presentation for management that emphasizes overall sales performance and market trends. Furthermore, the analysis unit can generate a presentation for sales representatives that displays specific customer data and product sales performance in detail. This allows for the provision of customized presentations tailored to the recipient's role and interests. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or without generative AI. For example, the analysis unit can input the collected data into a generating AI, which can then analyze the data and customize the content of the presentation.

[0071] The generation unit can generate presentations for management that emphasize overall sales performance and market trends, and for sales representatives that display detailed customer data and product sales performance, based on the analysis results. For example, the generation unit can generate a presentation for management that emphasizes overall sales performance. For example, the generation unit can visualize sales data in graphs and charts so that management can understand it intuitively. The generation unit can also generate a presentation that emphasizes market trends. For example, the generation unit can display market data in infographics so that management can grasp market trends. The generation unit can generate a presentation for sales representatives that displays detailed customer data. For example, the generation unit can display customer data in tabular format so that sales representatives can grasp customer attribute information and purchase history at a glance. The generation unit can also generate a presentation that displays detailed product sales performance. For example, the generation unit can display product sales volume in graphs so that sales representatives can quickly grasp sales performance. This makes it possible to provide appropriate information according to the recipient's position. Some or all of the above processing in the generation unit may be performed using generation AI, or it may be performed without generation AI. For example, the generation unit can input the analysis results into the generation AI, which can then generate a presentation.

[0072] The display unit can visualize data using graphs and charts so that recipients can understand it intuitively. The display unit can visualize data using graphs, for example. The display unit can visualize sales data using bar graphs, for example. The display unit can also visualize market share using pie charts. The display unit can visualize data using charts, for example. The display unit can visualize sales trends using line graphs, for example. The display unit can also visualize customer distribution using histograms. The display unit can visualize data using infographics, for example. The display unit can display data in a visually appealing way so that recipients can understand it intuitively. This allows data to be displayed in a visually appealing way. Some or all of the above processing in the display unit may be performed using or without a generating AI. For example, the display unit can input data into a generating AI, and the generating AI can visualize the data.

[0073] The display unit can incorporate interactive elements that display detailed information when a specific data point is clicked. For example, the display unit can display detailed information in a pop-up window when a specific data point is clicked. For example, the display unit can display detailed information when a data point in a graph or chart is clicked. The display unit can also display detailed information when a data point in an infographic is clicked. For example, the display unit can display detailed information in a separate window when a specific data point is clicked. For example, the display unit can display detailed information in text format. The display unit can also display detailed information in graphs or charts. This allows for a quick grasp of detailed data. Some or all of the above-described processes in the display unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the display unit can input data points into a generation AI, and the generation AI can display detailed information.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on changes in facial expressions. The data collection unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the data collection unit can analyze the tone and speed of the voice and calculate an emotion score. The data collection unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on fluctuations in heart rate. This allows the timing of data collection to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using generative AI or not. For example, the data collection unit can input user facial expression data into a generating AI, which can then estimate emotions and adjust the timing of data collection.

[0075] The data collection unit can analyze a company's past data collection history and select the optimal collection method. For example, the data collection unit can select the most efficient data collection method from past data collection history. For example, the data collection unit can optimize the collection frequency and timing based on past data collection history. For example, the data collection unit can analyze past data collection history and select the optimal collection method to avoid data duplication. This allows the optimal collection method to be selected based on past data collection history. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input past data collection history into a generation AI, and the generation AI can select the optimal collection method.

[0076] The data collection unit can filter data based on a company's current projects and areas of interest during data collection. For example, the data collection unit can prioritize the collection of data related to ongoing projects. For example, the data collection unit can filter and collect highly relevant data based on a company's areas of interest. For example, the data collection unit can select and collect necessary data according to a company's current needs. This allows for the collection of data that meets a company's current needs. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input a company's project information into a generative AI, which can then filter and collect relevant data.

[0077] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, the data collection unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on changes in facial expressions. The data collection unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the data collection unit can analyze the tone and speed of the voice and calculate an emotion score. The data collection unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on fluctuations in heart rate. This allows the data collection unit to determine the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using generative AI or not. For example, the data collection unit can input user facial expression data into a generating AI, which can then estimate emotions and determine the priority of data to collect.

[0078] 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 prioritize the collection of data related to the company's location. For example, the data collection unit can prioritize the collection of data from geographically close areas. For example, the data collection unit can prioritize the collection of highly relevant data based on the company's area of ​​activity. This allows for the collection of highly relevant data based on the geographical location information of companies. Some or all of the above processing in the data collection unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the data collection unit can input the geographical location information of companies into a generating AI, and the generating AI can prioritize the collection of relevant data.

[0079] 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 the content of a company's social media posts and collect relevant data. For example, the data collection unit can collect engagement data on a company's social media. For example, the data collection unit can collect highly relevant data based on a company's social media activities. This allows for the collection of highly relevant data based on a company's social media activities. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input a company's social media data into a generative AI, which can then collect relevant data.

[0080] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in facial expressions. The analysis unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice and calculate an emotion score. The analysis unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on fluctuations in heart rate. This allows the data analysis method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using generative AI or not. For example, the analysis unit can input user facial expression data into a generating AI, which can then estimate emotions and adjust the data analysis method accordingly.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during data analysis. For example, the analysis unit can perform a detailed analysis on important data and a simplified analysis on less important data. For example, the analysis unit can determine the priority of the analysis according to the importance of the data. For example, the analysis unit can apply multiple analysis methods to important data to obtain detailed results. This allows the level of detail of the analysis to be adjusted according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI, and the generative AI can adjust the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the data category during data analysis. For example, the analysis unit can apply a sales forecasting algorithm to sales data and a customer segmentation algorithm to customer data. For example, the analysis unit can apply a product lifecycle analysis algorithm to product data. For example, the analysis unit can apply a market trend analysis algorithm to market data. This allows for appropriate analysis according to the data category. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input the data category into a generation AI, and the generation AI can apply an appropriate analysis algorithm.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in facial expressions. The analysis unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice and calculate an emotion score. The analysis unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on fluctuations in heart rate. This allows the display method of the analysis results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using generative AI or not. For example, the analysis unit can input user facial expression data into a generating AI, which can then estimate emotions and adjust how the analysis results are displayed.

[0084] The analysis unit can determine the priority of analysis based on the data submission date during data analysis. For example, the analysis unit can prioritize the analysis of the most recent data and postpone the analysis of older data. For example, the analysis unit can prioritize the analysis of data with approaching submission deadlines. For example, the analysis unit can adjust the analysis schedule based on the submission date. This allows the analysis priority to be determined based on the data submission date. Some or all of the above processes in the analysis unit may be performed using a generation AI, or not. For example, the analysis unit can input the data submission date into the generation AI, and the generation AI can determine the analysis priority.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the data during data analysis. For example, the analysis unit can prioritize the analysis of highly relevant data and postpone the analysis of less relevant data. For example, the analysis unit can adjust the analysis schedule based on the relevance of the data. For example, the analysis unit can perform detailed analysis on highly relevant data and simplified analysis on less relevant data. This allows the order of analysis to be adjusted based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI, and the generative AI can adjust the order of analysis.

[0086] The generation unit can estimate the user's emotions and adjust the presentation generation method based on the estimated user emotions. For example, the generation unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on changes in facial expressions. The generation unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the voice and calculate an emotion score. The generation unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on fluctuations in heart rate. This allows the presentation generation method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI or not. For example, the generation unit can input user facial expression data into the generation AI, which can then estimate emotions and adjust the presentation generation method accordingly.

[0087] The generation unit can adjust the level of detail of the presentation based on the importance of the data during the presentation generation process. For example, the generation unit can generate a detailed presentation for important data and a simplified presentation for less important data. For example, the generation unit can determine the priority of the presentation according to the importance of the data. For example, the generation unit can apply multiple visualization techniques to important data to generate a detailed presentation. This allows the level of detail of the presentation to be adjusted according to the importance of the data. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the importance of the data into the generation AI, and the generation AI can adjust the level of detail of the presentation.

[0088] The generation unit can apply different generation algorithms depending on the data category when generating a presentation. For example, the generation unit can apply a sales forecasting algorithm to sales data and a customer segmentation algorithm to customer data. For example, the generation unit can apply a product lifecycle analysis algorithm to product data. For example, the generation unit can apply a market trend analysis algorithm to market data. This makes it possible to generate an appropriate presentation according to the data category. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the data category into the generation AI, and the generation AI can apply an appropriate generation algorithm.

[0089] The generation unit can estimate the user's emotions and adjust the length of the presentation based on the estimated emotions. For example, the generation unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on changes in facial expressions. The generation unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the voice and calculate an emotion score. The generation unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on fluctuations in heart rate. This allows the length of the presentation to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI or not. For example, the generation unit can input user facial expression data into the generation AI, which can then estimate emotions and adjust the length of the presentation.

[0090] The generation unit can determine the generation priority based on the data submission timing when generating a presentation. For example, the generation unit can prioritize the inclusion of data with approaching submission deadlines in the presentation. For example, the generation unit can prioritize the inclusion of the latest data in the presentation. For example, the generation unit can adjust the presentation generation schedule based on the submission timing. This allows the generation priority of the presentation to be determined based on the data submission timing. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input the data submission timing into the generation AI, and the generation AI can determine the generation priority.

[0091] The generation unit can adjust the generation order based on the relevance of the data when generating presentations. For example, the generation unit can prioritize reflecting highly relevant data in the presentation and postpone less relevant data. For example, the generation unit can adjust the presentation generation schedule based on the relevance of the data. For example, the generation unit can generate detailed presentations for highly relevant data and simplified presentations for less relevant data. This allows the generation order of presentations to be adjusted based on the relevance of the data. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the relevance of the data into the generation AI, and the generation AI can adjust the generation order.

[0092] The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, the display unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the display unit can calculate an emotion score based on changes in facial expressions. The display unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the display unit can analyze the tone and speed of the voice and calculate an emotion score. The display unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the display unit can calculate an emotion score based on fluctuations in heart rate. This allows the display method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the display unit may be performed using generative AI or not. For example, the display unit can input user facial expression data into a generating AI, which can then estimate emotions and adjust the display method accordingly.

[0093] The display unit can select the optimal display method by referring to the user's past operation history when displaying information. For example, the display unit can prioritize display methods that the user has previously preferred to use. For example, the display unit can select the optimal display layout from the user's past operation history. For example, the display unit can analyze the user's past operation history and provide the most efficient display method. This allows the display unit to provide the optimal display method based on the user's past operation history. Some or all of the above-described processes in the display unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the display unit can input user operation history data into a generation AI, which can then select the optimal display method.

[0094] The display unit can adjust the level of detail of the display based on the importance of the data during display. For example, the display unit can provide a detailed display for important data and a simplified display for less important data. For example, the display unit can determine the display priority according to the importance of the data. For example, the display unit can apply multiple visualization techniques to important data to provide a detailed display. This allows the level of detail of the display to be adjusted according to the importance of the data. Some or all of the above processing in the display unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the display unit can input the importance of the data into the generation AI, and the generation AI can adjust the level of detail of the display.

[0095] The display unit can estimate the user's emotions and determine the display priority based on the estimated user emotions. For example, the display unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the display unit can calculate an emotion score based on changes in facial expressions. The display unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the display unit can analyze the tone and speed of the voice and calculate an emotion score. The display unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the display unit can calculate an emotion score based on fluctuations in heart rate. This allows the display priority to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the display unit may be performed using generative AI or not. For example, the display unit can input user facial expression data into a generating AI, which can then estimate emotions and determine the display priority.

[0096] The display unit can select the optimal display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. For example, if the user is using a tablet, the display unit can provide a display method optimized for a larger screen. For example, if the user is using a desktop, the display unit can provide a display method that includes detailed information. This allows the display unit to provide the optimal display method based on the user's device information. Some or all of the above processing in the display unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the display unit can input the user's device information into a generation AI, and the generation AI can select the optimal display method.

[0097] The display unit can adjust the display order based on the relevance of the data during display. For example, the display unit can prioritize the display of highly relevant data and postpone the display of less relevant data. For example, the display unit can adjust the display schedule based on the relevance of the data. For example, the display unit can provide detailed displays for highly relevant data and simplified displays for less relevant data. This allows the display order to be adjusted based on the relevance of the data. Some or all of the above processing in the display unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the display unit can input the relevance of the data into a generation AI, and the generation AI can adjust the display order.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The data collection unit can be equipped with a function to evaluate the reliability of data when collecting corporate data. For example, the data collection unit can calculate a reliability score based on the data source and collection method. It can also check the consistency and integrity of the data and exclude unreliable data. Furthermore, the data collection unit can determine collection priorities based on data reliability. This allows for the priority collection of reliable data, improving the accuracy of presentations.

[0100] The analysis unit can be equipped with anomaly detection capabilities when analyzing collected data. For example, the analysis unit can detect outliers in the data using statistical methods. Furthermore, the analysis unit can learn anomaly patterns using machine learning algorithms and detect anomalies in real time. In addition, the analysis unit can correct or supplement the data based on the anomaly detection results. This ensures data quality and enables the generation of accurate presentations.

[0101] The generation unit can incorporate multilingual data handling capabilities when generating presentations. For example, it can translate collected data into multiple languages ​​and generate presentations corresponding to each language. Furthermore, it can perform appropriate translations, taking into account the cultural background and expression methods of each language. In addition, it can automatically provide presentations in the appropriate language based on the user's language settings. This enables use in international companies and multilingual environments.

[0102] The display unit can incorporate a real-time data update function when displaying generated presentations. For example, the display unit can link with a company's database to retrieve and display the latest data in real time. Furthermore, if data changes occur during the presentation, the display unit can automatically reflect the updated content. Additionally, when a user clicks on a specific data point, the display unit can retrieve and display the latest information for that data. This ensures that the latest information is always provided, deepening the recipient's understanding.

[0103] The display unit can incorporate a text-to-speech function when displaying generated presentations. For example, the display unit can convert text data into speech and read the presentation content aloud. It can also read specific data points based on user instructions. Furthermore, the display unit can adjust the speed and tone of the speech to provide audio output tailored to the user's preferences. This allows for the provision of not only visual but also auditory information, supporting the recipient's understanding.

[0104] The data collection unit can estimate the user's emotions and adjust the data collection method based on the estimated emotions. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the data collection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for more effective data collection by adjusting the data collection method according to the user's emotions.

[0105] The analysis unit can estimate the user's emotions and determine the priority of data analysis based on the estimated emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for the prioritization of data analysis according to the user's emotions, resulting in more effective data analysis.

[0106] The generation unit can estimate the user's emotions and adjust the presentation content based on those emotions. For example, the generation unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the generation unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the presentation content to be adjusted according to the user's emotions, resulting in more effective information delivery.

[0107] The display unit can estimate the user's emotions and adjust the interactivity of the display based on those estimated emotions. For example, the display unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the display unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the display to adjust its interactivity according to the user's emotions, enabling more effective information delivery.

[0108] The display unit can estimate the user's emotions and adjust the display's colors and design based on those estimated emotions. For example, the display unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the display unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the display's colors and design to be adjusted according to the user's emotions, enabling more effective information delivery.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit collects company data. This data includes sales data, customer data, and product data. For example, the data collection unit has a system that automatically collects company sales data and can also collect customer data and product data. Customer data includes customer attribute information and purchase history, while product data includes product specifications and sales volume. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis, machine learning algorithms, and data mining techniques. For example, the analysis unit uses regression analysis and clustering as statistical analysis, and learns data patterns using machine learning algorithms to perform predictions and classifications. Data mining techniques are used to extract useful information from large amounts of data. Step 3: The generation unit generates a presentation based on the data analyzed by the analysis unit. Generation is performed using template-based generation, automatic layout generation, and automatic data placement. For example, the generation unit performs template-based generation, which embeds data into pre-prepared templates, and automatic layout generation, which automatically generates the optimal layout according to the content of the data. Step 4: The display unit visually displays the presentation generated by the generation unit. The display is done using graphs, charts, and infographics. For example, the display unit visualizes data using bar graphs, pie charts, line graphs, and histograms, and presents the data in a visually appealing way using infographics.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, and display unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit can collect company sales data and customer data using the control unit 46A of the smart device 14. The analysis unit can analyze the data using statistical analysis and machine learning algorithms using the specific processing unit 290 of the data processing unit 12. The generation unit can generate presentations using template-based generation or automatic layout generation using the specific processing unit 290 of the data processing unit 12. The display unit can visualize the data using graphs and charts using the output device 40 of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and display unit, can be implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit can collect company sales data and customer data using the control unit 46A of the smart glasses 214. The analysis unit can analyze the data using statistical analysis and machine learning algorithms using the specific processing unit 290 of the data processing unit 12. The generation unit can generate presentations using template-based generation or automatic layout generation using the specific processing unit 290 of the data processing unit 12. The display unit can visualize graphs and charts using the speaker 240 or display of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, and display unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit can collect company sales data and customer data using the control unit 46A of the headset terminal 314. The analysis unit can analyze the data using statistical analysis and machine learning algorithms using the specific processing unit 290 of the data processing unit 12. The generation unit can generate presentations using template-based generation or automatic layout generation using the specific processing unit 290 of the data processing unit 12. The display unit can visualize graphs and charts using the display 343 and speaker 240 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and display unit, can be implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit can collect corporate sales data and customer data using the control unit 46A of the robot 414. The analysis unit can analyze the data using statistical analysis and machine learning algorithms using the specific processing unit 290 of the data processing unit 12. The generation unit can generate presentations using template-based generation or automatic layout generation using the specific processing unit 290 of the data processing unit 12. The display unit can visualize graphs and charts using the speaker 240 and LEDs in the eyes of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) The data collection department collects corporate data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates a presentation based on the data analyzed by the analysis unit, The system includes a display unit that visually displays the presentation generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collects company sales data, customer data, product data, etc. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed, and the presentation content is customized based on the recipient's role and interests. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Based on the analysis results, the system generates presentations that highlight overall sales performance and market trends for management, and provide detailed information on specific customer data and product sales performance for sales representatives. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned display unit is Graphs and charts are used to visualize data and make it easy for recipients to understand intuitively. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned display unit is Incorporate interactive elements that display detailed information when a specific data point is clicked. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze a company's past data collection history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the company's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is 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 12) 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 13) The aforementioned analysis unit, We estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During data analysis, adjust the level of detail of the analysis based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When analyzing data, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During data analysis, the priority of analysis is determined based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During data analysis, adjust the order of analysis based on the relationships between the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts how the presentation is generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating a presentation, 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 21) The generating unit is When generating a presentation, different generation algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts the length of the presentation based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating presentations, prioritize generation based on the data submission timing. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating presentations, adjust the generation order based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is It estimates the user's emotions and adjusts the display method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is When displaying information, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is When displaying data, 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 28) The aforementioned display unit is It estimates the user's emotions and determines the display priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is When displaying content, the system selects the optimal display method by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned display unit is When displaying data, adjust the display order based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects corporate data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates a presentation based on the data analyzed by the analysis unit, The system includes a display unit that visually displays the presentation generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collects company sales data, customer data, product data, etc. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed, and the presentation content is customized based on the recipient's role and interests. The system according to feature 1.

4. The generating unit is Based on the analysis results, the system generates presentations that highlight overall sales performance and market trends for management, and provide detailed information on specific customer data and product sales performance for sales representatives. The system according to feature 1.

5. The aforementioned display unit is Graphs and charts are used to visualize data and make it easy for recipients to understand intuitively. The system according to feature 1.

6. The aforementioned display unit is Incorporate interactive elements that display detailed information when a specific data point is clicked. The system according to feature 1.

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

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

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

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

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