system

Abstract

The system according to this embodiment aims to analyze data and visualize it effectively, even without specialized knowledge. [Solution] A system according to an embodiment includes an input unit, an analysis unit, and a visualization unit. The input unit inputs data. The analysis unit analyzes the data input by the input unit. The visualization unit visualizes the data analyzed by the analysis unit.

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies presented challenges in that the data analysis and visualization processes were time-consuming and difficult to perform effectively without specialized knowledge.

[0005] The system according to this embodiment aims to analyze data and visualize it effectively, even without specialized knowledge. [Means for solving the problem]

[0006] The system according to this embodiment comprises an input unit, an analysis unit, and a visualization unit. The input unit receives data. The analysis unit analyzes the data received by the input unit. The visualization unit visualizes the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment allows for data analysis and effective visualization even without specialized knowledge. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The data visualization system according to an embodiment of the present invention is a system that uses a generative AI to extract data from general data sources and visualize the results in an attractive manner. In this data visualization system, the user inputs data, the generative AI analyzes that data, and learns the company's unique data visualizations. The generative AI automatically adjusts fonts and colors to visualize the data attractively. This allows the user to entrust the data analysis and visualization process to the AI, maximizing the value that can be obtained from the data and effectively communicating the results to others. For example, the user inputs data. In this case, the user can easily input data regardless of the data format or content. For example, various types of data such as sales data and customer data can be input. This data is input to the generative AI. Next, the generative AI analyzes the input data. The generative AI understands the content of the data and learns the company's unique data visualizations. For example, it learns past data visualization patterns and font and color adjustments based on the company's brand guidelines. In this way, the generative AI accumulates knowledge for visualizing data attractively. Generative AI visualizes data in an engaging way based on its learned knowledge. For example, it can transform sales data into graphs and charts, and customer data into infographics. During this process, the generative AI automatically adjusts fonts and colors to make the data visually appealing. This allows users to delegate the data analysis and visualization process to the AI. This mechanism enables users to maximize the value they can derive from the data and effectively communicate the results to others. For instance, transforming sales data into compelling graphs allows for effective reporting to management. Similarly, transforming customer data into infographics allows for effective communication to the marketing team. Thus, data visualization systems allow users to delegate the data analysis and visualization process to AI, maximizing the value they can derive from the data and effectively communicating the results to others.

[0029] The data visualization system according to the embodiment includes an input unit, an analysis unit, and a visualization unit. The input unit allows a user to input data. Examples of data input by the user include, but are not limited to, sales data, customer data, and inventory data. The input unit can read data from common data sources, such as Excel files and CSV files. The input unit can also allow a user to manually input data. For example, a user can input data through a web form. The analysis unit uses a generation AI to analyze the data input by the input unit. For example, the analysis unit understands the content of the data and learns a company's unique data visualization. The generation AI learns patterns from past data visualizations and adjusts fonts and colors based on the company's brand guidelines. For example, the generation AI can learn visualization patterns from past sales data and apply similar patterns to new data. The generation AI can also automatically adjust fonts and colors based on the company's brand guidelines. The visualization unit uses a generation AI to visualize the data analyzed by the analysis unit. The visualization unit, for example, converts sales data into graphs and charts and customer data into infographics. The generation AI automatically adjusts fonts and colors to make the data visually appealing. For example, the generation AI can convert sales data into bar graphs and line graphs, and customer data into pie charts and heat maps. The generation AI can also select the optimal visualization method based on the content of the data. For example, a line graph can be selected to show the trend of sales data, and a heat map can be selected to show the distribution of customer data. This allows the data visualization system according to the embodiment to efficiently input, analyze, and visualize data. Some or all of the above-described processing in the visualization unit may be performed using, or without, the generation AI.For example, the visualization unit can perform visualization using an AI model that takes data analyzed by the generation AI as input and outputs visualization results.

[0030] The visualization unit can automatically adjust fonts and colors. The visualization unit can automatically adjust, for example, the font type, size, and color selection criteria. For example, the visualization unit can select appropriate fonts and colors based on the company's brand guidelines. The visualization unit can also select optimal fonts and colors based on the content of the data. For example, when visualizing sales data, it can select visually easy-to-understand fonts and colors, and when visualizing customer data, it can select visually appealing fonts and colors. This automatically adjusts fonts and colors, enabling visually appealing visualizations. Some or all of the above-described processing in the visualization unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the visualization unit can perform visualizations using an AI model that uses data analyzed by the generation AI as input and adjusts fonts and colors.

[0031] The visualization unit can convert the sales data into a graph or chart. For example, the visualization unit can convert the sales data into a graph or chart, such as a bar graph, line graph, or pie chart. For example, the visualization unit can select a line graph to show the trend of the sales data and a pie chart to show the distribution of the sales data. The visualization unit can also select a bar graph to compare sales data. For example, the visualization unit can convert monthly sales data into a bar graph and product sales data into a pie chart. This allows the sales data to be displayed in a visually easy-to-understand manner. Some or all of the above-mentioned processing in the visualization unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the visualization unit can perform visualization using an AI model that inputs sales data analyzed by the generation AI and converts it into a graph or chart.

[0032] The visualization unit can convert customer data into visual information diagrams. For example, the visualization unit can convert customer data into visual information diagrams such as infographics and heat maps. For example, the visualization unit can convert customer attribute information into infographics and customer purchase histories into heat maps. The visualization unit can also select a heat map to show the distribution of customer data and select an infographic to compare customer data. For example, the visualization unit can convert the age distribution of customers into infographics and customer purchase histories into heat maps. This allows customer data to be displayed in an easy-to-understand visual format. Some or all of the above-described processing in the visualization unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the visualization unit can perform visualization using an AI model that inputs customer data analyzed by the generation AI and converts it into a visual information diagram.

[0033] The analysis unit can analyze patterns of past data visualizations. For example, the analysis unit can learn patterns of past data visualizations and apply them to new data. For example, the analysis unit can learn visualization patterns of past sales data and apply similar patterns to new sales data. It can also learn visualization patterns of past customer data and apply similar patterns to new customer data. For example, the analysis unit can select a line graph to show trends in past sales data and apply similar patterns to new sales data. This allows for more effective visualization by learning patterns of past data visualizations. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can perform analysis using an AI model that takes past data visualization patterns analyzed by generative AI as input and applies them to new data.

[0034] The analysis unit can analyze font and color adjustments based on the company's brand guidelines. For example, the analysis unit can select appropriate fonts and colors based on the company's brand guidelines. For example, the analysis unit can learn the criteria for selecting font types, sizes, and colors based on the company's brand guidelines. The analysis unit can also select the optimal fonts and colors based on the content of the data. For example, when visualizing sales data, it can select fonts and colors that are visually easy to understand, and when visualizing customer data, it can select fonts and colors that are visually appealing. By learning font and color adjustments based on the company's brand guidelines, consistent visualization becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can take the company's brand guidelines analyzed by generative AI as input and perform analysis using an AI model that adjusts fonts and colors.

[0035] The input unit can analyze the user's past data input history and select an appropriate input method. The input unit, for example, analyzes the user's past data input history and provides an optimal input method. For example, the input unit preferentially suggests input methods (such as voice input or text input) that the user has frequently used in the past. The input unit can also automatically select a specific data format from the user's past input history to simplify input. Furthermore, the input unit can analyze patterns of data previously input by the user and provide an optimal input template. In this way, the optimal input method can be provided by analyzing the user's past data input history. Some or all of the above-described processing in the input unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the input unit can input the user's past data input history analyzed by the generation AI and use an AI model that selects an optimal input method.

[0036] The input unit can perform filtering based on the user's current project and areas of interest. The input unit, for example, prioritizes displaying highly relevant data based on the user's current project and areas of interest. For example, the input unit can display only data related to the project the user is currently working on, thereby streamlining input. The input unit can also prioritize displaying highly relevant data based on the user's areas of interest. Furthermore, the input unit can filter input candidates based on data in which the user has previously shown interest. This enables efficient data input by filtering data based on the user's current project and areas of interest. Some or all of the above-described processing in the input unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the input unit can input the user's project data and area of ​​interest data analyzed by the generation AI, using an AI model that performs filtering.

[0037] When inputting data, the input unit can prioritize inputting highly relevant data based on the user's geographical location information. The input unit, for example, prioritizes inputting highly relevant data taking into account the user's geographical location information. For example, when the user is in a specific area, the input unit prioritizes inputting data related to that area. Furthermore, when the user is moving, the input unit can also suggest highly relevant data based on the user's current location. Furthermore, when the user is in a specific location, the input unit can automatically display data related to that location. This allows highly relevant data to be efficiently input by taking the user's geographical location information into account. Some or all of the above-described processing in the input unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the input unit can input the user's geographical location information analyzed by the generation AI and use an AI model that prioritizes inputting highly relevant data.

[0038] The input unit can analyze the user's social media activity and input relevant data when inputting data. For example, the input unit analyzes the user's social media activity and automatically inputs relevant data. For example, the input unit analyzes the user's social media posts and automatically inputs relevant data. The input unit can also suggest input candidates based on the user's social media interests. Furthermore, the input unit can prioritize input of highly relevant data based on the user's social media activity history. This allows highly relevant data to be efficiently input by analyzing the user's social media activity. Some or all of the above-described processing in the input unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the input unit can input the user's social media activity data analyzed by the generation AI and use an AI model to input related data.

[0039] The analysis unit can adjust the level of detail of the analysis according to the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis for highly important data and a concise analysis for less important data. The analysis unit can also adjust the depth of the analysis according to the importance of the data. For example, the analysis unit can perform a detailed analysis for data with high business impact and a concise analysis for data with high novelty. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can take the importance of the data analyzed by generative AI as input and perform the analysis using an AI model that adjusts the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms according to the data category during analysis. For example, the analysis unit can select an appropriate analysis algorithm according to the data category. For example, the analysis unit can apply a sales forecasting algorithm to sales data and a customer segmentation algorithm to customer data. The analysis unit can also apply an inventory optimization algorithm to inventory data. For example, the analysis unit can apply a sales forecasting algorithm to predict sales data and a customer segmentation algorithm to analyze customer data. By applying different analysis algorithms according to the data category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can perform analysis using an AI model that takes the data categories analyzed by generative AI as input and applies an appropriate analysis algorithm.

[0041] The analysis unit can determine the priority of analysis based on the data submission date during the analysis. For example, the analysis unit can determine the priority of analysis based on the data submission date. For example, the analysis unit can prioritize the analysis of recently submitted data and postpone the analysis of older data. The analysis unit can also adjust the order of analysis based on the submission date. For example, the analysis unit can determine the order of analysis based on the submission date and time, and adjust the priority of analysis based on the submission frequency. This enables efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can perform analysis using an AI model that takes the data submission date analyzed by a generative AI as input and determines the priority of analysis.

[0042] The analysis unit can adjust the order of analysis according to the relationships between the data during the analysis. For example, the analysis unit can adjust the order of analysis based on the relationships between the data. For example, the analysis unit can prioritize the analysis of highly relevant data and postpone the analysis of less relevant data. The analysis unit can also adjust the order of analysis based on the relationships between the data. For example, the analysis unit can evaluate the relationships between the data using co-occurrence network analysis or correlation analysis and determine the order of analysis based on the evaluation results. This allows for efficient analysis by adjusting the order of analysis based on the relationships between the data. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can take the relationships between the data analyzed by generative AI as input and perform analysis using an AI model that adjusts the order of analysis.

[0043] The visualization unit can improve the accuracy of visualization based on the interrelationships between data during visualization. For example, the visualization unit analyzes the interrelationships between data and visualizes highly related data together. For example, the visualization unit adjusts the layout of the visualization based on the interrelationships between data. The visualization unit can also adjust the color and font of the visualization taking the interrelationships between data into consideration. For example, the visualization unit can evaluate the interrelationships between data using correlation analysis and improve the accuracy of the visualization based on the evaluation results. In this way, the accuracy of the visualization is improved by taking the interrelationships between data into consideration. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the visualization unit can perform visualization using an AI model that uses the interrelationships between data analyzed by the generation AI as input and improves the accuracy of the visualization.

[0044] The visualization unit can perform visualization based on the attribute information of the data submitter during visualization. For example, the visualization unit performs visualization taking into account the attribute information of the data submitter. For example, if the data submitter is a member of the management team, the visualization unit can provide a visualization that focuses on the key points. Furthermore, if the data submitter is a member of the marketing team, the visualization unit can provide a detailed visualization. Furthermore, the visualization unit can adjust the visualization method based on the attribute information of the data submitter. For example, the visualization unit can perform visualization taking into account the attribute information of the data submitter, such as age, occupation, and region. This enables more appropriate visualization by taking the attribute information of the data submitter into account. Some or all of the above-described processing in the visualization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the visualization unit can perform visualization using an AI model that performs visualization using the attribute information of the data submitter analyzed by the generation AI as input.

[0045] The visualization unit can perform visualization based on the geographic distribution of data during visualization. The visualization unit, for example, visualizes the geographic distribution of data on a map. For example, the visualization unit can display data by region on a map to show geographic clusters. The visualization unit can also adjust the color of the visualization based on the geographic distribution. Furthermore, the visualization unit can adjust the layout of the visualization taking the geographic distribution into consideration. For example, the visualization unit can display sales data by region on a map to visually show differences in sales between regions. This enables more appropriate visualization by taking the geographic distribution of data into consideration. Some or all of the above-described processing in the visualization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the visualization unit can perform visualization using an AI model that performs visualization using the geographic distribution of data analyzed by the generation AI as input.

[0046] During visualization, the visualization unit can improve the accuracy of the visualization based on literature related to the data. The visualization unit, for example, refers to literature related to the data to improve the accuracy of the visualization. For example, the visualization unit adjusts the color and font of the visualization based on the related literature. The visualization unit can also adjust the layout of the visualization taking the related literature into consideration. For example, the visualization unit can refer to academic papers and technical reports and improve the accuracy of the visualization based on their contents. In this way, the accuracy of the visualization is improved by referring to literature related to the data. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the visualization unit can perform visualization using an AI model that uses literature related to the data analyzed by the generation AI as input and improves the accuracy of the visualization.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The data visualization system can also include a customization section that learns user preferences based on the user's past visualization results. For example, the customization section can learn the fonts, colors, and layout patterns previously selected by the user and automatically apply them during subsequent visualizations. Furthermore, if the user prefers a specific data format, the customization section can prioritize suggesting that format. In addition, the customization section can collect user feedback and utilize it to improve the accuracy of visualizations. This enables visualizations tailored to user preferences, resulting in more satisfying results.

[0049] The visualization unit can update the data visualization results in real time. For example, when a user enters new data, the visualization unit can instantly reflect that data and update the visualization results. Furthermore, the visualization unit can automatically adjust the visualization results in response to data changes. In addition, if a user selects a specific data point, the visualization unit can highlight information related to that data point. This allows users to understand data changes in real time and make quick decisions.

[0050] The visualization section can include features that allow users to interactively manipulate the data visualization results. For example, users can click on a part of the visualization result to display detailed information. The visualization section can also provide a function that allows users to select a specific range of data and zoom in. Furthermore, the visualization section can include a filtering function that allows users to switch between different perspectives on the data. This allows users to gain a deeper understanding of the data and quickly obtain the information they need.

[0051] The visualization section can include features for sharing data visualization results. For example, it can provide a function to export visualization results as PDF or image files. It can also generate links for sharing visualization results on social media or via email. Furthermore, it can provide a function for collaborative editing of visualization results with other users in real time. This allows users to easily share visualization results and analyze data in cooperation with others.

[0052] The analysis unit can be equipped with data anomaly detection capabilities. For example, the analysis unit can detect abnormal patterns or values ​​in the data and notify the user. Furthermore, based on the anomaly detection results, the analysis unit can suggest data cleaning or correction. In addition, the analysis unit can continuously learn its anomaly detection algorithm to improve accuracy. This allows the user to maintain data quality and obtain reliable visualization results.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The input section is where users enter data. This data may include sales data, customer data, inventory data, etc. The input section can read data from common data sources such as Excel or CSV files. Users can also manually enter data, for example, through a web form. Step 2: The analysis unit is the part that analyzes the data input by the input unit using a generation AI. The analysis unit understands the content of the data and learns the company's unique data visualization techniques. The generation AI learns past data visualization patterns and adjustments to fonts and colors based on the company's brand guidelines, and applies these to new data. Step 3: The visualization part uses the generation AI to visualize the data analyzed by the analysis part. The visualization part converts sales data into graphs and charts, and customer data into infographics. The generation AI automatically adjusts fonts and colors to make the data visually appealing. Based on the content of the data, the optimal visualization method can be selected.

[0055] (Example 2) The data visualization system according to an embodiment of the present invention is a system that uses a generative AI to extract data from general data sources and visualize the results in an attractive manner. In this data visualization system, the user inputs data, the generative AI analyzes that data, and learns the company's unique data visualizations. The generative AI automatically adjusts fonts and colors to visualize the data attractively. This allows the user to entrust the data analysis and visualization process to the AI, maximizing the value that can be obtained from the data and effectively communicating the results to others. For example, the user inputs data. In this case, the user can easily input data regardless of the data format or content. For example, various types of data such as sales data and customer data can be input. This data is input to the generative AI. Next, the generative AI analyzes the input data. The generative AI understands the content of the data and learns the company's unique data visualizations. For example, it learns past data visualization patterns and font and color adjustments based on the company's brand guidelines. In this way, the generative AI accumulates knowledge for visualizing data attractively. Generative AI visualizes data in an engaging way based on its learned knowledge. For example, it can transform sales data into graphs and charts, and customer data into infographics. During this process, the generative AI automatically adjusts fonts and colors to make the data visually appealing. This allows users to delegate the data analysis and visualization process to the AI. This mechanism enables users to maximize the value they can derive from the data and effectively communicate the results to others. For instance, transforming sales data into compelling graphs allows for effective reporting to management. Similarly, transforming customer data into infographics allows for effective communication to the marketing team. Thus, data visualization systems allow users to delegate the data analysis and visualization process to AI, maximizing the value they can derive from the data and effectively communicating the results to others.

[0056] The data visualization system according to the embodiment includes an input unit, an analysis unit, and a visualization unit. The input unit allows a user to input data. Examples of data input by the user include, but are not limited to, sales data, customer data, and inventory data. The input unit can read data from common data sources, such as Excel files and CSV files. The input unit can also allow a user to manually input data. For example, a user can input data through a web form. The analysis unit uses a generation AI to analyze the data input by the input unit. For example, the analysis unit understands the content of the data and learns a company's unique data visualization. The generation AI learns patterns from past data visualizations and adjusts fonts and colors based on the company's brand guidelines. For example, the generation AI can learn visualization patterns from past sales data and apply similar patterns to new data. The generation AI can also automatically adjust fonts and colors based on the company's brand guidelines. The visualization unit uses a generation AI to visualize the data analyzed by the analysis unit. The visualization unit, for example, converts sales data into graphs and charts and customer data into infographics. The generation AI automatically adjusts fonts and colors to make the data visually appealing. For example, the generation AI can convert sales data into bar graphs and line graphs, and customer data into pie charts and heat maps. The generation AI can also select the optimal visualization method based on the content of the data. For example, a line graph can be selected to show the trend of sales data, and a heat map can be selected to show the distribution of customer data. This allows the data visualization system according to the embodiment to efficiently input, analyze, and visualize data. Some or all of the above-described processing in the visualization unit may be performed using, or without, the generation AI.For example, the visualization unit can perform visualization using an AI model that takes data analyzed by the generation AI as input and outputs visualization results.

[0057] The visualization unit can automatically adjust fonts and colors. The visualization unit can automatically adjust, for example, the font type, size, and color selection criteria. For example, the visualization unit can select appropriate fonts and colors based on the company's brand guidelines. The visualization unit can also select optimal fonts and colors based on the content of the data. For example, when visualizing sales data, it can select visually easy-to-understand fonts and colors, and when visualizing customer data, it can select visually appealing fonts and colors. This automatically adjusts fonts and colors, enabling visually appealing visualizations. Some or all of the above-described processing in the visualization unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the visualization unit can perform visualizations using an AI model that uses data analyzed by the generation AI as input and adjusts fonts and colors.

[0058] The visualization unit can convert the sales data into a graph or chart. For example, the visualization unit can convert the sales data into a graph or chart, such as a bar graph, line graph, or pie chart. For example, the visualization unit can select a line graph to show the trend of the sales data and a pie chart to show the distribution of the sales data. The visualization unit can also select a bar graph to compare sales data. For example, the visualization unit can convert monthly sales data into a bar graph and product sales data into a pie chart. This allows the sales data to be displayed in a visually easy-to-understand manner. Some or all of the above-mentioned processing in the visualization unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the visualization unit can perform visualization using an AI model that inputs sales data analyzed by the generation AI and converts it into a graph or chart.

[0059] The visualization unit can convert customer data into visual information diagrams. For example, the visualization unit can convert customer data into visual information diagrams such as infographics and heat maps. For example, the visualization unit can convert customer attribute information into infographics and customer purchase histories into heat maps. The visualization unit can also select a heat map to show the distribution of customer data and select an infographic to compare customer data. For example, the visualization unit can convert the age distribution of customers into infographics and customer purchase histories into heat maps. This allows customer data to be displayed in an easy-to-understand visual format. Some or all of the above-described processing in the visualization unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the visualization unit can perform visualization using an AI model that inputs customer data analyzed by the generation AI and converts it into a visual information diagram.

[0060] The analysis unit can analyze patterns of past data visualizations. For example, the analysis unit can learn patterns of past data visualizations and apply them to new data. For example, the analysis unit can learn visualization patterns of past sales data and apply similar patterns to new sales data. It can also learn visualization patterns of past customer data and apply similar patterns to new customer data. For example, the analysis unit can select a line graph to show trends in past sales data and apply similar patterns to new sales data. This allows for more effective visualization by learning patterns of past data visualizations. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can perform analysis using an AI model that takes past data visualization patterns analyzed by generative AI as input and applies them to new data.

[0061] The analysis unit can analyze font and color adjustments based on the company's brand guidelines. For example, the analysis unit can select appropriate fonts and colors based on the company's brand guidelines. For example, the analysis unit can learn the criteria for selecting font types, sizes, and colors based on the company's brand guidelines. The analysis unit can also select the optimal fonts and colors based on the content of the data. For example, when visualizing sales data, it can select fonts and colors that are visually easy to understand, and when visualizing customer data, it can select fonts and colors that are visually appealing. By learning font and color adjustments based on the company's brand guidelines, consistent visualization becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can take the company's brand guidelines analyzed by generative AI as input and perform analysis using an AI model that adjusts fonts and colors.

[0062] The input unit can determine the user's emotions and adjust the timing of data input based on the determined user emotions. The input unit estimates emotions using, for example, facial expression recognition or voice analysis of the user. For example, if the user is feeling stressed, the input unit can delay the timing of input to provide a relaxing environment. The input unit can also speed up the timing of input to enable efficient data input when the user is concentrating. Furthermore, if the user is tired, the input unit can adjust the timing of input and display a message encouraging the user to take a break. This enables efficient data input by adjusting the timing of data input according to the user's emotions. Some or all of the above-described processing in the input unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the input unit can input user emotion data analyzed by the generation AI and use an AI model to adjust the timing of data input.

[0063] The input unit can analyze the user's past data input history and select an appropriate input method. The input unit, for example, analyzes the user's past data input history and provides an optimal input method. For example, the input unit preferentially suggests input methods (such as voice input or text input) that the user has frequently used in the past. The input unit can also automatically select a specific data format from the user's past input history to simplify input. Furthermore, the input unit can analyze patterns of data previously input by the user and provide an optimal input template. In this way, the optimal input method can be provided by analyzing the user's past data input history. Some or all of the above-described processing in the input unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the input unit can input the user's past data input history analyzed by the generation AI and use an AI model that selects an optimal input method.

[0064] The input unit can perform filtering based on the user's current project and areas of interest. The input unit, for example, prioritizes displaying highly relevant data based on the user's current project and areas of interest. For example, the input unit can display only data related to the project the user is currently working on, thereby streamlining input. The input unit can also prioritize displaying highly relevant data based on the user's areas of interest. Furthermore, the input unit can filter input candidates based on data in which the user has previously shown interest. This enables efficient data input by filtering data based on the user's current project and areas of interest. Some or all of the above-described processing in the input unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the input unit can input the user's project data and area of ​​interest data analyzed by the generation AI, using an AI model that performs filtering.

[0065] The input unit can determine the user's emotions and prioritize the data to be input based on the determined user emotions. The input unit, for example, estimates emotions using facial expression recognition or voice analysis of the user. For example, when the user is feeling stressed, the input unit can postpone input of less important data and start inputting simpler data. The input unit can also prioritize input of more important data when the user is concentrating. Furthermore, when the user is tired, the input unit can adjust the priority to reduce the input burden. This enables efficient data input by prioritizing data according to the user's emotions. Some or all of the above-described processing in the input unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the input unit can input user emotion data analyzed by a generation AI and use an AI model that determines the priority of data.

[0066] When inputting data, the input unit can prioritize inputting highly relevant data based on the user's geographical location information. The input unit, for example, prioritizes inputting highly relevant data taking into account the user's geographical location information. For example, when the user is in a specific area, the input unit prioritizes inputting data related to that area. Furthermore, when the user is moving, the input unit can also suggest highly relevant data based on the user's current location. Furthermore, when the user is in a specific location, the input unit can automatically display data related to that location. This allows highly relevant data to be efficiently input by taking the user's geographical location information into account. Some or all of the above-described processing in the input unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the input unit can input the user's geographical location information analyzed by the generation AI and use an AI model that prioritizes inputting highly relevant data.

[0067] The input unit can analyze the user's social media activity and input relevant data when inputting data. For example, the input unit analyzes the user's social media activity and automatically inputs relevant data. For example, the input unit analyzes the user's social media posts and automatically inputs relevant data. The input unit can also suggest input candidates based on the user's social media interests. Furthermore, the input unit can prioritize input of highly relevant data based on the user's social media activity history. This allows highly relevant data to be efficiently input by analyzing the user's social media activity. Some or all of the above-described processing in the input unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the input unit can input the user's social media activity data analyzed by the generation AI and use an AI model to input related data.

[0068] The analysis unit can determine the user's emotions and adjust the way the analysis is presented based on the determined user's emotions. The analysis unit estimates emotions using, for example, facial expression recognition or voice analysis of the user. For example, the analysis unit can provide detailed analysis results when the user is relaxed. Furthermore, the analysis unit can provide concise analysis results that focus on the main points when the user is in a hurry. Furthermore, the analysis unit can provide visually stimulating analysis results when the user is excited. This allows for more appropriate analysis results to be provided by adjusting the way the analysis is presented according to the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can perform analysis using an AI model that inputs the user's emotion data analyzed by the generation AI and adjusts the way the analysis is presented.

[0069] The analysis unit can adjust the level of detail of the analysis according to the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis for highly important data and a concise analysis for less important data. The analysis unit can also adjust the depth of the analysis according to the importance of the data. For example, the analysis unit can perform a detailed analysis for data with high business impact and a concise analysis for data with high novelty. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can take the importance of the data analyzed by generative AI as input and perform the analysis using an AI model that adjusts the level of detail of the analysis.

[0070] The analysis unit can apply different analysis algorithms according to the data category during analysis. For example, the analysis unit can select an appropriate analysis algorithm according to the data category. For example, the analysis unit can apply a sales forecasting algorithm to sales data and a customer segmentation algorithm to customer data. The analysis unit can also apply an inventory optimization algorithm to inventory data. For example, the analysis unit can apply a sales forecasting algorithm to predict sales data and a customer segmentation algorithm to analyze customer data. By applying different analysis algorithms according to the data category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can perform analysis using an AI model that takes the data categories analyzed by generative AI as input and applies an appropriate analysis algorithm.

[0071] The analysis unit can determine the user's emotions and adjust the length of the analysis based on the determined user emotions. The analysis unit, for example, estimates emotions using facial expression recognition or voice analysis of the user. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can also provide a visually stimulating analysis result. This allows for adjusting the length of the analysis according to the user's emotions to provide more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can perform analysis using an AI model that inputs the user's emotion data analyzed by the generation AI and adjusts the length of the analysis.

[0072] The analysis unit can determine the priority of analysis based on the data submission date during the analysis. For example, the analysis unit can determine the priority of analysis based on the data submission date. For example, the analysis unit can prioritize the analysis of recently submitted data and postpone the analysis of older data. The analysis unit can also adjust the order of analysis based on the submission date. For example, the analysis unit can determine the order of analysis based on the submission date and time, and adjust the priority of analysis based on the submission frequency. This enables efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can perform analysis using an AI model that takes the data submission date analyzed by a generative AI as input and determines the priority of analysis.

[0073] The analysis unit can adjust the order of analysis according to the relationships between the data during the analysis. For example, the analysis unit can adjust the order of analysis based on the relationships between the data. For example, the analysis unit can prioritize the analysis of highly relevant data and postpone the analysis of less relevant data. The analysis unit can also adjust the order of analysis based on the relationships between the data. For example, the analysis unit can evaluate the relationships between the data using co-occurrence network analysis or correlation analysis and determine the order of analysis based on the evaluation results. This allows for efficient analysis by adjusting the order of analysis based on the relationships between the data. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can take the relationships between the data analyzed by generative AI as input and perform analysis using an AI model that adjusts the order of analysis.

[0074] The visualization unit can determine the user's emotion and adjust the visualization method based on the determined user's emotion. The visualization unit can estimate the emotion using, for example, facial expression recognition or voice analysis of the user. For example, the visualization unit can provide a detailed visualization when the user is relaxed. The visualization unit can also provide a concise and to-the-point visualization when the user is in a hurry. Furthermore, the visualization unit can provide a visually stimulating visualization when the user is excited. This allows for more appropriate visualization by adjusting the visualization method according to the user's emotion. Some or all of the above-described processing in the visualization unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the visualization unit can perform visualization using an AI model that inputs the user's emotion data analyzed by the generation AI and adjusts the visualization method.

[0075] The visualization unit can improve the accuracy of visualization based on the interrelationships between data during visualization. For example, the visualization unit analyzes the interrelationships between data and visualizes highly related data together. For example, the visualization unit adjusts the layout of the visualization based on the interrelationships between data. The visualization unit can also adjust the color and font of the visualization taking the interrelationships between data into consideration. For example, the visualization unit can evaluate the interrelationships between data using correlation analysis and improve the accuracy of the visualization based on the evaluation results. In this way, the accuracy of the visualization is improved by taking the interrelationships between data into consideration. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the visualization unit can perform visualization using an AI model that uses the interrelationships between data analyzed by the generation AI as input and improves the accuracy of the visualization.

[0076] The visualization unit can perform visualization based on the attribute information of the data submitter during visualization. For example, the visualization unit performs visualization taking into account the attribute information of the data submitter. For example, if the data submitter is a member of the management team, the visualization unit can provide a visualization that focuses on the key points. Furthermore, if the data submitter is a member of the marketing team, the visualization unit can provide a detailed visualization. Furthermore, the visualization unit can adjust the visualization method based on the attribute information of the data submitter. For example, the visualization unit can perform visualization taking into account the attribute information of the data submitter, such as age, occupation, and region. This enables more appropriate visualization by taking the attribute information of the data submitter into account. Some or all of the above-described processing in the visualization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the visualization unit can perform visualization using an AI model that performs visualization using the attribute information of the data submitter analyzed by the generation AI as input.

[0077] The visualization unit can determine the user's emotions and adjust the order in which the visualization results are displayed based on the determined emotions. The visualization unit estimates emotions using, for example, facial recognition or voice analysis of the user. For example, if the user is relaxed, the visualization unit can display detailed visualization results first. Also, if the user is in a hurry, the visualization unit can display concise visualization results first. Furthermore, if the user is excited, the visualization unit can display visually stimulating visualization results first. By adjusting the order in which the visualization results are displayed according to the user's emotions, more appropriate visualization becomes possible. Some or all of the above processing in the visualization unit may be performed using, for example, generative AI, or without generative AI. For example, the visualization unit can perform visualization using an AI model that takes user emotion data analyzed by generative AI as input and adjusts the order in which the visualization results are displayed.

[0078] The visualization unit can perform visualization based on the geographic distribution of data during visualization. The visualization unit, for example, visualizes the geographic distribution of data on a map. For example, the visualization unit can display data by region on a map to show geographic clusters. The visualization unit can also adjust the color of the visualization based on the geographic distribution. Furthermore, the visualization unit can adjust the layout of the visualization taking the geographic distribution into consideration. For example, the visualization unit can display sales data by region on a map to visually show differences in sales between regions. This enables more appropriate visualization by taking the geographic distribution of data into consideration. Some or all of the above-described processing in the visualization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the visualization unit can perform visualization using an AI model that performs visualization using the geographic distribution of data analyzed by the generation AI as input.

[0079] During visualization, the visualization unit can improve the accuracy of the visualization based on literature related to the data. The visualization unit, for example, refers to literature related to the data to improve the accuracy of the visualization. For example, the visualization unit adjusts the color and font of the visualization based on the related literature. The visualization unit can also adjust the layout of the visualization taking the related literature into consideration. For example, the visualization unit can refer to academic papers and technical reports and improve the accuracy of the visualization based on their contents. In this way, the accuracy of the visualization is improved by referring to literature related to the data. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the visualization unit can perform visualization using an AI model that uses literature related to the data analyzed by the generation AI as input and improves the accuracy of the visualization. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned input unit, analysis unit, and visualization unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit allows a user to input data using the reception device 38 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input data using a generation AI. The visualization unit can visually display the analyzed data using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned input unit, analysis unit, and visualization unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the input unit allows a user to input data by voice using the microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input data using a generative AI. The visualization unit can visually display the analyzed data using the display of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, analysis unit, and visualization unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the input unit allows a user to input data by voice using the microphone 238 of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input data using a generation AI. The visualization unit can visually display the analyzed data using the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, analysis unit, and visualization unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit allows a user to input data by voice using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input data using a generative AI. The visualization unit can visually display the analyzed data using the display of the robot 414.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The data visualization system can also include a customization section that learns user preferences based on the user's past visualization results. For example, the customization section can learn the fonts, colors, and layout patterns previously selected by the user and automatically apply them during subsequent visualizations. Furthermore, if the user prefers a specific data format, the customization section can prioritize suggesting that format. In addition, the customization section can collect user feedback and utilize it to improve the accuracy of visualizations. This enables visualizations tailored to user preferences, resulting in more satisfying results.

[0082] The visualization unit can update the data visualization results in real time. For example, when a user enters new data, the visualization unit can instantly reflect that data and update the visualization results. Furthermore, the visualization unit can automatically adjust the visualization results in response to data changes. In addition, if a user selects a specific data point, the visualization unit can highlight information related to that data point. This allows users to understand data changes in real time and make quick decisions.

[0083] The visualization section can include features that allow users to interactively manipulate the data visualization results. For example, users can click on a part of the visualization result to display detailed information. The visualization section can also provide a function that allows users to select a specific range of data and zoom in. Furthermore, the visualization section can include a filtering function that allows users to switch between different perspectives on the data. This allows users to gain a deeper understanding of the data and quickly obtain the information they need.

[0084] The visualization section can include features for sharing data visualization results. For example, it can provide a function to export visualization results as PDF or image files. It can also generate links for sharing visualization results on social media or via email. Furthermore, it can provide a function for collaborative editing of visualization results with other users in real time. This allows users to easily share visualization results and analyze data in cooperation with others.

[0085] The analysis unit can be equipped with data anomaly detection capabilities. For example, the analysis unit can detect abnormal patterns or values ​​in the data and notify the user. Furthermore, based on the anomaly detection results, the analysis unit can suggest data cleaning or correction. In addition, the analysis unit can continuously learn its anomaly detection algorithm to improve accuracy. This allows the user to maintain data quality and obtain reliable visualization results.

[0086] The analysis unit can determine the user's emotions and adjust the analysis priority based on those emotions. For example, if the user is stressed, the analysis unit can postpone less important analyses and start with simpler ones. Conversely, if the user is focused, the analysis unit can prioritize more important analyses. Furthermore, if the user is tired, the analysis unit can adjust priorities to reduce the analysis burden. By adjusting the analysis priority according to the user's emotions, efficient analysis becomes possible.

[0087] The visualization unit can determine the user's emotion and adjust the style of the visualization based on the determined user's emotion. For example, the visualization unit can provide a colorful and detailed visualization if the user is relaxed. The visualization unit can also provide a simple and to-the-point visualization if the user is in a hurry. Furthermore, the visualization unit can provide a visually stimulating visualization if the user is excited. This allows for more appropriate visualization by adjusting the style of the visualization according to the user's emotion.

[0088] The input unit can determine the user's emotion and adjust the input interface based on the determined user's emotion. For example, the input unit can provide a simple and intuitive interface when the user is stressed. The input unit can also provide detailed input options when the user is concentrating. Furthermore, the input unit can simplify the interface to reduce the input burden when the user is tired. This allows for efficient data input by adjusting the input interface according to the user's emotion.

[0089] The analysis unit can determine the user's emotions and adjust the display method of the analysis results based on the determined user's emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. Alternatively, the analysis unit can provide concise analysis results that focus on the main points when the user is in a hurry. Furthermore, the analysis unit can provide visually stimulating analysis results when the user is excited. In this way, by adjusting the display method of the analysis results according to the user's emotions, more appropriate analysis results can be provided.

[0090] The visualization unit can determine the user's emotion and adjust the order in which visualization results are displayed based on the determined user's emotion. For example, if the user is relaxed, the visualization unit can first display detailed visualization results. If the user is in a hurry, the visualization unit can also first display visualization results that focus on the main points. Furthermore, if the user is excited, the visualization unit can first display visually stimulating visualization results. In this way, by adjusting the order in which visualization results are displayed according to the user's emotion, more appropriate visualization is possible.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The input section is where users enter data. This data may include sales data, customer data, inventory data, etc. The input section can read data from common data sources such as Excel or CSV files. Users can also manually enter data, for example, through a web form. Step 2: The analysis unit is the part that analyzes the data input by the input unit using a generation AI. The analysis unit understands the content of the data and learns the company's unique data visualization techniques. The generation AI learns past data visualization patterns and adjustments to fonts and colors based on the company's brand guidelines, and applies these to new data. Step 3: The visualization part uses the generation AI to visualize the data analyzed by the analysis part. The visualization part converts sales data into graphs and charts, and customer data into infographics. The generation AI automatically adjusts fonts and colors to make the data visually appealing. Based on the content of the data, the optimal visualization method can be selected.

[0093] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0111] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0127] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 7, a 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.

[0131] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0137] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0139] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0143] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0147] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0148] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0149] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0150] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0151] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0152] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0153] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0154] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0155] 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.

[0156] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0157] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0158] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0159] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0160] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0161] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0162] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0163] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0164] [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input section for inputting data; an analysis unit that analyzes the data input by the input unit; a visualization unit that visualizes the data analyzed by the analysis unit; Equipped with A system characterized by:

2. The visualization unit Automatically adjust fonts and colors 2. The system of claim 1.

3. The visualization unit Convert sales data into a graph or chart 2. The system of claim 1.

4. The visualization unit Transform customer data into visual infographics 2. The system of claim 1.

5. The analysis unit Analyzing patterns in past data visualizations 2. The system of claim 1.

6. The analysis unit Analyze font and color adjustments based on company brand guidelines 2. The system of claim 1.

7. The input unit Determine the user's emotions and adjust the timing of data input based on the determined user emotions.

2. The system of claim 1.

8. The input unit Analyze the user's past data entry history and select the appropriate entry method 2. The system of claim 1.

Images