System

Abstract

An object of the system according to the embodiment is to automatically generate a report with an appropriate context and structure based on data and comments.SOLUTION: A system according to an embodiment includes a data input unit, a data analysis unit, a report generation unit, and a visual data generation unit. The data input unit inputs data and comments from the user. The data analysis unit analyzes the data input by the data input unit. The report generator generates a report in an appropriate context and structure based on the data analyzed by the data analyzer. The visual data generation unit generates visual data such as an image, a statistical graph, and a heat map associated with the report generated by the report generation unit.SELECTED DRAWING: Figure 1

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 have the drawback of making it difficult to generate reports with the appropriate context and structure based on data and comments, making it time-consuming and difficult to do efficiently.

[0005] The system according to the embodiment aims to automatically generate reports with appropriate context and structure based on data and comments. [Means for solving the problem]

[0006] The system according to the embodiment includes a data input unit, a data analysis unit, a report generation unit, and a visual data generation unit. The data input unit inputs data and comments from a user. The data analysis unit analyzes the data input by the data input unit. The report generation unit generates a report with an appropriate context and structure based on the data analyzed by the data analysis unit. The visual data generation unit generates visual data such as images, statistical graphs, and heat maps to accompany the report generated by the report generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate reports with the appropriate context and structure based on the data and comments. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) An automatic report generation system according to an embodiment of the present invention is a system in which a generation AI automatically generates a report with an appropriate context and structure based on data and comments entered by a user, and also generates visual data such as images, statistical graphs, heat maps, etc. As a result, the automatic report generation system automatically generates a report based on the user's data and comments, and by including visual data, the report becomes easier to visually understand.

[0029] An automatic report generation system according to an embodiment includes a data input unit, a data analysis unit, a report generation unit, and a visual data generation unit. The data input unit inputs data and comments from a user. For example, text data, audio data, feedback comments, and the like can be input. The data analysis unit analyzes the data input by the data input unit. For example, the data analysis unit analyzes the data using statistical analysis, natural language processing, or a machine learning algorithm. The report generation unit generates a report with an appropriate context and structure based on the data analyzed by the data analysis unit. For example, the report is created taking into consideration the report's chapter structure, information priority, and grammatical accuracy. The visual data generation unit generates visual data such as images, statistical graphs, and heat maps to accompany the report generated by the report generation unit. For example, it generates a line graph showing sales trends, a heat map showing participant distribution, and a pie chart showing survey results. As a result, the automatic report generation system according to an embodiment automatically generates a report based on user data and comments, and by including visual data, the report is easier to visually understand.

[0030] The data input unit supports voice input and can automatically convert it into text data using voice recognition technology. The data input unit, for example, allows a user to input data by voice and automatically converts it into text data using voice recognition technology. For example, minutes of a meeting can be recorded by voice input and saved as text data. Supporting voice input thus makes data input more efficient.

[0031] The data analysis unit can compare data from past promotions and contests to extract trends and patterns. For example, the data analysis unit analyzes data from past promotions and contests and compares it with current data to extract trends and patterns. For example, it compares past sales data with current sales data to analyze trends in sales increases and decreases. This makes it easier to understand trends and patterns by comparing with past data.

[0032] The data analysis unit can integrate data from different data sources and perform more comprehensive analysis. For example, the data analysis unit collects, integrates, and analyzes data from social media and external databases. For example, it collects data from posts on Twitter and Facebook and evaluates the effectiveness of promotions. This allows for more comprehensive analysis by integrating data from different data sources.

[0033] The data input unit can also analyze image and video data and reflect visual information in the report. The data input unit, for example, analyzes image and video data when inputting data and reflects visual information in the report. For example, it analyzes promotional photos and videos and includes them in the report. In this way, by analyzing image and video data, visual information can be reflected in the report.

[0034] The report generation unit uses natural language generation technology to generate reports in different writing styles and tones, allowing for customization to suit the reader's preferences. The report generation unit, for example, uses natural language generation technology to generate reports in different writing styles and tones, allowing for customization to suit the reader's preferences. For example, reports can be created in a formal style or a casual style. This allows for customization to suit the reader's preferences.

[0035] The report generation unit can cite related external data when generating a report to improve the reliability of the report. For example, the report generation unit automatically collects and cites related industry news and market data when generating a report. For example, the latest market trends and industry news are reflected in the report. In this way, citing external data improves the reliability of the report.

[0036] The report generation unit supports automatic report generation in multiple languages, allowing reports to be created from an international perspective. The report generation unit, for example, supports automatic report generation in multiple languages ​​and creates reports in different languages. For example, reports are created in multiple languages, such as English, French, and Chinese. This makes it possible to create reports from an international perspective through multilingual support.

[0037] The report generation unit can add interactive elements when generating a report to increase reader engagement. For example, the report generation unit adds interactive elements such as clickable links and embedded videos when generating a report. For example, links to related websites and videos can be embedded in the report. This increases reader engagement by adding interactive elements.

[0038] The visual data generation unit can incorporate 3D graphics and animations when generating visual data to enhance the visual impact. For example, the visual data generation unit can incorporate 3D graphics when generating visual data to enhance the visual impact. For example, sales data can be displayed in a 3D graph. In this way, the incorporation of 3D graphics and animations enhances the visual impact.

[0039] The visual data generation unit can create infographics that visually show correlations and causal relationships in the data when generating the visual data. For example, the visual data generation unit creates infographics that visually show correlations and causal relationships in the data when generating the visual data. For example, it creates a graph that shows the correlation between sales and advertising expenses. This visually shows correlations and causal relationships in the data, thereby deepening understanding of the report.

[0040] When generating visual data, the visual data generation unit can output the data in a format optimized for different devices. For example, when generating visual data, the visual data generation unit outputs the data in a format optimized for smartphones and tablets. For example, the visual data generation unit employs a responsive design to accommodate different screen sizes. This allows the data to be output in a format optimized for different devices, allowing users to comfortably view the data on any device.

[0041] The visual data generation unit provides a user-customizable template when generating visual data, thereby enabling individual needs to be met. The visual data generation unit, for example, provides a user-customizable template when generating visual data. For example, a template is prepared that allows the color, font, and layout to be freely changed. This allows individual needs to be met by providing a user-customizable template.

[0042] When customizing a report, the report generation unit can refer to the user's past customization history and propose the optimal customization. For example, when customizing a report, the report generation unit can refer to the user's past customization history and propose the optimal customization. For example, the report generation unit makes a proposal based on formats and designs used in the past. In this way, by referring to the user's past customization history, the optimal customization can be proposed.

[0043] The report generation unit can display a preview in real time when customizing a report, allowing the user to immediately check the changes. For example, the report generation unit can display a preview in real time when customizing a report, allowing the user to immediately check the changes. For example, a preview screen is provided in which the changes are immediately reflected. This allows the user to immediately check the changes by displaying the preview in real time.

[0044] The report generation unit can customize reports by providing templates that correspond to different industries and applications, thereby meeting diverse needs. For example, the report generation unit can customize reports by providing templates that correspond to different industries and applications, thereby meeting diverse needs. For example, templates for the medical industry and the education industry can be prepared. This allows the provision of templates that correspond to different industries and applications to meet diverse needs.

[0045] The report generation unit can add a function that allows users to edit collaboratively when customizing a report, thereby making team work more efficient. The report generation unit can add a function that allows users to edit collaboratively when customizing a report, thereby making team work more efficient. For example, the report generation unit can provide an interface that allows multiple users to edit simultaneously. By adding a function that allows users to edit collaboratively, team work can be made more efficient.

[0046] The report generation unit can transmit a report in a format optimized for the device or platform of the destination when distributing the report. For example, the report generation unit transmits a report in a format optimized for the device or platform of the destination when distributing the report. For example, the report generation unit distributes a report optimized for a smartphone or tablet. By transmitting the report in a format optimized for the device or platform of the destination, the recipient can comfortably view the report.

[0047] The report generation unit can automatically collect feedback from recipients after distributing a report and reflect it in the next distribution. The report generation unit, for example, builds a system that automatically collects feedback from recipients after distributing a report and reflects it in the next distribution. For example, it automatically collects ratings and comments from recipients. This automatically collects feedback from recipients and reflects it in the next distribution, thereby improving the quality of the report.

[0048] The report generation unit can also support report distribution via different communication channels. For example, the report generation unit can support report distribution via different communication channels such as social media and chat apps. For example, the report can be distributed via Twitter or Slack. This allows the report to be distributed via different communication channels, thereby expanding the scope of report distribution.

[0049] The report generation unit can automatically update the list of recipients and distribute reports based on the latest information. The report generation unit, for example, automatically updates the list of recipients and builds a system that distributes reports based on the latest information. For example, the report generation unit automatically distributes reports to newly added recipients. In this way, by automatically updating the recipient list, reports can be distributed based on the latest information.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The data analysis unit can learn from the user's past input data and predict future input data. For example, it can predict next month's sales based on past sales data. It can also analyze past feedback comments to predict the success rate of the next promotion. This allows users to understand future trends and patterns in advance and develop strategies.

[0052] The data input section supports handwritten input and can automatically convert handwritten characters into text data. For example, when taking handwritten notes during a meeting, the notes can be automatically saved as text data. Handwritten diagrams and graphs can also be analyzed and reflected in reports in digital format. This support for handwritten input increases the flexibility of data input.

[0053] The data analysis unit can automatically translate, integrate, and analyze data entered in different languages. For example, it can automatically translate, integrate, and analyze comments entered in different languages, such as English, Japanese, and French. It can also extract trends and patterns in different languages ​​and perform analysis from a global perspective. This allows for more comprehensive analysis by integrating data in different languages.

[0054] The data input unit supports gesture input, allowing users to input data using hand movements. For example, it can recognize specific gestures and input corresponding text or commands. It can also use gestures to draw graphs and diagrams. Supporting gesture input thus diversifies data input methods and improves user convenience.

[0055] The report generator can highlight keywords specified by the user when automatically generating a report. For example, it can highlight specific product or project names. It can also change colors and fonts to highlight important data and statistical information. By highlighting keywords specified by the user, important information in the report can be seen at a glance.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The data input section inputs data and comments from the user. For example, text data, voice data, feedback comments, etc. can be input. Step 2: The data analysis unit analyzes the data input by the data input unit, for example, by using statistical analysis, natural language processing, or machine learning algorithms. Step 3: The report generator generates a report with the appropriate context and structure based on the data analyzed by the data analyzer, taking into consideration, for example, the chapter structure, priority of information, and grammatical accuracy. Step 4: The visual data generation unit generates visual data such as images, statistical graphs, and heat maps to accompany the report generated by the report generation unit. For example, it generates a line graph showing sales trends, a heat map showing the distribution of participants, and a pie chart showing survey results.

[0058] (Example 2) An automatic report generation system according to an embodiment of the present invention is a system in which a generation AI automatically generates a report with an appropriate context and structure based on data and comments entered by a user, and also generates visual data such as images, statistical graphs, heat maps, etc. As a result, the automatic report generation system automatically generates a report based on the user's data and comments, and by including visual data, the report becomes easier to visually understand.

[0059] An automatic report generation system according to an embodiment includes a data input unit, a data analysis unit, a report generation unit, and a visual data generation unit. The data input unit inputs data and comments from a user. For example, text data, audio data, feedback comments, and the like can be input. The data analysis unit analyzes the data input by the data input unit. For example, the data analysis unit analyzes the data using statistical analysis, natural language processing, or a machine learning algorithm. The report generation unit generates a report with an appropriate context and structure based on the data analyzed by the data analysis unit. For example, the report is created taking into consideration the report's chapter structure, information priority, and grammatical accuracy. The visual data generation unit generates visual data such as images, statistical graphs, and heat maps to accompany the report generated by the report generation unit. For example, it generates a line graph showing sales trends, a heat map showing participant distribution, and a pie chart showing survey results. As a result, the automatic report generation system according to an embodiment automatically generates a report based on user data and comments, and by including visual data, the report is easier to visually understand.

[0060] The data analysis unit can use the emotion estimation function to analyze the emotions of the comments entered and highlight positive comments. For example, the data analysis unit performs emotion analysis on comments entered by users and highlights comments with positive emotions. For example, comments that express joy or satisfaction are displayed in different colors. This highlights positive comments, making the content of the report more appealing.

[0061] The data input unit supports voice input and can automatically convert it into text data using voice recognition technology. The data input unit, for example, allows a user to input data by voice and automatically converts it into text data using voice recognition technology. For example, minutes of a meeting can be recorded by voice input and saved as text data. Supporting voice input thus makes data input more efficient.

[0062] The data analysis unit can compare data from past promotions and contests to extract trends and patterns. For example, the data analysis unit analyzes data from past promotions and contests and compares it with current data to extract trends and patterns. For example, it compares past sales data with current sales data to analyze trends in sales increases and decreases. This makes it easier to understand trends and patterns by comparing with past data.

[0063] The data analysis unit can integrate data from different data sources and perform more comprehensive analysis. For example, the data analysis unit collects, integrates, and analyzes data from social media and external databases. For example, it collects data from posts on Twitter and Facebook and evaluates the effectiveness of promotions. This allows for more comprehensive analysis by integrating data from different data sources.

[0064] The data input unit can also analyze image and video data and reflect visual information in the report. The data input unit, for example, analyzes image and video data when inputting data and reflects visual information in the report. For example, it analyzes promotional photos and videos and includes them in the report. In this way, by analyzing image and video data, visual information can be reflected in the report.

[0065] The data analysis unit can use the emotion estimation function to analyze the emotion of the user when entering data in real time and make suggestions to optimize the input content. For example, when a user enters data, the data analysis unit uses the emotion estimation function to analyze the emotion in real time and make suggestions to optimize the input content. For example, if a negative emotion is detected, the data analysis unit will suggest a positive expression. This allows the user's emotion to be analyzed in real time and suggestions to optimize the input content to enable more effective report creation.

[0066] The report generation unit can use the emotion estimation function to evaluate the emotional impact that the report content has on the reader and emphasize positive expressions. For example, the report generation unit performs emotion analysis on the report content and emphasizes positive expressions. For example, it highlights success stories and positive feedback. In this way, by emphasizing positive expressions, the impact that the report content has on the reader is improved.

[0067] The report generation unit uses natural language generation technology to generate reports in different writing styles and tones, allowing for customization to suit the reader's preferences. The report generation unit, for example, uses natural language generation technology to generate reports in different writing styles and tones, allowing for customization to suit the reader's preferences. For example, reports can be created in a formal style or a casual style. This allows for customization to suit the reader's preferences.

[0068] The report generation unit can cite related external data when generating a report to improve the reliability of the report. For example, the report generation unit automatically collects and cites related industry news and market data when generating a report. For example, the latest market trends and industry news are reflected in the report. In this way, citing external data improves the reliability of the report.

[0069] The report generation unit supports automatic report generation in multiple languages, allowing reports to be created from an international perspective. The report generation unit, for example, supports automatic report generation in multiple languages ​​and creates reports in different languages. For example, reports are created in multiple languages, such as English, French, and Chinese. This makes it possible to create reports from an international perspective through multilingual support.

[0070] The report generation unit can add interactive elements when generating a report to increase reader engagement. For example, the report generation unit adds interactive elements such as clickable links and embedded videos when generating a report. For example, links to related websites and videos can be embedded in the report. This increases reader engagement by adding interactive elements.

[0071] The report generation unit can use the emotion estimation function to provide feedback on the emotional impact of the report content on the reader in real time and select the optimal expression. The report generation unit, for example, uses the emotion estimation function to provide feedback on the emotional impact of the report content on the reader in real time. For example, the report generation unit selects the optimal expression based on the reader's emotion score. This improves the effectiveness of the report by selecting the optimal expression based on the reader's emotion.

[0072] The visual data generation unit can use the emotion estimation function to evaluate the emotional impact of the visual data on the reader and emphasize positive visuals. For example, the visual data generation unit performs emotion analysis on the visual data and emphasizes visuals that evoke positive emotions. For example, it uses bright colors and positive images. This enhances the visual effect of the report by emphasizing positive visuals.

[0073] The visual data generation unit can incorporate 3D graphics and animations when generating visual data to enhance the visual impact. For example, the visual data generation unit can incorporate 3D graphics when generating visual data to enhance the visual impact. For example, sales data can be displayed in a 3D graph. In this way, the incorporation of 3D graphics and animations enhances the visual impact.

[0074] The visual data generation unit can create infographics that visually show correlations and causal relationships in the data when generating the visual data. For example, the visual data generation unit creates infographics that visually show correlations and causal relationships in the data when generating the visual data. For example, it creates a graph that shows the correlation between sales and advertising expenses. This visually shows correlations and causal relationships in the data, thereby deepening understanding of the report.

[0075] When generating visual data, the visual data generation unit can output the data in a format optimized for different devices. For example, when generating visual data, the visual data generation unit outputs the data in a format optimized for smartphones and tablets. For example, the visual data generation unit employs a responsive design to accommodate different screen sizes. This allows the data to be output in a format optimized for different devices, allowing users to comfortably view the data on any device.

[0076] The visual data generation unit provides a user-customizable template when generating visual data, thereby enabling individual needs to be met. The visual data generation unit, for example, provides a user-customizable template when generating visual data. For example, a template is prepared that allows the color, font, and layout to be freely changed. This allows individual needs to be met by providing a user-customizable template.

[0077] The visual data generation unit uses the emotion estimation function to provide feedback on the emotional impact of the visual data on the reader in real time, and can select an optimal design. The visual data generation unit, for example, uses the emotion estimation function to provide feedback on the emotional impact of the visual data on the reader in real time. For example, it selects an optimal design based on the reader's emotion score. This improves the effectiveness of the visual data by selecting an optimal design based on the reader's emotion.

[0078] The report generation unit can use the emotion estimation function to make customization suggestions based on the user's emotions and create a report that elicits positive emotions. The report generation unit, for example, uses the emotion estimation function to make customization suggestions based on the user's emotions and create a report that elicits positive emotions. For example, the report generation unit suggests optimal expressions based on the user's emotion score. This makes it possible to create a report that elicits positive emotions by making customization suggestions based on the user's emotions.

[0079] When customizing a report, the report generation unit can refer to the user's past customization history and propose the optimal customization. For example, when customizing a report, the report generation unit can refer to the user's past customization history and propose the optimal customization. For example, the report generation unit makes a proposal based on formats and designs used in the past. In this way, by referring to the user's past customization history, the optimal customization can be proposed.

[0080] The report generation unit can display a preview in real time when customizing a report, allowing the user to immediately check the changes. For example, the report generation unit can display a preview in real time when customizing a report, allowing the user to immediately check the changes. For example, a preview screen is provided in which the changes are immediately reflected. This allows the user to immediately check the changes by displaying the preview in real time.

[0081] The report generation unit can customize reports by providing templates that correspond to different industries and applications, thereby meeting diverse needs. For example, the report generation unit can customize reports by providing templates that correspond to different industries and applications, thereby meeting diverse needs. For example, templates for the medical industry and the education industry can be prepared. This allows the provision of templates that correspond to different industries and applications to meet diverse needs.

[0082] The report generation unit can add a function that allows users to edit collaboratively when customizing a report, thereby making team work more efficient. The report generation unit can add a function that allows users to edit collaboratively when customizing a report, thereby making team work more efficient. For example, the report generation unit can provide an interface that allows multiple users to edit simultaneously. By adding a function that allows users to edit collaboratively, team work can be made more efficient.

[0083] The report generation unit can use the emotion estimation function to make customization suggestions based on the user's emotions in real time and create an optimal report. The report generation unit, for example, uses the emotion estimation function to make customization suggestions based on the user's emotions in real time and create an optimal report. For example, the report generation unit suggests optimal expressions based on the user's emotion score. This makes it possible to make an optimal report by making customization suggestions based on the user's emotions in real time.

[0084] The report generation unit uses the emotion estimation function to optimize delivery timing based on the recipient's emotions, thereby eliciting a positive response. The report generation unit, for example, uses the emotion estimation function to analyze the recipient's emotional state in real time and determine the optimal delivery timing. For example, the report is delivered during a time period when the recipient is feeling positive emotions. In this way, by optimizing the delivery timing based on the recipient's emotions, a positive response can be elicited.

[0085] The report generation unit can transmit a report in a format optimized for the device or platform of the destination when distributing the report. For example, the report generation unit transmits a report in a format optimized for the device or platform of the destination when distributing the report. For example, the report generation unit distributes a report optimized for a smartphone or tablet. By transmitting the report in a format optimized for the device or platform of the destination, the recipient can comfortably view the report.

[0086] The report generation unit can automatically collect feedback from recipients after distributing a report and reflect it in the next distribution. The report generation unit, for example, builds a system that automatically collects feedback from recipients after distributing a report and reflects it in the next distribution. For example, it automatically collects ratings and comments from recipients. This automatically collects feedback from recipients and reflects it in the next distribution, thereby improving the quality of the report.

[0087] The report generation unit can also support report distribution via different communication channels. For example, the report generation unit can support report distribution via different communication channels such as social media and chat apps. For example, the report can be distributed via Twitter or Slack. This allows the report to be distributed via different communication channels, thereby expanding the scope of report distribution.

[0088] The report generation unit can automatically update the list of recipients and distribute reports based on the latest information. The report generation unit, for example, automatically updates the list of recipients and builds a system that distributes reports based on the latest information. For example, the report generation unit automatically distributes reports to newly added recipients. In this way, by automatically updating the recipient list, reports can be distributed based on the latest information.

[0089] The report generation unit uses the emotion estimation function to optimize the timing of delivery based on the emotions of the recipient in real time, and can deliver the report at the optimal timing. The report generation unit, for example, uses the emotion estimation function to analyze the emotional state of the recipient in real time and determine the optimal timing of delivery. For example, the report is delivered during a time period when the recipient is feeling positive emotions. In this way, the delivery timing based on the emotions of the recipient is optimized in real time, and the report can be delivered at the optimal timing.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The data analysis unit can learn from the user's past input data and predict future input data. For example, it can predict next month's sales based on past sales data. It can also analyze past feedback comments to predict the success rate of the next promotion. This allows users to understand future trends and patterns in advance and develop strategies.

[0092] The data analysis unit can use the emotion estimation function to estimate the stress level based on the user's input data and provide appropriate advice. For example, if the user enters comments indicating high stress, the system will suggest ways to relax or take a break. If there are many comments indicating positive emotions, the system will provide advice to further increase motivation. This makes it possible to provide appropriate support according to the user's emotional state.

[0093] The data input section supports handwritten input and can automatically convert handwritten characters into text data. For example, when taking handwritten notes during a meeting, the notes can be automatically saved as text data. Handwritten diagrams and graphs can also be analyzed and reflected in reports in digital format. This support for handwritten input increases the flexibility of data input.

[0094] The data analysis unit can use the emotion estimation function to recommend music and videos based on the user's emotions. For example, if a user enters a comment expressing negative emotions, it can recommend relaxing music or encouraging videos. On the other hand, if there are many comments expressing positive emotions, it can provide content to further uplift the mood. This makes it possible to provide entertainment that matches the user's emotions.

[0095] The data analysis unit can automatically translate, integrate, and analyze data entered in different languages. For example, it can automatically translate, integrate, and analyze comments entered in different languages, such as English, Japanese, and French. It can also extract trends and patterns in different languages ​​and perform analysis from a global perspective. This allows for more comprehensive analysis by integrating data in different languages.

[0096] The data analysis unit can use the emotion estimation function to send customized notifications based on the user's emotions. For example, if the user is feeling stressed, a notification to relax can be sent. If the user is showing positive emotions, a notification to further increase motivation can be sent. This makes it possible to send appropriate notifications according to the user's emotions.

[0097] The data input unit supports gesture input, allowing users to input data using hand movements. For example, it can recognize specific gestures and input corresponding text or commands. It can also use gestures to draw graphs and diagrams. Supporting gesture input thus diversifies data input methods and improves user convenience.

[0098] The report generator uses the emotion estimation function to evaluate the emotional impact of the report content on the reader and mitigate negative expressions. For example, it converts critical comments and negative feedback into softer expressions. It also emphasizes success stories and positive feedback so that readers have positive emotions. This improves the impact of the report content on the reader.

[0099] The report generator can highlight keywords specified by the user when automatically generating a report. For example, it can highlight specific product or project names. It can also change colors and fonts to highlight important data and statistical information. By highlighting keywords specified by the user, important information in the report can be seen at a glance.

[0100] The report generator uses the emotion estimation function to provide real-time feedback on the emotional impact of the report content on the reader, allowing it to select the most appropriate expression. For example, it can select the most appropriate expression based on the reader's emotion score. This improves the effectiveness of the report by selecting the most appropriate expression based on the reader's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The data input section inputs data and comments from the user. For example, text data, voice data, feedback comments, etc. can be input. Step 2: The data analysis unit analyzes the data input by the data input unit, for example, by using statistical analysis, natural language processing, or machine learning algorithms. Step 3: The report generator generates a report with the appropriate context and structure based on the data analyzed by the data analyzer, taking into consideration, for example, the chapter structure, priority of information, and grammatical accuracy. Step 4: The visual data generation unit generates visual data such as images, statistical graphs, and heat maps to accompany the report generated by the report generation unit. For example, it generates a line graph showing sales trends, a heat map showing the distribution of participants, and a pie chart showing survey results.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type 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.

[0129] 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.

[0130] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] 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.

[0149] 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.

[0150] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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. [Explanation of symbols]

[0170] 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. a data input section for inputting data and comments from users; a data analysis unit that analyzes the data input by the data input unit; a report generator that generates a report with appropriate context and structure based on the data analyzed by the data analyzer; a visual data generation unit that generates visual data such as images, statistical graphs, and heat maps that accompany the report generated by the report generation unit. A system characterized by:

2. The data input unit Supports voice input and automatically converts it into text data using voice recognition technology 2. The system of claim 1.

3. The data analysis unit Integrate data from different sources for more comprehensive analysis 2. The system of claim 1.

4. The report generation unit Evaluate the emotional impact of the report's content on the reader and emphasize the positive aspects 2. The system of claim 1.

5. The visual data generation unit Evaluate the emotional impact of visual data on your readers and highlight positive visuals 2. The system of claim 1.

6. The report generation unit Make customized recommendations based on user sentiment and create reports that elicit positive emotions 2. The system of claim 1.

7. The report generation unit Optimize delivery timing based on recipient emotions to elicit positive responses 2. The system of claim 1.

8. The data analysis unit Analyze the sentiment of the comments entered and emphasize positive comments 2. The system of claim 1.

Images