System

Abstract

An object of the system according to the embodiment is to easily visualize data of a Web questionnaire.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, and a generation unit. The reception unit receives aggregation contents from a user. The analysis unit analyzes the aggregation content received by the reception unit. The generation unit generates a graph based on the result analyzed by the analysis 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] With conventional technology, visualizing data from web surveys required setting complex aggregation conditions and relying on specific tools, which made it inefficient.

[0005] The system according to the embodiment aims to easily visualize data from a web survey. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a generation unit. The reception unit receives tabulation content from a user. The analysis unit analyzes the tabulation content received by the reception unit. The generation unit generates a graph based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can easily visualize data from a web survey. [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) A data visualization system according to an embodiment of the present invention accepts user-submitted data, analyzes it using a generation AI, and graphs the results. When a user posts data via chat, the generation AI analyzes the data and graphs the results. This mechanism allows data collected through internal and external survey systems to be aggregated via chat, eliminating the need for complex aggregation conditions or specific tools. For example, a user might input a message into the chat window, such as, "I would like the results of the 2023 customer satisfaction survey to be aggregated and graphed." This message is then input to the generation AI. The generation AI then analyzes the input data. The generation AI then understands the text data and automatically sets the necessary aggregation conditions. For example, based on the message, "I would like the results of the 2023 customer satisfaction survey to be aggregated and graphed," the generation AI extracts customer satisfaction survey data and sets the aggregation conditions. The generated aggregate results are then displayed as a graph. For example, the aggregate results are displayed in a format specified by the user, such as a bar graph or pie chart. This allows users to visually understand the data more easily. This allows the data visualization system to easily visualize data without the need for users to set complex aggregation conditions or rely on specific tools. This allows users to easily visualize data and quickly understand the aggregation results. For example, a marketing department employee can quickly understand the results of a customer satisfaction survey and use them in their next measures.

[0029] A data visualization system according to an embodiment includes a reception unit, an analysis unit, and a generation unit. The reception unit receives tabulation content from a user. The tabulation content from the user may include, but is not limited to, numerical data, text data, and image data. The reception unit may receive tabulation content from the user via a chat interface, for example. The reception unit may also receive voice input or image input. For example, if a user inputs the tabulation content by voice, the input is received using voice recognition technology. The analysis unit uses a generation AI to analyze the tabulation content received by the reception unit. The analysis may be performed using, for example, statistical analysis, text mining, image analysis, or the like, but is not limited to these examples. For example, the generation AI analyzes text data and automatically sets the necessary tabulation conditions. The analysis unit may also use the generation AI to analyze numerical data and image data. The generation unit uses the generation AI to generate a graph based on the results of the analysis by the analysis unit. The graph may be generated in the form of, for example, a bar graph, a pie chart, a line graph, or the like, but is not limited to these examples. For example, the generation AI displays the tabulation results in a format specified by the user. The generation unit can also use the generation AI to display the tabulation results in a visually easy-to-understand format. As a result, the data visualization system according to the embodiment can easily visualize data by accepting and analyzing the tabulation contents from the user and generating graphs.

[0030] The generation unit can graph the tabulated results using the generation AI. For example, the generation unit can use the generation AI to display the tabulated results as a bar graph. The generation unit can also use the generation AI to display the tabulated results as a pie chart. The generation unit can also use the generation AI to display the tabulated results as a line graph. In this way, graphing of the tabulated results is automated by using the generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the tabulated results into the generation AI and cause the generation AI to generate a graph.

[0031] The analysis unit can analyze the text data and automatically set the necessary aggregation conditions. The analysis unit analyzes the text data, for example, using a generation AI. For example, the analysis unit uses the generation AI to understand the text data and automatically set the necessary aggregation conditions. The analysis unit can also use the generation AI to extract keywords from the text data and set the aggregation conditions. The analysis unit can also use the generation AI to understand the context of the text data and set the aggregation conditions. In this way, analyzing the text data and automatically setting the aggregation conditions reduces the user's effort. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the text data to the generation AI and have the generation AI set the aggregation conditions.

[0032] The generation unit can display the aggregation results in a format specified by the user. For example, the generation unit displays the aggregation results as a bar graph in a format specified by the user. The generation unit can also display the aggregation results as a pie chart in a format specified by the user. The generation unit can also display the aggregation results as a line graph in a format specified by the user. Displaying the aggregation results in a format specified by the user makes it easier to visually understand the data. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the aggregation results into the generation AI and cause the generation AI to generate a graph in a specified format.

[0033] The reception unit can accept the tally content entered by the user in the chat. The reception unit accepts the tally content from the user, for example, through a chat interface. The reception unit can also accept voice input or image input. For example, if the user enters the tally content by voice, the tally content can be accepted using voice recognition technology. Also, if the user enters the tally content by image, the tally content can be accepted using image recognition technology. This allows data to be easily entered by accepting the tally content entered by the user in the chat. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the tally content accepted through the chat interface into a generation AI and have the generation AI perform preprocessing for analysis and graph generation.

[0034] The generation unit can display the aggregation results in at least one of a bar graph or a pie chart, as specified by the user. For example, the generation unit displays the aggregation results as a bar graph in the format specified by the user. The generation unit can also display the aggregation results as a pie chart in the format specified by the user. The generation unit can also display the aggregation results as a line graph in the format specified by the user. Displaying the aggregation results in the format specified by the user makes it easier to visually understand the data. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the aggregation results into the generation AI and cause the generation AI to generate a graph in the specified format.

[0035] The reception unit can analyze the user's past submission history of aggregated content and select an appropriate reception method. For example, the reception unit can prioritize and suggest reception methods that the user has frequently used in the past. The reception unit can also select the most efficient reception method from the user's past submission history. The reception unit can also analyze the user's past submission history and suggest the reception method that is optimal for a specific time period. In this way, the optimal reception method can be provided by analyzing the user's past submission history. Some or all of the above-mentioned processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's past submission history data into the generation AI and have the generation AI select the optimal reception method.

[0036] When receiving the aggregated content, the reception unit can filter the aggregated content based on the user's current project or area of ​​interest. For example, the reception unit preferentially receives aggregated content related to the project the user is currently working on. The reception unit can also filter and receive related aggregated content based on the user's area of ​​interest. The reception unit can also receive appropriate aggregated content according to the user's project progress. In this way, by filtering based on the user's current project or area of ​​interest, it is possible to preferentially receive highly relevant aggregated content. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the reception unit can input data related to the user's project or area of ​​interest into the generation AI and have the generation AI perform filtering.

[0037] When accepting the tally content, the acceptance unit can select an appropriate acceptance means according to the user's input method. For example, if the user inputs the tally content by voice, the acceptance unit accepts the tally content using voice recognition technology. Furthermore, if the user inputs the tally content by text, the acceptance unit can accept the tally content using text analysis technology. Furthermore, if the user inputs the tally content by image, the acceptance unit can accept the tally content using image recognition technology. This improves user convenience by selecting the optimal acceptance means according to the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the acceptance unit can input the user's input data to the generation AI and have the generation AI select an appropriate acceptance means.

[0038] When receiving the aggregated content, the reception unit can prioritize receiving highly relevant content by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes receiving aggregated content related to that area. The reception unit can also filter and receive related aggregated content based on the user's current location. The reception unit can also propose optimal aggregated content by taking into account the user's geographical location information. In this way, by taking the user's geographical location information into account, highly relevant aggregated content can be preferentially received. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to filter highly relevant content.

[0039] When receiving the aggregated content, the reception unit can analyze the user's social media activity and receive related content. The reception unit can receive related aggregated content based on, for example, content shared by the user on social media. The reception unit can also analyze the user's social media activity and suggest related aggregated content. The reception unit can also receive related aggregated content with reference to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, related aggregated content can be preferentially received. Some or all of the above-described processing by the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to analyze related content.

[0040] The reception unit can customize the reception method by reflecting the user's past feedback when receiving the aggregated content. The reception unit can, for example, suggest an optimal reception method based on feedback provided by the user in the past. The reception unit can also customize the reception interface by reflecting the user's past feedback. The reception unit can also analyze the user's feedback and improve the reception method. In this way, the optimal reception method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the reception unit can input the user's past feedback data into the generation AI and have the generation AI customize the reception method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the aggregated content. For example, the analysis unit performs a detailed analysis on important aggregated content. The analysis unit can also perform a standard analysis on normal aggregated content. The analysis unit can also perform a simplified analysis on aggregated content with low importance. In this way, by adjusting the level of detail of the analysis based on the importance of the aggregated content, detailed analysis can be performed on important content. Some or all of the above-mentioned 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 input importance data of the aggregated content to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the aggregated content. For example, the analysis unit applies a specific analysis algorithm to data from a customer satisfaction survey. The analysis unit can also apply a different analysis algorithm to market research data. The analysis unit can also apply yet another analysis algorithm to data from an internal survey. In this way, by applying different analysis algorithms depending on the category of the aggregated content, optimal analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI, for example. For example, the analysis unit can input category data of the aggregated content into the generation AI and have the generation AI apply an appropriate analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can improve the accuracy of the current analysis, for example, based on the user's past analysis results. The analysis unit can also select the optimal analysis method by referring to the user's past analysis results. The analysis unit can also analyze the user's past analysis results and improve the analysis algorithm. In this way, the accuracy of the current analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the submission date of the aggregated content. For example, the analysis unit prioritizes analysis of the most recently submitted aggregated content. The analysis unit can also postpone analysis of aggregated content that was submitted earlier. The analysis unit can also dynamically adjust the priority of analysis based on the submission date. This allows the most recent data to be analyzed preferentially by determining the priority of analysis based on the submission date of the aggregated content. Some or all of the above-mentioned 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 input data on the submission date of the aggregated content into the generation AI and have the generation AI determine the priority of analysis.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the aggregated content. For example, the analysis unit prioritizes analysis of highly relevant aggregated content. The analysis unit can also postpone analysis of less relevant aggregated content. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the aggregated content. In this way, by adjusting the order of analysis based on the relevance of the aggregated content, highly relevant data can be analyzed preferentially. Some or all of the above-mentioned 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 input relevance data of the aggregated content to the generation AI and have the generation AI adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide the analysis results using detailed technical terminology. Alternatively, if the user does not have technical expertise, the analysis unit can provide the analysis results in simple language. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. This allows the use of technical terminology in the analysis to be adjusted according to the user's level of expertise, thereby providing analysis results that are easy for the user to understand. 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 input the user's level of expertise data into the generation AI and have the generation AI use technical terminology in the analysis.

[0047] When generating a graph, the generation unit can adjust the level of detail of the graph based on the importance of the aggregation results. For example, the generation unit generates a detailed graph for important aggregation results. The generation unit can also generate a standard graph for normal aggregation results. The generation unit can also generate a simplified graph for aggregation results with low importance. In this way, by adjusting the level of detail of the graph based on the importance of the aggregation results, a detailed graph can be provided for important content. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input importance data of the aggregation results to the generation AI and cause the generation AI to adjust the level of detail of the graph.

[0048] When generating a graph, the generation unit can apply different graph generation algorithms depending on the category of the aggregated results. For example, the generation unit applies a specific graph generation algorithm to data from a customer satisfaction survey. The generation unit can also apply a different graph generation algorithm to market research data. The generation unit can also apply yet another graph generation algorithm to data from an internal survey. In this way, by applying different graph generation algorithms depending on the category of the aggregated results, an optimal graph can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input category data of the aggregated results into the generation AI and cause the generation AI to apply an appropriate graph generation algorithm.

[0049] When generating a graph, the generation unit can improve the accuracy of the graph by referring to the user's past graph generation results. For example, the generation unit improves the accuracy of the current graph based on graphs generated by the user in the past. The generation unit can also select the optimal graph generation method by referring to the user's past graph generation results. The generation unit can also analyze the user's past graph generation results and improve the graph generation algorithm. In this way, the accuracy of the current graph can be improved by referring to the user's past graph generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past graph generation result data into the generation AI and cause the generation AI to improve the accuracy of the graph.

[0050] When generating a graph, the generation unit can determine the priority of the graph based on the submission date of the aggregation results. For example, the generation unit can prioritize graphing of the most recently submitted aggregation results. The generation unit can also graph aggregation results that were submitted earlier later. The generation unit can also dynamically adjust the priority of graphing based on the submission date. In this way, by determining the priority of the graph based on the submission date of the aggregation results, the latest data can be graphed preferentially. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the submission date of the aggregation results into the generation AI and have the generation AI determine the priority of the graphs.

[0051] When generating a graph, the generation unit can adjust the order of the graph based on the relevance of the aggregation results. For example, the generation unit can prioritize graphing of highly relevant aggregation results. The generation unit can also postpone graphing of less relevant aggregation results. The generation unit can also dynamically adjust the order of graphing based on the relevance of the aggregation results. In this way, by adjusting the order of the graph based on the relevance of the aggregation results, highly relevant data can be prioritized for graphing. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input relevance data of the aggregation results to the generation AI and cause the generation AI to adjust the order of the graph.

[0052] When generating a graph, the generation unit can adjust the use of technical terminology in the graph according to the user's level of expertise. For example, if the user has technical expertise, the generation unit can display the graph using detailed technical terminology. Alternatively, if the user does not have technical expertise, the generation unit can display the graph in simple language. The generation unit can also adjust the way the graph is expressed according to the user's level of expertise. This allows for the provision of a graph that is easy for the user to understand by adjusting the use of technical terminology in the graph according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology in the graph.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The reception unit can learn the user's past behavioral patterns and propose optimal aggregation content. For example, if the user has submitted aggregation content during a specific time period in the past, the reception unit can propose aggregation content that matches that time period. Also, if the user has frequently aggregated a specific type of data in the past, the reception unit can preferentially propose that type of data. Furthermore, if the user has displayed aggregation results in a specific format in the past, the reception unit can propose aggregation results in that format. In this way, by learning the user's past behavioral patterns, it becomes possible to propose more personalized aggregation content.

[0055] The generation unit can suggest an optimal graph format based on the user's current task or project. For example, if the user is working on a marketing project, the generation unit can suggest a graph format suitable for marketing data. For example, if the user is performing financial analysis, the generation unit can suggest a graph format suitable for financial data. Furthermore, if the user is preparing a presentation, the generation unit can suggest a visually appealing graph format. This makes it possible to provide the optimal graph format based on the user's current task or project.

[0056] The analysis unit can provide a function to compare current analysis results based on the user's past analysis results. For example, it can display the user's past analysis results and current analysis results side by side, allowing them to visually confirm changes. It can also automatically calculate the difference between the past analysis results and the current analysis results and present it to the user. Furthermore, it can provide a function to make future predictions based on the past analysis results and current analysis results. This allows the user to compare past data with current data and gain deeper insights.

[0057] The reception unit can preferentially receive region-specific data based on the user's geographical location information. For example, if the user is in a specific region, data related to that region can be preferentially received. Also, if the user is traveling, related data can be suggested based on the user's current location. Furthermore, if the user is interested in a specific region, data related to that region can be preferentially received. In this way, by preferentially receiving region-specific data based on the user's geographical location information, it is possible to provide data that is highly relevant to the user.

[0058] The generation unit can suggest the optimal graph format based on the user's past graph generation history. For example, if the user has frequently used bar graphs in the past, the generation unit can preferentially suggest bar graphs. Also, if the user has used pie charts in the past, the generation unit can suggest pie charts. Furthermore, if the user has used graphs with a specific design in the past, the generation unit can suggest graphs based on that design. This makes it possible to provide the optimal graph format based on the user's past graph generation history.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The reception unit receives the tally content from the user. The tally content from the user includes numerical data, text data, image data, etc. The reception unit receives the tally content from the user through a chat interface, and can also receive voice input and image input. For example, if the user inputs the tally content by voice, it is accepted using voice recognition technology. Step 2: The analysis unit uses the generation AI to analyze the tabulation content received by the reception unit. The analysis is performed using methods such as statistical analysis, text mining, and image analysis. For example, the generation AI analyzes text data and automatically sets the necessary tabulation conditions. The analysis unit can also use the generation AI to analyze numerical data and image data. Step 3: The generation unit uses the generation AI to generate a graph based on the results of the analysis by the analysis unit. The graph is generated in the form of a bar graph, pie chart, line graph, etc. The generation AI displays the aggregated results in a format specified by the user. The generation unit can also use the generation AI to display the aggregated results in a visually easy-to-understand format.

[0061] (Example 2) A data visualization system according to an embodiment of the present invention accepts user-submitted data, analyzes it using a generation AI, and graphs the results. When a user posts data via chat, the generation AI analyzes the data and graphs the results. This mechanism allows data collected through internal and external survey systems to be aggregated via chat, eliminating the need for complex aggregation conditions or specific tools. For example, a user might input a message into the chat window, such as, "I would like the results of the 2023 customer satisfaction survey to be aggregated and graphed." This message is then input to the generation AI. The generation AI then analyzes the input data. The generation AI then understands the text data and automatically sets the necessary aggregation conditions. For example, based on the message, "I would like the results of the 2023 customer satisfaction survey to be aggregated and graphed," the generation AI extracts customer satisfaction survey data and sets the aggregation conditions. The generated aggregate results are then displayed as a graph. For example, the aggregate results are displayed in a format specified by the user, such as a bar graph or pie chart. This allows users to visually understand the data more easily. This allows the data visualization system to easily visualize data without the need for users to set complex aggregation conditions or rely on specific tools. This allows users to easily visualize data and quickly understand the aggregation results. For example, a marketing department employee can quickly understand the results of a customer satisfaction survey and use them in their next measures.

[0062] A data visualization system according to an embodiment includes a reception unit, an analysis unit, and a generation unit. The reception unit receives tabulation content from a user. The tabulation content from the user may include, but is not limited to, numerical data, text data, and image data. The reception unit may receive tabulation content from the user via a chat interface, for example. The reception unit may also receive voice input or image input. For example, if a user inputs the tabulation content by voice, the input is received using voice recognition technology. The analysis unit uses a generation AI to analyze the tabulation content received by the reception unit. The analysis may be performed using, for example, statistical analysis, text mining, image analysis, or the like, but is not limited to these examples. For example, the generation AI analyzes text data and automatically sets the necessary tabulation conditions. The analysis unit may also use the generation AI to analyze numerical data and image data. The generation unit uses the generation AI to generate a graph based on the results of the analysis by the analysis unit. The graph may be generated in the form of, for example, a bar graph, a pie chart, a line graph, or the like, but is not limited to these examples. For example, the generation AI displays the tabulation results in a format specified by the user. The generation unit can also use the generation AI to display the tabulation results in a visually easy-to-understand format. As a result, the data visualization system according to the embodiment can easily visualize data by accepting and analyzing the tabulation contents from the user and generating graphs.

[0063] The generation unit can graph the tabulated results using the generation AI. For example, the generation unit can use the generation AI to display the tabulated results as a bar graph. The generation unit can also use the generation AI to display the tabulated results as a pie chart. The generation unit can also use the generation AI to display the tabulated results as a line graph. In this way, graphing of the tabulated results is automated by using the generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the tabulated results into the generation AI and cause the generation AI to generate a graph.

[0064] The analysis unit can analyze the text data and automatically set the necessary aggregation conditions. The analysis unit analyzes the text data, for example, using a generation AI. For example, the analysis unit uses the generation AI to understand the text data and automatically set the necessary aggregation conditions. The analysis unit can also use the generation AI to extract keywords from the text data and set the aggregation conditions. The analysis unit can also use the generation AI to understand the context of the text data and set the aggregation conditions. In this way, analyzing the text data and automatically setting the aggregation conditions reduces the user's effort. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the text data to the generation AI and have the generation AI set the aggregation conditions.

[0065] The generation unit can display the aggregation results in a format specified by the user. For example, the generation unit displays the aggregation results as a bar graph in a format specified by the user. The generation unit can also display the aggregation results as a pie chart in a format specified by the user. The generation unit can also display the aggregation results as a line graph in a format specified by the user. Displaying the aggregation results in a format specified by the user makes it easier to visually understand the data. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the aggregation results into the generation AI and cause the generation AI to generate a graph in a specified format.

[0066] The reception unit can accept the tally content entered by the user in the chat. The reception unit accepts the tally content from the user, for example, through a chat interface. The reception unit can also accept voice input or image input. For example, if the user enters the tally content by voice, the tally content can be accepted using voice recognition technology. Also, if the user enters the tally content by image, the tally content can be accepted using image recognition technology. This allows data to be easily entered by accepting the tally content entered by the user in the chat. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the tally content accepted through the chat interface into a generation AI and have the generation AI perform preprocessing for analysis and graph generation.

[0067] The generation unit can display the aggregation results in at least one of a bar graph or a pie chart, as specified by the user. For example, the generation unit displays the aggregation results as a bar graph in the format specified by the user. The generation unit can also display the aggregation results as a pie chart in the format specified by the user. The generation unit can also display the aggregation results as a line graph in the format specified by the user. Displaying the aggregation results in the format specified by the user makes it easier to visually understand the data. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the aggregation results into the generation AI and cause the generation AI to generate a graph in the specified format.

[0068] The reception unit can estimate the user's emotions and adjust the timing of receiving the tally content based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can reduce the user's burden by immediately receiving the tally content. Furthermore, if the user is relaxed, the reception unit can maintain the user's concentration by receiving the tally content at an appropriate timing. Furthermore, if the user is in a hurry, the reception unit can save the user's time by quickly receiving the tally content. This reduces the user's burden by adjusting the timing of receiving the tally content according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI. For example, the reception unit may input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0069] The reception unit can analyze the user's past submission history of aggregated content and select an appropriate reception method. For example, the reception unit can prioritize and suggest reception methods that the user has frequently used in the past. The reception unit can also select the most efficient reception method from the user's past submission history. The reception unit can also analyze the user's past submission history and suggest the reception method that is optimal for a specific time period. In this way, the optimal reception method can be provided by analyzing the user's past submission history. Some or all of the above-mentioned processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's past submission history data into the generation AI and have the generation AI select the optimal reception method.

[0070] When receiving the aggregated content, the reception unit can filter the aggregated content based on the user's current project or area of ​​interest. For example, the reception unit preferentially receives aggregated content related to the project the user is currently working on. The reception unit can also filter and receive related aggregated content based on the user's area of ​​interest. The reception unit can also receive appropriate aggregated content according to the user's project progress. In this way, by filtering based on the user's current project or area of ​​interest, it is possible to preferentially receive highly relevant aggregated content. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the reception unit can input data related to the user's project or area of ​​interest into the generation AI and have the generation AI perform filtering.

[0071] When accepting the tally content, the acceptance unit can select an appropriate acceptance means according to the user's input method. For example, if the user inputs the tally content by voice, the acceptance unit accepts the tally content using voice recognition technology. Furthermore, if the user inputs the tally content by text, the acceptance unit can accept the tally content using text analysis technology. Furthermore, if the user inputs the tally content by image, the acceptance unit can accept the tally content using image recognition technology. This improves user convenience by selecting the optimal acceptance means according to the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the acceptance unit can input the user's input data to the generation AI and have the generation AI select an appropriate acceptance means.

[0072] The reception unit can estimate the user's emotions and determine the priority of the collected data to be received based on the estimated user's emotions. For example, when the user is stressed, the reception unit can prioritize important collected data. Furthermore, when the user is relaxed, the reception unit can also receive collected data with normal priority. Furthermore, when the user is in a hurry, the reception unit can also receive urgent collected data with top priority. Thus, by determining the priority of collected data according to the user's emotions, important collected data can be received with priority. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0073] When receiving the aggregated content, the reception unit can prioritize receiving highly relevant content by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes receiving aggregated content related to that area. The reception unit can also filter and receive related aggregated content based on the user's current location. The reception unit can also propose optimal aggregated content by taking into account the user's geographical location information. In this way, by taking the user's geographical location information into account, highly relevant aggregated content can be preferentially received. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to filter highly relevant content.

[0074] When receiving the aggregated content, the reception unit can analyze the user's social media activity and receive related content. The reception unit can receive related aggregated content based on, for example, content shared by the user on social media. The reception unit can also analyze the user's social media activity and suggest related aggregated content. The reception unit can also receive related aggregated content with reference to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, related aggregated content can be preferentially received. Some or all of the above-described processing by the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to analyze related content.

[0075] The reception unit can customize the reception method by reflecting the user's past feedback when receiving the aggregated content. The reception unit can, for example, suggest an optimal reception method based on feedback provided by the user in the past. The reception unit can also customize the reception interface by reflecting the user's past feedback. The reception unit can also analyze the user's feedback and improve the reception method. In this way, the optimal reception method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the reception unit can input the user's past feedback data into the generation AI and have the generation AI customize the reception method.

[0076] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple and visually easy-to-understand analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result that focuses on the main points. By adjusting the presentation method of the analysis according to the user's emotions, it is possible to provide an analysis result that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0077] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the aggregated content. For example, the analysis unit performs a detailed analysis on important aggregated content. The analysis unit can also perform a standard analysis on normal aggregated content. The analysis unit can also perform a simplified analysis on aggregated content with low importance. In this way, by adjusting the level of detail of the analysis based on the importance of the aggregated content, detailed analysis can be performed on important content. Some or all of the above-mentioned 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 input importance data of the aggregated content to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0078] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the aggregated content. For example, the analysis unit applies a specific analysis algorithm to data from a customer satisfaction survey. The analysis unit can also apply a different analysis algorithm to market research data. The analysis unit can also apply yet another analysis algorithm to data from an internal survey. In this way, by applying different analysis algorithms depending on the category of the aggregated content, optimal analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI, for example. For example, the analysis unit can input category data of the aggregated content into the generation AI and have the generation AI apply an appropriate analysis algorithm.

[0079] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can improve the accuracy of the current analysis, for example, based on the user's past analysis results. The analysis unit can also select the optimal analysis method by referring to the user's past analysis results. The analysis unit can also analyze the user's past analysis results and improve the analysis algorithm. In this way, the accuracy of the current analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0080] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is stressed, the analysis unit can also provide a simple, visually easy-to-understand analysis result. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide the optimal analysis result for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0081] During analysis, the analysis unit can determine the priority of analysis based on the submission date of the aggregated content. For example, the analysis unit prioritizes analysis of the most recently submitted aggregated content. The analysis unit can also postpone analysis of aggregated content that was submitted earlier. The analysis unit can also dynamically adjust the priority of analysis based on the submission date. This allows the most recent data to be analyzed preferentially by determining the priority of analysis based on the submission date of the aggregated content. Some or all of the above-mentioned 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 input data on the submission date of the aggregated content into the generation AI and have the generation AI determine the priority of analysis.

[0082] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the aggregated content. For example, the analysis unit prioritizes analysis of highly relevant aggregated content. The analysis unit can also postpone analysis of less relevant aggregated content. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the aggregated content. In this way, by adjusting the order of analysis based on the relevance of the aggregated content, highly relevant data can be analyzed preferentially. Some or all of the above-mentioned 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 input relevance data of the aggregated content to the generation AI and have the generation AI adjust the order of analysis.

[0083] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide the analysis results using detailed technical terminology. Alternatively, if the user does not have technical expertise, the analysis unit can provide the analysis results in simple language. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. This allows the use of technical terminology in the analysis to be adjusted according to the user's level of expertise, thereby providing analysis results that are easy for the user to understand. 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 input the user's level of expertise data into the generation AI and have the generation AI use technical terminology in the analysis.

[0084] The generation unit can estimate the user's emotions and adjust the graph generation method based on the estimated user emotions. For example, the generation unit generates a detailed graph when the user is relaxed. The generation unit can also generate a concise graph when the user is in a hurry. The generation unit can also generate a visually easy-to-understand graph when the user is stressed. By adjusting the graph generation method according to the user's emotions, the optimal graph can be provided to the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the graph generation method.

[0085] When generating a graph, the generation unit can adjust the level of detail of the graph based on the importance of the aggregation results. For example, the generation unit generates a detailed graph for important aggregation results. The generation unit can also generate a standard graph for normal aggregation results. The generation unit can also generate a simplified graph for aggregation results with low importance. In this way, by adjusting the level of detail of the graph based on the importance of the aggregation results, a detailed graph can be provided for important content. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input importance data of the aggregation results to the generation AI and cause the generation AI to adjust the level of detail of the graph.

[0086] When generating a graph, the generation unit can apply different graph generation algorithms depending on the category of the aggregated results. For example, the generation unit applies a specific graph generation algorithm to data from a customer satisfaction survey. The generation unit can also apply a different graph generation algorithm to market research data. The generation unit can also apply yet another graph generation algorithm to data from an internal survey. In this way, by applying different graph generation algorithms depending on the category of the aggregated results, an optimal graph can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input category data of the aggregated results into the generation AI and cause the generation AI to apply an appropriate graph generation algorithm.

[0087] When generating a graph, the generation unit can improve the accuracy of the graph by referring to the user's past graph generation results. For example, the generation unit improves the accuracy of the current graph based on graphs generated by the user in the past. The generation unit can also select the optimal graph generation method by referring to the user's past graph generation results. The generation unit can also analyze the user's past graph generation results and improve the graph generation algorithm. In this way, the accuracy of the current graph can be improved by referring to the user's past graph generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past graph generation result data into the generation AI and cause the generation AI to improve the accuracy of the graph.

[0088] The generation unit can estimate the user's emotions and adjust the graph display method based on the estimated user emotions. For example, the generation unit can display a detailed graph when the user is relaxed. The generation unit can also display a simple graph when the user is in a hurry. The generation unit can also display a visually easy-to-understand graph when the user is stressed. By adjusting the graph display method according to the user's emotions, the optimal graph can be provided to the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the graph display method.

[0089] When generating a graph, the generation unit can determine the priority of the graph based on the submission date of the aggregation results. For example, the generation unit can prioritize graphing of the most recently submitted aggregation results. The generation unit can also graph aggregation results that were submitted earlier later. The generation unit can also dynamically adjust the priority of graphing based on the submission date. In this way, by determining the priority of the graph based on the submission date of the aggregation results, the latest data can be graphed preferentially. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the submission date of the aggregation results into the generation AI and have the generation AI determine the priority of the graphs.

[0090] When generating a graph, the generation unit can adjust the order of the graph based on the relevance of the aggregation results. For example, the generation unit can prioritize graphing of highly relevant aggregation results. The generation unit can also postpone graphing of less relevant aggregation results. The generation unit can also dynamically adjust the order of graphing based on the relevance of the aggregation results. In this way, by adjusting the order of the graph based on the relevance of the aggregation results, highly relevant data can be prioritized for graphing. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input relevance data of the aggregation results to the generation AI and cause the generation AI to adjust the order of the graph.

[0091] When generating a graph, the generation unit can adjust the use of technical terminology in the graph according to the user's level of expertise. For example, if the user has technical expertise, the generation unit can display the graph using detailed technical terminology. Alternatively, if the user does not have technical expertise, the generation unit can display the graph in simple language. The generation unit can also adjust the way the graph is expressed according to the user's level of expertise. This allows for the provision of a graph that is easy for the user to understand by adjusting the use of technical terminology in the graph according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology in the graph. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and generation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives the tabulated content from the user through a chat interface 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 tabulated content received by the reception unit using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a graph based on the results of the analysis by the analysis unit. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, and generation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives the tabulated content from the user through a chat interface 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 tabulated content received by the reception unit using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a graph based on the results of the analysis by the analysis unit. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and generation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit receives tabulation content from a user through a chat interface 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 tabulation content received by the reception unit using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates a graph based on the results of the analysis by the analysis unit. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and generation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives the tabulation content from the user through a chat interface of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the tabulation content received by the reception unit using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates a graph based on the results of the analysis by the analysis unit.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The reception unit can learn the user's past behavioral patterns and propose optimal aggregation content. For example, if the user has submitted aggregation content during a specific time period in the past, the reception unit can propose aggregation content that matches that time period. Also, if the user has frequently aggregated a specific type of data in the past, the reception unit can preferentially propose that type of data. Furthermore, if the user has displayed aggregation results in a specific format in the past, the reception unit can propose aggregation results in that format. In this way, by learning the user's past behavioral patterns, it becomes possible to propose more personalized aggregation content.

[0094] The analysis unit can estimate the user's emotions and adjust the depth of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, it can provide a concise and to-the-point analysis result. If the user is relaxed, it can also provide a detailed analysis result. Furthermore, if the user is in a hurry, it can provide a visually easy-to-understand analysis result for quick understanding. In this way, by adjusting the depth of the analysis according to the user's emotions, it is possible to provide the optimal analysis result for the user.

[0095] The generation unit can suggest an optimal graph format based on the user's current task or project. For example, if the user is working on a marketing project, the generation unit can suggest a graph format suitable for marketing data. For example, if the user is performing financial analysis, the generation unit can suggest a graph format suitable for financial data. Furthermore, if the user is preparing a presentation, the generation unit can suggest a visually appealing graph format. This makes it possible to provide the optimal graph format based on the user's current task or project.

[0096] The reception unit can estimate the user's emotions and adjust the interface design based on the estimated user emotions. For example, if the user is feeling stressed, a simple and intuitive interface can be provided. Alternatively, if the user is relaxed, an interface displaying detailed information can be provided. Furthermore, if the user is in a hurry, the layout of buttons and menus can be optimized to allow for quick operation. In this way, by adjusting the interface design according to the user's emotions, user operability can be improved.

[0097] The analysis unit can provide a function to compare current analysis results based on the user's past analysis results. For example, it can display the user's past analysis results and current analysis results side by side, allowing them to visually confirm changes. It can also automatically calculate the difference between the past analysis results and the current analysis results and present it to the user. Furthermore, it can provide a function to make future predictions based on the past analysis results and current analysis results. This allows the user to compare past data with current data and gain deeper insights.

[0098] The generation unit can estimate the user's emotions and adjust the color and design of the graph based on the estimated user's emotions. For example, if the user is feeling stressed, a graph with subdued colors can be generated. Alternatively, if the user is relaxed, a graph with vivid colors can be generated. Furthermore, if the user is in a hurry, a graph with a visually easy-to-understand design can be generated. In this way, by adjusting the color and design of the graph according to the user's emotions, it is possible to provide the user with an optimal graph.

[0099] The reception unit can preferentially receive region-specific data based on the user's geographical location information. For example, if the user is in a specific region, data related to that region can be preferentially received. Also, if the user is traveling, related data can be suggested based on the user's current location. Furthermore, if the user is interested in a specific region, data related to that region can be preferentially received. In this way, by preferentially receiving region-specific data based on the user's geographical location information, it is possible to provide data that is highly relevant to the user.

[0100] The analysis unit can estimate the user's emotions and adjust the analysis visualization method based on the estimated user emotions. For example, if the user is feeling stressed, a simple and visually easy-to-understand analysis result can be provided. If the user is relaxed, detailed analysis results can be provided. Furthermore, if the user is in a hurry, analysis results that are concise and easy to understand can be provided. In this way, by adjusting the analysis visualization method according to the user's emotions, it is possible to provide the optimal analysis results for the user.

[0101] The generation unit can suggest the optimal graph format based on the user's past graph generation history. For example, if the user has frequently used bar graphs in the past, the generation unit can preferentially suggest bar graphs. Also, if the user has used pie charts in the past, the generation unit can suggest pie charts. Furthermore, if the user has used graphs with a specific design in the past, the generation unit can suggest graphs based on that design. This makes it possible to provide the optimal graph format based on the user's past graph generation history.

[0102] The reception unit can estimate the user's emotions and customize the method for receiving the tally content based on the estimated user emotions. For example, if the user is feeling stressed, an interface that allows the user to receive the tally content with a simple operation can be provided. Alternatively, if the user is relaxed, an interface that allows detailed input can be provided. Furthermore, if the user is in a hurry, shortcuts or templates can be provided to enable quick input. In this way, the method for receiving the tally content can be customized according to the user's emotions, thereby improving user convenience.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The reception unit receives the tally content from the user. The tally content from the user includes numerical data, text data, image data, etc. The reception unit receives the tally content from the user through a chat interface, and can also receive voice input and image input. For example, if the user inputs the tally content by voice, it is accepted using voice recognition technology. Step 2: The analysis unit uses the generation AI to analyze the tabulation content received by the reception unit. The analysis is performed using methods such as statistical analysis, text mining, and image analysis. For example, the generation AI analyzes text data and automatically sets the necessary tabulation conditions. The analysis unit can also use the generation AI to analyze numerical data and image data. Step 3: The generation unit uses the generation AI to generate a graph based on the results of the analysis by the analysis unit. The graph is generated in the form of a bar graph, pie chart, line graph, etc. The generation AI displays the aggregated results in a format specified by the user. The generation unit can also use the generation AI to display the aggregated results in a visually easy-to-understand format.

[0105] 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.

[0106] 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 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.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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 reception unit that receives the tally content from a user; an analysis unit that analyzes the tally content received by the reception unit; a generation unit that generates a graph based on the results of the analysis by the analysis unit. A system characterized by:

2. The generation unit Generate a graph of the aggregated results using AI 2. The system of claim 1.

3. The analysis unit Analyze text data and automatically set the necessary aggregation conditions 2. The system of claim 1.

4. The generation unit Display the summary results in the format specified by the user 2. The system of claim 1.

5. The reception unit Accepts the summary entered by the user in the chat 2. The system of claim 1.

6. The generation unit Display the summary results in a bar graph or pie chart, or in a format specified by the user.

2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the timing of accepting the aggregated content based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past submission history of aggregated data and select the appropriate method of acceptance.

2. The system of claim 1.

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