System

Abstract

An object of a system according to an embodiment is to automate creation of a data analysis report and improve a data analysis ability of a user.SOLUTION: A system includes a reception unit, an analysis unit, a generation unit, and a feedback unit. The reception unit receives data from a user. The analysis unit analyzes the data received by the reception unit and extracts important trends and insights. The generation unit generates a report based on the tendency and the insight extracted by the analysis unit. The feedback unit provides feedback based on the report generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that creating a data analysis report takes time and effort, making it difficult to perform efficient data analysis.

[0005] The system according to the embodiment aims to automate the creation of data analysis reports and improve the data analysis capabilities of users. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a feedback unit. The reception unit receives data from a user. The analysis unit analyzes the data received by the reception unit and extracts important trends and insights. The generation unit generates a report based on the trends and insights extracted by the analysis unit. The feedback unit provides feedback based on the report generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment automates the creation of data analysis reports and can improve the data analysis capabilities of users. [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 analysis report automatic generation system according to an embodiment of the present invention analyzes data provided by a user, extracts important trends and insights using a generation AI, and generates a report. The data analysis report automatic generation system accepts data from a user, analyzes it using a generation AI, extracts important trends and insights, and generates a report. The data analysis report automatic generation system also provides feedback based on the generated report. For example, the data analysis report automatic generation system receives data provided by a user. For example, the system may identify seasonal sales trends from sales data or behavioral patterns of specific customer segments from customer data. The data analysis report automatic generation system then uses a generation AI to generate a consistent, easy-to-understand report based on the extracted trends and insights. This report is designed to visually convey information using graphs and charts. For example, it may include a line graph showing trends in sales data or a pie chart showing behavioral patterns of customer segments. Furthermore, the data analysis report automatic generation system provides feedback on how to analyze data and derive insights based on the generated report. For example, the system may provide advice on what analysis method to use for a specific data set or from what perspective to interpret the data. As a result, the data analysis report automatic generation system can improve the user's data analysis capabilities while significantly reducing the time and effort required to create a data analysis report. As a result, the data analysis report automatic generation system can improve the user's data analysis capabilities while significantly reducing the time and effort required to create a data analysis report. For example, a company's marketing department can efficiently analyze sales data and customer data and quickly develop marketing strategies. Also, a research institute can quickly analyze experimental data and effectively present research results.

[0029] The data analysis report automatic generation system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a feedback unit. The reception unit receives data from a user. The data provided by the user includes, but is not limited to, text data, numerical data, and image data. The reception unit receives, for example, a file uploaded by the user. The reception unit can also receive data via an API. The reception unit can also directly receive text data entered by the user. For example, the reception unit receives a CSV file provided by the user and prepares the data for analysis. The data received via the API is automatically imported into the system. The text data entered by the user is updated in real time. The analysis unit uses a generation AI to analyze the data received by the reception unit and extract important trends and insights. The analysis is performed by, for example, detecting frequent patterns and detecting anomalies, but is not limited to, such examples. For example, the analysis unit analyzes sales data and extracts seasonal sales trends. The analysis unit can also analyze customer data and identify behavioral patterns of specific customer segments. The analysis unit can also analyze social media data and perform sentiment analysis. For example, the analysis unit can perform time-series analysis of sales data to identify seasonal sales fluctuations. It can cluster customer data and classify customer demographics. It can perform text mining of social media data to analyze sentiment trends. The generation unit uses generative AI to generate a report based on the trends and insights extracted by the analysis unit. The report can be generated in the form of, for example, a text report or a graph report, but is not limited to these examples. For example, the generation unit can generate a line graph that shows trends in sales data. The generation unit can also generate a pie chart that shows behavior patterns of customer demographics. The generation unit can also generate a heat map that shows the results of the sentiment analysis. For example, the generation unit can generate a line graph to visually show trends in sales data. It can generate a pie chart to show behavior patterns of customer demographics. It can also generate a heat map to show the results of the sentiment analysis.The feedback unit provides feedback on how to analyze data and how to derive insights based on the generated report. The feedback includes, but is not limited to, advice on what analysis method should be used for a specific data set and from what perspective the data should be interpreted. For example, the feedback unit may advise that time series analysis should be performed on sales data. The feedback unit may also advise that clustering should be performed on customer data. The feedback unit may also advise that sentiment analysis should be performed on social media data. For example, the feedback unit may recommend time series analysis in the analysis of sales data, clustering in the analysis of customer data, or sentiment analysis in the analysis of social media data. Thus, the data analysis report automatic generation system according to the embodiment automates the creation of data analysis reports for users and improves their data analysis capabilities.

[0030] The reception unit can analyze the user's past data provision history and select the optimal reception method. For example, the reception unit can prioritize and suggest data provision methods (such as file uploads and API integration) that the user has frequently used in the past. The reception unit can also analyze the format and content of data provided by the user in the past and suggest the optimal data provision format. The reception unit can also suggest a simplified data provision procedure if the user has taken a long time to provide data in the past. In this way, by analyzing the past data provision history, the optimal data reception method can be provided to the user. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past data provision history into the generation AI and have the generation AI select the optimal reception method.

[0031] When receiving data, the reception unit can filter the data based on the user's current project or area of ​​interest. For example, the reception unit can preferentially receive only data related to the user's ongoing project. The reception unit can also automatically filter and receive highly relevant data based on the user's area of ​​interest. The reception unit can also suggest and receive optimal data based on data categories in which the user has previously shown interest. In this way, by filtering data based on the user's project or area of ​​interest, highly relevant data can be preferentially received. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's project information into a generation AI and cause the generation AI to filter highly relevant data.

[0032] When accepting data, the acceptance unit can select an appropriate acceptance means depending on the user's input method. For example, if the user desires voice input, the acceptance unit accepts the data using voice recognition technology. Furthermore, if the user desires text input, the acceptance unit can provide a text box to allow easy data entry. Furthermore, if the user provides image data, the acceptance unit can analyze the data using image recognition technology and accept the data. By selecting the optimal acceptance means depending on the user's input method, data can be accepted smoothly. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can input voice data to a generation AI and have the generation AI analyze the voice data.

[0033] When receiving data, the reception unit can prioritize receiving highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes receiving data related to that area. The reception unit can also prioritize receiving nearby data based on the user's location information. When the user is moving, the reception unit can also prioritize receiving data that is most relevant to the user's current location. In this way, highly relevant data can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's location information data to the generation AI and cause the generation AI to select highly relevant data.

[0034] The reception unit can analyze the user's social media activity and receive related data when receiving data. For example, the reception unit can automatically receive data shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and preferentially receive related data. The reception unit can also receive related data with reference to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, highly relevant data can be preferentially received. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to select related data.

[0035] The reception unit can customize the reception method by reflecting the user's past feedback when receiving data. The reception unit can, for example, propose an optimal data reception method based on feedback provided by the user in the past. The reception unit can also improve and streamline the data reception procedure based on the user's past feedback. The reception unit can also resolve problems previously pointed out by the user to ensure smooth data reception. This makes it possible to provide an optimal data reception method by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using 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.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on highly important data to provide deep insights. The analysis unit can also perform a concise analysis on less important data to provide results that focus on the main points. The analysis unit can also optimally allocate analysis resources according to the importance of the data to perform efficient analysis. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0037] During analysis, the analysis unit can apply an appropriate analysis algorithm depending on the data category. For example, the analysis unit can apply a time series analysis algorithm to sales data to analyze sales trends. The analysis unit can also apply a clustering algorithm to customer data to classify customer segments. The analysis unit can also apply a text mining algorithm to social media data to perform sentiment analysis. This improves the accuracy of analysis by applying the optimal analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the data category into the generation AI and have the generation AI apply the appropriate analysis algorithm.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit applies the optimal analysis method to similar data based on the user's past analysis results. The analysis unit can also adjust parameters to improve the accuracy of the analysis based on the user's past analysis results. The analysis unit can also optimally allocate analysis resources by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0039] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. For example, the analysis unit prioritizes analysis of the most recent data to provide real-time insights. The analysis unit can also postpone analysis of data that was submitted earlier. The analysis unit can also optimally allocate analysis resources based on the time of submission to perform efficient analysis. In this way, efficient analysis can be performed by determining the priority of analysis based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data submission to the generation AI and have the generation AI determine the analysis priority.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data, making it easier to obtain overall insight. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also optimally allocate analysis resources based on the relevance of the data to perform efficient analysis. In this way, adjusting the order of analysis based on the relevance of the data enables efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0041] 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 analysis results using detailed technical terminology. Alternatively, if the user does not have technical expertise, the analysis unit can provide analysis results using concise, easy-to-understand language. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise into the generation AI and have the generation AI use technical terminology.

[0042] When generating a report, the generation unit can adjust the level of detail of the report based on the importance of the data. For example, the generation unit can generate a detailed report for data with high importance to provide deep insight. The generation unit can also generate a concise report for data with low importance to provide information that focuses on the main points. The generation unit can also optimally allocate report resources according to the importance of the data to generate efficient reports. In this way, by adjusting the level of detail of the report based on the importance of the data, efficient report generation can be achieved. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the report.

[0043] When generating a report, the generation unit can apply an appropriate report generation algorithm depending on the data category. For example, the generation unit can generate a report using a time series graph for sales data. The generation unit can also generate a report using clustering results for customer data. The generation unit can also generate a report using sentiment analysis results for social media data. This improves the accuracy of the report by applying the optimal report generation algorithm depending on the data category. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the data category into the generation AI and cause the generation AI to apply an appropriate report generation algorithm.

[0044] When generating a report, the generation unit can improve the accuracy of the report by referring to the user's past report results. For example, the generation unit applies an optimal report generation method to similar data based on reports created by the user in the past. The generation unit can also adjust parameters for improving the accuracy of the report based on the user's past report results. The generation unit can also optimally allocate report resources by referring to the user's past report results. In this way, the accuracy of the report is improved by referring to the user's past report results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past report results into the generation AI and cause the generation AI to improve the accuracy of the report.

[0045] When generating a report, the generation unit can determine the priority of the report based on the time of data submission. For example, the generation unit can prioritize the most recent data in the report to provide real-time insight. The generation unit can also postpone generating a report for data that was submitted earlier. The generation unit can also optimally allocate report resources based on the time of submission to generate reports efficiently. In this way, by determining the priority of reports based on the time of data submission, efficient report generation can be achieved. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the time of data submission to the generation AI and have the generation AI determine the priority of the reports.

[0046] When generating a report, the generation unit can adjust the order of the reports based on the relevance of the data. For example, the generation unit can prioritize highly relevant data in the report, making it easier to obtain overall insight. The generation unit can also postpone generating reports for less relevant data. The generation unit can also optimally allocate report resources based on the relevance of the data, thereby generating reports efficiently. In this way, adjusting the order of the reports based on the relevance of the data enables efficient report generation. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the relevance of the data into the generation AI and cause the generation AI to adjust the order of the reports.

[0047] When generating a report, the generation unit can adjust the use of technical terminology in the report according to the user's level of expertise. For example, if the user has technical expertise, the generation unit can generate the report using detailed technical terminology. Alternatively, if the user does not have technical expertise, the generation unit can generate the report using concise and easy-to-understand language. The generation unit can also adjust the way the report is expressed according to the user's level of expertise. This allows for adjusting the use of technical terminology in the report according to the user's level of expertise, thereby providing a more appropriate report. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's level of expertise into the generation AI and have the generation AI execute the use of technical terminology.

[0048] When providing feedback, the feedback unit can adjust the level of detail of the feedback based on the importance of the data. For example, the feedback unit can provide detailed feedback for data with high importance, allowing for deeper insight. The feedback unit can also provide concise feedback for data with low importance, providing information that focuses on the main points. The feedback unit can also optimally allocate feedback resources according to the importance of the data and provide efficient feedback. In this way, efficient feedback can be provided by adjusting the level of detail of the feedback based on the importance of the data. Some or all of the above-mentioned processing in the feedback unit may be performed using, or without, AI, for example. For example, the feedback unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the feedback.

[0049] When providing feedback, the feedback unit can apply different feedback algorithms depending on the data category. For example, the feedback unit can provide feedback based on time series analysis results for sales data. The feedback unit can also provide feedback based on clustering results for customer data. The feedback unit can also provide feedback based on sentiment analysis results for social media data. This improves the accuracy of feedback by applying the optimal feedback algorithm depending on the data category. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input the data category to the generation AI and cause the generation AI to apply an appropriate feedback algorithm.

[0050] When providing feedback, the feedback unit can improve the accuracy of the feedback by referring to the user's past feedback results. The feedback unit, for example, provides optimal feedback for similar data based on feedback the user has received in the past. The feedback unit can also adjust parameters for improving the accuracy of the feedback based on the user's past feedback results. The feedback unit can also optimally allocate feedback resources by referring to the user's past feedback results. In this way, the accuracy of the feedback is improved by referring to the user's past feedback results. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past feedback results into the generation AI and cause the generation AI to improve the accuracy of the feedback.

[0051] When providing feedback, the feedback unit can determine the priority of the feedback based on the time of data submission. For example, the feedback unit can provide feedback preferentially to the most recent data and provide real-time insight. The feedback unit can also provide feedback later to data submitted earlier. The feedback unit can also optimally allocate feedback resources based on the time of submission and provide efficient feedback. In this way, efficient feedback can be provided by determining the priority of feedback based on the time of data submission. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the time of data submission to the generation AI and have the generation AI determine the priority of feedback.

[0052] When providing feedback, the feedback unit can adjust the order of feedback based on the relevance of the data. For example, the feedback unit can provide feedback preferentially to highly relevant data, making it easier to obtain overall insight. The feedback unit can also provide feedback later to less relevant data. The feedback unit can also optimally allocate feedback resources based on the relevance of the data to provide efficient feedback. In this way, efficient feedback can be provided by adjusting the order of feedback based on the relevance of the data. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of feedback.

[0053] When providing feedback, the feedback unit can adjust the use of technical terminology in the feedback depending on the user's level of expertise. For example, if the user has technical expertise, the feedback unit can provide feedback using detailed technical terminology. Alternatively, if the user does not have technical expertise, the feedback unit can provide feedback using concise and easy-to-understand language. The feedback unit can also adjust the way the feedback is expressed depending on the user's level of expertise. This allows for more appropriate feedback to be provided by adjusting the use of technical terminology in the feedback depending on the user's level of expertise. Some or all of the above-described processing in the feedback unit can be performed using AI, for example, or without AI. For example, the feedback unit can input the user's level of expertise to the generation AI and cause the generation AI to use technical terminology.

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

[0055] The reception unit can analyze the user's past data provision history and select the optimal reception method. For example, it can preferentially suggest data provision methods (file upload, API integration, etc.) that the user has frequently used in the past. The reception unit can also analyze the format and content of data that the user has provided in the past and suggest the optimal data provision format. Furthermore, if the user has taken a long time to provide data in the past, the reception unit can also suggest a simplified data provision procedure. In this way, by analyzing the past data provision history, it is possible to provide the user with the optimal data reception method.

[0056] When accepting data, the accepting unit can filter the data based on the user's current project or field of interest. For example, the accepting unit can preferentially accept only data related to the user's ongoing project. The accepting unit can also automatically filter and accept highly relevant data based on the user's field of interest. Furthermore, the accepting unit can propose and accept optimal data based on data categories in which the user has shown interest in the past. In this way, by filtering data based on the user's project or field of interest, highly relevant data can be preferentially accepted.

[0057] When accepting data, the acceptance unit can select an appropriate acceptance means depending on the user's input method. For example, if the user desires voice input, the acceptance unit accepts data using voice recognition technology. If the user desires text input, the acceptance unit can also provide a text box to allow the user to easily enter data. Furthermore, if the user provides image data, the acceptance unit can analyze the data using image recognition technology and accept it. This allows for smooth data acceptance by selecting the optimal acceptance means depending on the user's input method.

[0058] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on highly important data to provide deep insights. The analysis unit can also perform a concise analysis on less important data to provide results that focus on the main points. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data to perform efficient analysis. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0059] During analysis, the analysis unit can apply an appropriate analysis algorithm depending on the data category. For example, a time series analysis algorithm can be applied to sales data to analyze sales trends. The analysis unit can also apply a clustering algorithm to customer data to classify customer segments. Furthermore, the analysis unit can apply a text mining algorithm to social media data to perform sentiment analysis. This improves the accuracy of analysis by applying the optimal analysis algorithm depending on the data category.

[0060] When generating a report, the generation unit can adjust the level of detail of the report based on the importance of the data. For example, a detailed report can be generated for highly important data to provide deep insight. The generation unit can also generate a concise report for less important data to provide information that focuses on the main points. Furthermore, the generation unit can optimally allocate report resources according to the importance of the data to generate reports efficiently. As a result, by adjusting the level of detail of the report based on the importance of the data, efficient report generation is possible.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit receives data from the user. Data provided by the user can include text data, numerical data, image data, etc. The reception unit can receive data from files uploaded by the user or through the API, and can also receive text data entered by the user directly. For example, it can receive a CSV file and prepare the data for analysis. Data received through the API is automatically imported into the system, and text data entered by the user is reflected in the system in real time. Step 2: The analysis unit uses generative AI to analyze the data received by the reception unit and extract important trends and insights. Analysis is performed using methods such as detecting frequent patterns and detecting anomalies. For example, sales data can be analyzed to extract seasonal sales trends, customer data can be analyzed to identify behavioral patterns of specific customer segments, and social media data can be analyzed to perform sentiment analysis. Step 3: The generation unit uses generation AI to generate reports based on the trends and insights extracted by the analysis unit. Reports are generated in the form of text reports, graph reports, etc. For example, it generates line graphs showing trends in sales data, pie charts showing behavioral patterns of customer segments, and heat maps showing the results of sentiment analysis. Step 4: The feedback department provides feedback on how to analyze the data and derive insights based on the generated report. This feedback includes advice on what analytical techniques should be used for a particular data set and how the data should be interpreted. For example, the feedback department may recommend time series analysis for sales data, clustering for customer data, or sentiment analysis for social media data.

[0063] (Example 2) A data analysis report automatic generation system according to an embodiment of the present invention analyzes data provided by a user, extracts important trends and insights using a generation AI, and generates a report. The data analysis report automatic generation system accepts data from a user, analyzes it using a generation AI, extracts important trends and insights, and generates a report. The data analysis report automatic generation system also provides feedback based on the generated report. For example, the data analysis report automatic generation system receives data provided by a user. For example, the system may identify seasonal sales trends from sales data or behavioral patterns of specific customer segments from customer data. The data analysis report automatic generation system then uses a generation AI to generate a consistent, easy-to-understand report based on the extracted trends and insights. This report is designed to visually convey information using graphs and charts. For example, it may include a line graph showing trends in sales data or a pie chart showing behavioral patterns of customer segments. Furthermore, the data analysis report automatic generation system provides feedback on how to analyze data and derive insights based on the generated report. For example, the system may provide advice on what analysis method to use for a specific data set or from what perspective to interpret the data. As a result, the data analysis report automatic generation system can improve the user's data analysis capabilities while significantly reducing the time and effort required to create a data analysis report. As a result, the data analysis report automatic generation system can improve the user's data analysis capabilities while significantly reducing the time and effort required to create a data analysis report. For example, a company's marketing department can efficiently analyze sales data and customer data and quickly develop marketing strategies. Also, a research institute can quickly analyze experimental data and effectively present research results.

[0064] The data analysis report automatic generation system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a feedback unit. The reception unit receives data from a user. The data provided by the user includes, but is not limited to, text data, numerical data, and image data. The reception unit receives, for example, a file uploaded by the user. The reception unit can also receive data via an API. The reception unit can also directly receive text data entered by the user. For example, the reception unit receives a CSV file provided by the user and prepares the data for analysis. The data received via the API is automatically imported into the system. The text data entered by the user is updated in real time. The analysis unit uses a generation AI to analyze the data received by the reception unit and extract important trends and insights. The analysis is performed by, for example, detecting frequent patterns and detecting anomalies, but is not limited to, such examples. For example, the analysis unit analyzes sales data and extracts seasonal sales trends. The analysis unit can also analyze customer data and identify behavioral patterns of specific customer segments. The analysis unit can also analyze social media data and perform sentiment analysis. For example, the analysis unit can perform time-series analysis of sales data to identify seasonal sales fluctuations. It can cluster customer data and classify customer demographics. It can perform text mining of social media data to analyze sentiment trends. The generation unit uses generative AI to generate a report based on the trends and insights extracted by the analysis unit. The report can be generated in the form of, for example, a text report or a graph report, but is not limited to these examples. For example, the generation unit can generate a line graph that shows trends in sales data. The generation unit can also generate a pie chart that shows behavior patterns of customer demographics. The generation unit can also generate a heat map that shows the results of the sentiment analysis. For example, the generation unit can generate a line graph to visually show trends in sales data. It can generate a pie chart to show behavior patterns of customer demographics. It can also generate a heat map to show the results of the sentiment analysis.The feedback unit provides feedback on how to analyze data and how to derive insights based on the generated report. The feedback includes, but is not limited to, advice on what analysis method should be used for a specific data set and from what perspective the data should be interpreted. For example, the feedback unit may advise that time series analysis should be performed on sales data. The feedback unit may also advise that clustering should be performed on customer data. The feedback unit may also advise that sentiment analysis should be performed on social media data. For example, the feedback unit may recommend time series analysis in the analysis of sales data, clustering in the analysis of customer data, or sentiment analysis in the analysis of social media data. Thus, the data analysis report automatic generation system according to the embodiment automates the creation of data analysis reports for users and improves their data analysis capabilities.

[0065] The reception unit can estimate the user's emotions and adjust the timing of data reception based on the estimated emotions. For example, if the user is feeling stressed, the reception unit temporarily delays data reception and attempts to receive the data again when the user is relaxed. Furthermore, if the user is concentrating, the reception unit can immediately receive data and quickly start the analysis process. Furthermore, if the user is tired, the reception unit can postpone data reception until the next day so that the data can be provided when the user is refreshed. By adjusting the timing of data reception according to the user's emotions, data can be received at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 AI, or may be performed without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0066] The reception unit can analyze the user's past data provision history and select the optimal reception method. For example, the reception unit can prioritize and suggest data provision methods (such as file uploads and API integration) that the user has frequently used in the past. The reception unit can also analyze the format and content of data provided by the user in the past and suggest the optimal data provision format. The reception unit can also suggest a simplified data provision procedure if the user has taken a long time to provide data in the past. In this way, by analyzing the past data provision history, the optimal data reception method can be provided to the user. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past data provision history into the generation AI and have the generation AI select the optimal reception method.

[0067] When receiving data, the reception unit can filter the data based on the user's current project or area of ​​interest. For example, the reception unit can preferentially receive only data related to the user's ongoing project. The reception unit can also automatically filter and receive highly relevant data based on the user's area of ​​interest. The reception unit can also suggest and receive optimal data based on data categories in which the user has previously shown interest. In this way, by filtering data based on the user's project or area of ​​interest, highly relevant data can be preferentially received. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's project information into a generation AI and cause the generation AI to filter highly relevant data.

[0068] When accepting data, the acceptance unit can select an appropriate acceptance means depending on the user's input method. For example, if the user desires voice input, the acceptance unit accepts the data using voice recognition technology. Furthermore, if the user desires text input, the acceptance unit can provide a text box to allow easy data entry. Furthermore, if the user provides image data, the acceptance unit can analyze the data using image recognition technology and accept the data. By selecting the optimal acceptance means depending on the user's input method, data can be accepted smoothly. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can input voice data to a generation AI and have the generation AI analyze the voice data.

[0069] The reception unit can estimate the user's emotions and determine the priority of data to be received based on the estimated emotions. For example, when the user is stressed, the reception unit postpones the reception of less important data and prioritizes the reception of more important data. Furthermore, when the user is relaxed, the reception unit can equally receive all data. Furthermore, when the user is in a hurry, the reception unit can prioritize the reception of data that requires rapid processing. Thus, by determining the priority of data according to the user's emotions, important data can be preferentially received. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, 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, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0070] When receiving data, the reception unit can prioritize receiving highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes receiving data related to that area. The reception unit can also prioritize receiving nearby data based on the user's location information. When the user is moving, the reception unit can also prioritize receiving data that is most relevant to the user's current location. In this way, highly relevant data can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's location information data to the generation AI and cause the generation AI to select highly relevant data.

[0071] The reception unit can analyze the user's social media activity and receive related data when receiving data. For example, the reception unit can automatically receive data shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and preferentially receive related data. The reception unit can also receive related data with reference to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, highly relevant data can be preferentially received. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to select related data.

[0072] The reception unit can customize the reception method by reflecting the user's past feedback when receiving data. The reception unit can, for example, propose an optimal data reception method based on feedback provided by the user in the past. The reception unit can also improve and streamline the data reception procedure based on the user's past feedback. The reception unit can also resolve problems previously pointed out by the user to ensure smooth data reception. This makes it possible to provide an optimal data reception method by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using 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.

[0073] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results to enable deeper insights. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. This allows for more appropriate analysis results to be provided by adjusting the way the analysis is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0074] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on highly important data to provide deep insights. The analysis unit can also perform a concise analysis on less important data to provide results that focus on the main points. The analysis unit can also optimally allocate analysis resources according to the importance of the data to perform efficient analysis. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0075] During analysis, the analysis unit can apply an appropriate analysis algorithm depending on the data category. For example, the analysis unit can apply a time series analysis algorithm to sales data to analyze sales trends. The analysis unit can also apply a clustering algorithm to customer data to classify customer segments. The analysis unit can also apply a text mining algorithm to social media data to perform sentiment analysis. This improves the accuracy of analysis by applying the optimal analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the data category into the generation AI and have the generation AI apply the appropriate analysis algorithm.

[0076] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit applies the optimal analysis method to similar data based on the user's past analysis results. The analysis unit can also adjust parameters to improve the accuracy of the analysis based on the user's past analysis results. The analysis unit can also optimally allocate analysis resources by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0077] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result to gain deeper insights. Furthermore, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. This allows for adjusting the length of the analysis according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0078] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. For example, the analysis unit prioritizes analysis of the most recent data to provide real-time insights. The analysis unit can also postpone analysis of data that was submitted earlier. The analysis unit can also optimally allocate analysis resources based on the time of submission to perform efficient analysis. In this way, efficient analysis can be performed by determining the priority of analysis based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data submission to the generation AI and have the generation AI determine the analysis priority.

[0079] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data, making it easier to obtain overall insight. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also optimally allocate analysis resources based on the relevance of the data to perform efficient analysis. In this way, adjusting the order of analysis based on the relevance of the data enables efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0080] 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 analysis results using detailed technical terminology. Alternatively, if the user does not have technical expertise, the analysis unit can provide analysis results using concise, easy-to-understand language. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise into the generation AI and have the generation AI use technical terminology.

[0081] The generation unit can estimate the user's emotions and adjust the report presentation method based on the estimated emotions. For example, if the user is nervous, the generation unit generates a simple, highly visible report. Furthermore, if the user is relaxed, the generation unit can generate a report containing detailed information. Furthermore, if the user is in a hurry, the generation unit can generate a concise report that focuses on the main points. This allows the report presentation method to be adjusted according to the user's emotions, thereby providing a more appropriate report. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, 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, an AI, or may be performed without using an AI. For example, the generation unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0082] When generating a report, the generation unit can adjust the level of detail of the report based on the importance of the data. For example, the generation unit can generate a detailed report for data with high importance to provide deep insight. The generation unit can also generate a concise report for data with low importance to provide information that focuses on the main points. The generation unit can also optimally allocate report resources according to the importance of the data to generate efficient reports. In this way, by adjusting the level of detail of the report based on the importance of the data, efficient report generation can be achieved. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the report.

[0083] When generating a report, the generation unit can apply an appropriate report generation algorithm depending on the data category. For example, the generation unit can generate a report using a time series graph for sales data. The generation unit can also generate a report using clustering results for customer data. The generation unit can also generate a report using sentiment analysis results for social media data. This improves the accuracy of the report by applying the optimal report generation algorithm depending on the data category. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the data category into the generation AI and cause the generation AI to apply an appropriate report generation algorithm.

[0084] When generating a report, the generation unit can improve the accuracy of the report by referring to the user's past report results. For example, the generation unit applies an optimal report generation method to similar data based on reports created by the user in the past. The generation unit can also adjust parameters for improving the accuracy of the report based on the user's past report results. The generation unit can also optimally allocate report resources by referring to the user's past report results. In this way, the accuracy of the report is improved by referring to the user's past report results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past report results into the generation AI and cause the generation AI to improve the accuracy of the report.

[0085] The generation unit can estimate the user's emotions and adjust the length of the report based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point report. If the user is relaxed, the generation unit can generate a longer report with detailed explanations. If the user is excited, the generation unit can generate a report with visually stimulating effects. By adjusting the length of the report according to the user's emotions, a more appropriate report can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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, AI, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0086] When generating a report, the generation unit can determine the priority of the report based on the time of data submission. For example, the generation unit can prioritize the most recent data in the report to provide real-time insight. The generation unit can also postpone generating a report for data that was submitted earlier. The generation unit can also optimally allocate report resources based on the time of submission to generate reports efficiently. In this way, by determining the priority of reports based on the time of data submission, efficient report generation can be achieved. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the time of data submission to the generation AI and have the generation AI determine the priority of the reports.

[0087] When generating a report, the generation unit can adjust the order of the reports based on the relevance of the data. For example, the generation unit can prioritize highly relevant data in the report, making it easier to obtain overall insight. The generation unit can also postpone generating reports for less relevant data. The generation unit can also optimally allocate report resources based on the relevance of the data, thereby generating reports efficiently. In this way, adjusting the order of the reports based on the relevance of the data enables efficient report generation. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the relevance of the data into the generation AI and cause the generation AI to adjust the order of the reports.

[0088] When generating a report, the generation unit can adjust the use of technical terminology in the report according to the user's level of expertise. For example, if the user has technical expertise, the generation unit can generate the report using detailed technical terminology. Alternatively, if the user does not have technical expertise, the generation unit can generate the report using concise and easy-to-understand language. The generation unit can also adjust the way the report is expressed according to the user's level of expertise. This allows for adjusting the use of technical terminology in the report according to the user's level of expertise, thereby providing a more appropriate report. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's level of expertise into the generation AI and have the generation AI execute the use of technical terminology.

[0089] The feedback unit can estimate the user's emotions and adjust the way the feedback is expressed based on the estimated emotions. For example, if the user is nervous, the feedback unit can provide simple, highly visible feedback. Furthermore, if the user is relaxed, the feedback unit can provide feedback with detailed information. Furthermore, if the user is in a hurry, the feedback unit can provide concise feedback that focuses on the main points. This allows for more appropriate feedback to be provided by adjusting the way the feedback is expressed based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 feedback unit can be performed using, for example, an AI, or without an AI. For example, the feedback unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0090] When providing feedback, the feedback unit can adjust the level of detail of the feedback based on the importance of the data. For example, the feedback unit can provide detailed feedback for data with high importance, allowing for deeper insight. The feedback unit can also provide concise feedback for data with low importance, providing information that focuses on the main points. The feedback unit can also optimally allocate feedback resources according to the importance of the data and provide efficient feedback. In this way, efficient feedback can be provided by adjusting the level of detail of the feedback based on the importance of the data. Some or all of the above-mentioned processing in the feedback unit may be performed using, or without, AI, for example. For example, the feedback unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the feedback.

[0091] When providing feedback, the feedback unit can apply different feedback algorithms depending on the data category. For example, the feedback unit can provide feedback based on time series analysis results for sales data. The feedback unit can also provide feedback based on clustering results for customer data. The feedback unit can also provide feedback based on sentiment analysis results for social media data. This improves the accuracy of feedback by applying the optimal feedback algorithm depending on the data category. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input the data category to the generation AI and cause the generation AI to apply an appropriate feedback algorithm.

[0092] When providing feedback, the feedback unit can improve the accuracy of the feedback by referring to the user's past feedback results. The feedback unit, for example, provides optimal feedback for similar data based on feedback the user has received in the past. The feedback unit can also adjust parameters for improving the accuracy of the feedback based on the user's past feedback results. The feedback unit can also optimally allocate feedback resources by referring to the user's past feedback results. In this way, the accuracy of the feedback is improved by referring to the user's past feedback results. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past feedback results into the generation AI and cause the generation AI to improve the accuracy of the feedback.

[0093] The feedback unit can estimate the user's emotions and adjust the length of the feedback based on the estimated emotions. For example, if the user is in a hurry, the feedback unit can provide short, to-the-point feedback. If the user is relaxed, the feedback unit can provide longer feedback with detailed explanations. If the user is excited, the feedback unit can provide feedback with visually stimulating effects. By adjusting the length of the feedback according to the user's emotions, more appropriate feedback can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 feedback unit can be performed using, for example, an AI, or without an AI. For example, the feedback unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0094] When providing feedback, the feedback unit can determine the priority of the feedback based on the time of data submission. For example, the feedback unit can provide feedback preferentially to the most recent data and provide real-time insight. The feedback unit can also provide feedback later to data submitted earlier. The feedback unit can also optimally allocate feedback resources based on the time of submission and provide efficient feedback. In this way, efficient feedback can be provided by determining the priority of feedback based on the time of data submission. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the time of data submission to the generation AI and have the generation AI determine the priority of feedback.

[0095] When providing feedback, the feedback unit can adjust the order of feedback based on the relevance of the data. For example, the feedback unit can provide feedback preferentially to highly relevant data, making it easier to obtain overall insight. The feedback unit can also provide feedback later to less relevant data. The feedback unit can also optimally allocate feedback resources based on the relevance of the data to provide efficient feedback. In this way, efficient feedback can be provided by adjusting the order of feedback based on the relevance of the data. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of feedback.

[0096] When providing feedback, the feedback unit can adjust the use of technical terminology in the feedback depending on the user's level of expertise. For example, if the user has technical expertise, the feedback unit can provide feedback using detailed technical terminology. Alternatively, if the user does not have technical expertise, the feedback unit can provide feedback using concise and easy-to-understand language. The feedback unit can also adjust the way the feedback is expressed depending on the user's level of expertise. This allows for more appropriate feedback to be provided by adjusting the use of technical terminology in the feedback depending on the user's level of expertise. Some or all of the above-described processing in the feedback unit can be performed using AI, for example, or without AI. For example, the feedback unit can input the user's level of expertise to the generation AI and cause the generation AI to use technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, and feedback 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 data from a user via the reception device 38 of the smart device 14 or the communication I / F 26 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the data using a generation AI to extract important trends and insights. The generation unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and generates a report based on the analysis results. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides feedback based on the generated report. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, and feedback 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 data from a user via the microphone 238 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the data using a generation AI to extract important trends and insights. The generation unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and generates a report based on the analysis results. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides feedback based on the generated report. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and feedback 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 data from a user via the microphone 238 of the headset type terminal 314 or the communication I / F 26 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the data using a generation AI to extract important trends and insights. The generation unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and generates a report based on the analysis results. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides feedback based on the generated report. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and feedback 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 data from a user via the microphone 238 of the robot 414 or the communication I / F 26 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the data using a generative AI to extract important trends and insights. The generation unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and generates a report based on the analysis results. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides feedback based on the generated report.

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

[0098] The reception unit can analyze the user's past data provision history and select the optimal reception method. For example, it can preferentially suggest data provision methods (file upload, API integration, etc.) that the user has frequently used in the past. The reception unit can also analyze the format and content of data that the user has provided in the past and suggest the optimal data provision format. Furthermore, if the user has taken a long time to provide data in the past, the reception unit can also suggest a simplified data provision procedure. In this way, by analyzing the past data provision history, it is possible to provide the user with the optimal data reception method.

[0099] The reception unit can estimate the user's emotions and adjust the timing of data reception based on the estimated emotions. For example, if the user is feeling stressed, the reception unit can temporarily delay data reception and try again when the user is relaxed. In addition, if the user is concentrating, the reception unit can immediately receive data and quickly start the analysis process. Furthermore, if the user is tired, the reception unit can postpone data reception until the next day so that the data can be provided when the user is refreshed. In this way, by adjusting the timing of data reception according to the user's emotions, data can be received at a more appropriate time.

[0100] When accepting data, the accepting unit can filter the data based on the user's current project or field of interest. For example, the accepting unit can preferentially accept only data related to the user's ongoing project. The accepting unit can also automatically filter and accept highly relevant data based on the user's field of interest. Furthermore, the accepting unit can propose and accept optimal data based on data categories in which the user has shown interest in the past. In this way, by filtering data based on the user's project or field of interest, highly relevant data can be preferentially accepted.

[0101] When accepting data, the acceptance unit can select an appropriate acceptance means depending on the user's input method. For example, if the user desires voice input, the acceptance unit accepts data using voice recognition technology. If the user desires text input, the acceptance unit can also provide a text box to allow the user to easily enter data. Furthermore, if the user provides image data, the acceptance unit can analyze the data using image recognition technology and accept it. This allows for smooth data acceptance by selecting the optimal acceptance means depending on the user's input method.

[0102] The reception unit can estimate the user's emotions and determine the priority of data to be received based on the estimated emotions. For example, if the user is feeling stressed, the reception unit postpones the reception of data with low importance and prioritizes the reception of data with high importance. The reception unit can also receive all data equally when the user is relaxed. Furthermore, if the user is in a hurry, the reception unit can also prioritize the reception of data that requires quick processing. In this way, by determining the priority of data according to the user's emotions, important data can be received with priority.

[0103] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on highly important data to provide deep insights. The analysis unit can also perform a concise analysis on less important data to provide results that focus on the main points. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data to perform efficient analysis. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0104] During analysis, the analysis unit can apply an appropriate analysis algorithm depending on the data category. For example, a time series analysis algorithm can be applied to sales data to analyze sales trends. The analysis unit can also apply a clustering algorithm to customer data to classify customer segments. Furthermore, the analysis unit can apply a text mining algorithm to social media data to perform sentiment analysis. This improves the accuracy of analysis by applying the optimal analysis algorithm depending on the data category.

[0105] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results to enable deeper insights. Furthermore, if the user is in a hurry, the analysis unit can also provide concise analysis results that focus on the main points. This allows the analysis unit to provide more appropriate analysis results by adjusting the way the analysis is presented depending on the user's emotions.

[0106] The generation unit can estimate the user's emotions and adjust the way the report is presented based on the estimated emotions. For example, if the user is nervous, the generation unit can generate a simple, highly visible report. If the user is relaxed, the generation unit can also generate a report that includes detailed information. Furthermore, if the user is in a hurry, the generation unit can also generate a concise report that focuses on the main points. In this way, by adjusting the way the report is presented according to the user's emotions, it is possible to provide a more appropriate report.

[0107] When generating a report, the generation unit can adjust the level of detail of the report based on the importance of the data. For example, a detailed report can be generated for highly important data to provide deep insight. The generation unit can also generate a concise report for less important data to provide information that focuses on the main points. Furthermore, the generation unit can optimally allocate report resources according to the importance of the data to generate reports efficiently. As a result, by adjusting the level of detail of the report based on the importance of the data, efficient report generation is possible.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The reception unit receives data from the user. Data provided by the user can include text data, numerical data, image data, etc. The reception unit can receive data from files uploaded by the user or through the API, and can also receive text data entered by the user directly. For example, it can receive a CSV file and prepare the data for analysis. Data received through the API is automatically imported into the system, and text data entered by the user is reflected in the system in real time. Step 2: The analysis unit uses generative AI to analyze the data received by the reception unit and extract important trends and insights. Analysis is performed using methods such as detecting frequent patterns and detecting anomalies. For example, sales data can be analyzed to extract seasonal sales trends, customer data can be analyzed to identify behavioral patterns of specific customer segments, and social media data can be analyzed to perform sentiment analysis. Step 3: The generation unit uses generation AI to generate reports based on the trends and insights extracted by the analysis unit. Reports are generated in the form of text reports, graph reports, etc. For example, it generates line graphs showing trends in sales data, pie charts showing behavioral patterns of customer segments, and heat maps showing the results of sentiment analysis. Step 4: The feedback department provides feedback on how to analyze the data and derive insights based on the generated report. This feedback includes advice on what analytical techniques should be used for a particular data set and how the data should be interpreted. For example, the feedback department may recommend time series analysis for sales data, clustering for customer data, or sentiment analysis for social media data.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0111] 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.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0127] 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.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0131] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the 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.

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

[0142] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

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

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

[0147] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0160] 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.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] [Explanation of symbols]

[0182] 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 data from a user; an analysis unit that analyzes the data received by the reception unit and extracts important trends and insights; a generator for generating a report based on the trends and insights extracted by the analyzer; a feedback unit that provides feedback based on the report generated by the generation unit. A system characterized by:

2. The reception unit Estimate user emotions and adjust the timing of data reception based on the estimated emotions.

2. The system of claim 1.

3. The reception unit Analyze the user's past data provision history and select the appropriate reception method 2. The system of claim 1.

4. The reception unit As data is received, it is filtered based on the user's current project or area of ​​interest.

2. The system of claim 1.

5. The reception unit When accepting data, select the appropriate acceptance method depending on the user's input method.

2. The system of claim 1.

6. The reception unit Estimate the user's emotions and prioritize the data to be accepted based on the estimated emotions.

2. The system of claim 1.

7. The reception unit When accepting data, prioritize data that is highly relevant based on the user's geographic location information.

2. The system of claim 1.

8. The reception unit Upon receiving the data, analyze the user's social media activity and receive relevant data.

2. The system of claim 1.

Images