system

Abstract

The system according to the embodiment aims to efficiently analyze data in different formats and locations, and to extract, collect, and output necessary information. [Solution] The system according to the embodiment comprises an analysis unit, an extraction unit, a collection unit, and an output unit. The analysis unit automatically analyzes data in different formats and locations. The extraction unit extracts necessary information from the data analyzed by the analysis unit. The collection unit collects data based on the information extracted by the extraction unit. The output unit outputs the data collected by the collection unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have presented challenges in efficiently analyzing data stored in different formats and locations and extracting necessary information.

[0005] The system according to the embodiment aims to efficiently analyze data in different formats and locations, and to extract, collect, and output necessary information. [Means for solving the problem]

[0006] The system according to this embodiment comprises an analysis unit, an extraction unit, a data collection unit, and an output unit. The analysis unit automatically analyzes data located in different formats and locations. The extraction unit extracts necessary information from the data analyzed by the analysis unit. The data collection unit collects data based on the information extracted by the extraction unit. The output unit outputs the data collected by the data collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently analyze data in different formats and locations, and extract, collect, and output necessary information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The data analysis system according to an embodiment of the present invention is a system that automatically analyzes data stored in various formats and locations within a company, collects the necessary data, and outputs it. This data analysis system automatically analyzes data in different formats and locations, extracts the necessary information from the analyzed data, and collects data based on conditions specified by the user. The collected data is output by the data analysis system, and the data source is also indicated, making it easy to make corrections and provide external explanations. This significantly reduces the man-hours required for data collection and output. For example, the data analysis system automatically analyzes data stored in various formats and locations within a company. For example, it targets data in different formats and locations such as Excel files, PDFs, databases, and cloud storage. The data analysis system analyzes this data and extracts the necessary information. Next, the data analysis system collects the necessary data based on the extracted information. For example, it collects data based on conditions specified by the user, such as data related to a specific project or data related to a specific period. The collected data is output by the data analysis system. The output format is provided in various formats such as reports, graphs, and tables, depending on the format specified by the user. In addition, the data source is also indicated in the output, making it easy to make corrections and provide external explanations. This system significantly reduces the time and effort required for data collection and output. For example, tasks such as data collection, analysis, and report creation, which were previously done manually, are automated, shortening working hours and enabling more efficient operations. As a result, data analysis systems can significantly reduce the time and effort required for data collection and output.

[0029] The data analysis system according to this embodiment comprises an analysis unit, an extraction unit, a collection unit, and an output unit. The analysis unit automatically analyzes data in different formats and locations. For example, the analysis unit analyzes data in different formats and locations such as Excel files, PDFs, databases, and cloud storage. For example, the analysis unit analyzes cell data in an Excel file and extracts data containing specific keywords. The analysis unit can also analyze text data in a PDF file and extract data containing specific phrases. Furthermore, the analysis unit can analyze table data in a database and extract records that match specific conditions. Data stored in cloud storage is also analyzed by the analysis unit, and the necessary information is extracted. The extraction unit extracts the necessary information from the data analyzed by the analysis unit. For example, the extraction unit extracts data related to a specific project or data related to a specific period. For example, the extraction unit extracts relevant data based on a project ID or project name. The extraction unit can also filter data by specifying a start date and end date to extract data related to a specific period. The collection unit collects data based on the information extracted by the extraction unit. For example, the collection unit collects data based on conditions specified by the user. For instance, the collection unit can collect data by specifying a project ID to collect data related to a specific project. The collection unit can also collect data by specifying a start date and end date to collect data for a specific period. The output unit outputs the data collected by the collection unit. For example, the output unit outputs the collected data in the form of reports, graphs, tables, etc. For example, the output unit generates a report based on the collected data and provides it to the user. The output unit can also generate a graph based on the collected data to visually display the data. Furthermore, the output unit can generate a table based on the collected data to display the data details. As a result, the data analysis system according to this embodiment can significantly reduce the man-hours required for data collection and output.

[0030] The analysis unit can analyze data in different formats and locations, such as Excel files, PDFs, databases, and cloud storage. For example, the analysis unit can analyze cell data in an Excel file and extract data containing specific keywords. For example, the analysis unit can specify a specific sheet in an Excel file to analyze the data. The analysis unit can also analyze text data in a PDF file and extract data containing specific phrases. For example, the analysis unit can specify a specific page in a PDF file to analyze the data. The analysis unit can also analyze table data in a database and extract records that match specific conditions. For example, the analysis unit can specify a specific table in a database to analyze the data. Data stored in cloud storage can also be analyzed by the analysis unit to extract required information. For example, the analysis unit can specify a specific folder in cloud storage to analyze the data. This allows required information to be extracted from a wide range of data sources by analyzing data in different formats and locations.

[0031] The extraction unit can extract data related to a specific project or data related to a specific period from the analyzed data. The extraction unit extracts related data based on, for example, a project ID or a project name. For example, the extraction unit can filter data by specifying a project ID. The extraction unit can also filter data by specifying a start date and an end date to extract data related to a specific period. For example, the extraction unit can filter data by specifying a start date and an end date to extract data related to a specific period. This allows the user to efficiently collect information they need by extracting data related to a specific project or period.

[0032] The collection unit can collect data based on conditions specified by a user. For example, in order to collect data related to a specific project, the collection unit collects data by specifying a project ID. For example, the collection unit can collect data by specifying a project ID. The collection unit can also collect data by specifying a start date and an end date in order to collect data related to a specific period. For example, the collection unit can collect data by specifying a start date and an end date. This allows required information to be collected efficiently by collecting data based on conditions specified by a user.

[0033] The output unit can output the collected data in the form of a report, graph, or table. For example, the output unit can generate a report based on the collected data and provide it to the user. For example, the output unit can generate a report based on the collected data and provide it to the user. The output unit can also generate a graph based on the collected data and visually display the data. For example, the output unit can generate a graph based on the collected data and visually display the data. Furthermore, the output unit can generate a table based on the collected data and display details of the data. For example, the output unit can generate a table based on the collected data and display details of the data. In this way, by outputting the collected data in various formats, the user can more easily visually understand the data.

[0034] The output unit can display the data source in the output. For example, the output unit can display the data source in the output. By showing the data source in the output, corrections and external explanations can be easily made.

[0035] During analysis, the analysis unit can evaluate the reliability of the data and prioritize analyzing highly reliable data. For example, the analysis unit can check the source of the data and prioritize analyzing highly reliable data. For example, the analysis unit can check the source of the data and prioritize analyzing highly reliable data. The analysis unit can also evaluate the update frequency of the data and prioritize analyzing the latest data. For example, the analysis unit can evaluate the update frequency of the data and prioritize analyzing the latest data. Furthermore, the analysis unit can check the consistency of the data and prioritize analyzing consistent data. For example, the analysis unit can check the consistency of the data and prioritize analyzing consistent data. This prioritizes analyzing highly reliable data, thereby improving the accuracy of the analysis results.

[0036] During analysis, the analysis unit can apply different analysis algorithms depending on the format of the data. For example, the analysis unit applies a natural language processing algorithm to text data. For example, the analysis unit can apply a natural language processing algorithm to text data. The analysis unit can also apply a statistical analysis algorithm to numerical data. For example, the analysis unit can apply a statistical analysis algorithm to numerical data. Furthermore, the analysis unit can also apply an image recognition algorithm to image data. For example, the analysis unit can apply an image recognition algorithm to image data. In this way, by applying an analysis algorithm depending on the format of the data, the accuracy of the analysis is improved.

[0037] The analysis unit can perform analysis based on the geographical distribution of the data. For example, the analysis unit can map the geographical distribution of the data and perform analysis for each region. The analysis unit can also analyze data trends while considering geographical factors. For example, the analysis unit can analyze data trends while considering geographical factors. Furthermore, the analysis unit can identify geographical clusters and perform detailed analysis for each cluster. For example, the analysis unit can identify geographical clusters and perform detailed analysis for each cluster. This makes detailed analysis for each region possible by considering the geographical distribution of the data.

[0038] The analysis unit can improve the accuracy of the analysis by referring to relevant literature during the analysis. For example, the analysis unit can automatically search for relevant literature and incorporate it into the analysis. For example, the analysis unit can automatically search for relevant literature and incorporate it into the analysis. The analysis unit can also incorporate citation information from relevant literature into the analysis. For example, the analysis unit can incorporate citation information from relevant literature into the analysis. Furthermore, the analysis unit can add summaries of relevant literature to the analysis results. For example, the analysis unit can add summaries of relevant literature to the analysis results. This improves the accuracy of the analysis by referring to relevant literature.

[0039] The extraction unit can improve the accuracy of extraction based on the interrelationships of data during extraction. The extraction unit, for example, analyzes the interrelationships of data and extracts highly related information. For example, the extraction unit can analyze the interrelationships of data and extract highly related information. The extraction unit can also calculate a correlation coefficient of data and preferentially extract highly correlated data. For example, the extraction unit can calculate a correlation coefficient of data and preferentially extract highly correlated data. Furthermore, the extraction unit can perform a network analysis of data and extract important nodes. For example, the extraction unit can perform a network analysis of data and extract important nodes. In this way, the accuracy of extraction is improved by taking the interrelationships of data into consideration.

[0040] The extraction unit can perform extraction while taking into consideration attribute information of the data submitter. The extraction unit extracts important information while taking into consideration, for example, the job title and department of the data submitter. For example, the extraction unit can extract important information while taking into consideration the job title and department of the data submitter. The extraction unit can also evaluate the past performance of the data submitter and extract highly reliable information. For example, the extraction unit can evaluate the past performance of the data submitter and extract highly reliable information. Furthermore, the extraction unit can extract highly relevant information while taking into consideration the field of expertise of the data submitter. For example, the extraction unit can extract highly relevant information while taking into consideration the field of expertise of the data submitter. In this way, highly reliable information can be extracted by taking into consideration the attribute information of the data submitter.

[0041] The extraction unit can perform extraction based on the geographical distribution of the data. For example, the extraction unit can map the geographical distribution of the data and extract information for each region. The extraction unit can also extract data trends by considering geographical factors. For example, the extraction unit can extract data trends by considering geographical factors. Furthermore, the extraction unit can identify geographical clusters and extract detailed information for each cluster. For example, the extraction unit can identify geographical clusters and extract detailed information for each cluster. This allows for the extraction of detailed information for each region by considering the geographical distribution of the data.

[0042] The extraction unit can improve the accuracy of the extraction by referring to related literature during the extraction process. For example, the extraction unit can automatically search for related literature and incorporate it into the extraction. For example, the extraction unit can automatically search for related literature and incorporate it into the extraction. The extraction unit can also incorporate citation information from related literature into the extraction. For example, the extraction unit can incorporate citation information from related literature into the extraction. Furthermore, the extraction unit can add summaries of related literature to the extraction results. For example, the extraction unit can add summaries of related literature to the extraction results. This improves the accuracy of the extraction by referring to related literature.

[0043] The data collection unit can evaluate the reliability of the data during collection and prioritize the collection of highly reliable data. For example, the data collection unit can verify the source of the data and prioritize the collection of highly reliable data. The data collection unit can also evaluate the frequency of data updates and prioritize the collection of the most recent data. For example, the data collection unit can evaluate the frequency of data updates and prioritize the collection of the most recent data. Furthermore, the data collection unit can check the consistency of the data and prioritize the collection of consistent data. For example, the data collection unit can check the consistency of the data and prioritize the collection of consistent data. By prioritizing the collection of highly reliable data, the accuracy of the collection results is improved.

[0044] The data collection unit can apply different collection algorithms depending on the data format during collection. For example, it can apply a natural language processing algorithm to text data. It can also apply a statistical analysis algorithm to numerical data. Furthermore, it can apply an image recognition algorithm to image data. This improves the accuracy of data collection by applying a collection algorithm appropriate to the data format.

[0045] The collection unit can perform collection based on the geographical distribution of data at the time of collection. For example, the collection unit can map the geographical distribution of data and collect information for each region. For example, the collection unit can map the geographical distribution of data and collect information for each region. The collection unit can also collect data trends taking geographical factors into consideration. For example, the collection unit can collect data trends taking geographical factors into consideration. Furthermore, the collection unit can identify geographical clusters and collect detailed information for each cluster. For example, the collection unit can identify geographical clusters and collect detailed information for each cluster. In this way, detailed information for each region can be collected by taking the geographical distribution of data into consideration.

[0046] The collection unit can improve the accuracy of the collection by referring to literature related to the data during collection. The collection unit, for example, automatically searches for literature related to the data and reflects it in the collection. For example, the collection unit can automatically search for literature related to the data and reflect it in the collection. The collection unit can also incorporate citation information of the related literature into the collection. For example, the collection unit can incorporate citation information of the related literature into the collection. Furthermore, the collection unit can add summaries of the related literature to the collection results. For example, the collection unit can add summaries of the related literature to the collection results. This improves the accuracy of the collection by referring to the related literature.

[0047] The output unit can adjust the level of detail of the output based on the importance of the data at the time of output. For example, the output unit can add a detailed explanation to data with high importance. For example, the output unit can add a detailed explanation to data with high importance. The output unit can also add a concise explanation to data with low importance. For example, the output unit can add a concise explanation to data with low importance. Furthermore, the output unit can change the format of the output according to the importance. For example, the output unit can change the format of the output according to the importance. In this way, by adjusting the level of detail of the output based on the importance of the data, it is possible to efficiently provide the information that the user needs.

[0048] The output unit can apply different output algorithms depending on the category of data at the time of output. For example, the output unit can apply a natural language generation algorithm to text data. For example, the output unit can apply a natural language generation algorithm to text data. The output unit can also apply a statistical analysis algorithm to numerical data. For example, the output unit can apply a statistical analysis algorithm to numerical data. The output unit can also apply an image generation algorithm to image data. For example, the output unit can apply an image generation algorithm to image data. In this way, by applying an output algorithm depending on the category of data, the accuracy of the output is improved.

[0049] At the time of output, the output unit can determine the priority of the output based on the time of submission of the data. The output unit, for example, prioritizes output of the latest data. For example, the output unit can prioritize output of the latest data. The output unit can also postpone output of older data. For example, the output unit can postpone output of older data. Furthermore, the output unit can also adjust the order of the output based on the time of submission. For example, the output unit can adjust the order of the output based on the time of submission. In this way, by determining the priority of the output based on the time of submission of the data, the latest information can be provided preferentially.

[0050] The output unit can adjust the order of output based on the relevance of the data at the time of output. The output unit, for example, prioritizes output of highly relevant data. For example, the output unit can prioritize output of highly relevant data. Furthermore, the output unit can postpone output of less relevant data. For example, the output unit can postpone output of less relevant data. Furthermore, the output unit can adjust the order of output based on the relevance of the data. For example, the output unit can adjust the order of output based on the relevance of the data. In this way, by adjusting the order of output based on the relevance of the data, highly relevant information can be provided preferentially.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The data analysis system may further include a duplicate detection unit that detects duplicate data and automatically removes the duplicate data. For example, the duplicate detection unit may detect and remove duplicates when the same data exists in multiple formats or locations. The duplicate detection unit may also compare the content of data and treat highly similar data as duplicates. Furthermore, the duplicate detection unit may detect only specific data as duplicates based on user-specified conditions. This eliminates data duplication, improving data accuracy and efficiency.

[0053] When analyzing data, the analysis unit can track changes in the data and detect patterns of change. For example, the analysis unit can analyze changes in the data over time and detect trends. The analysis unit can also detect abnormal changes and issue alerts. Furthermore, the analysis unit can make future predictions based on changes in the data. This allows deeper insights to be gained by tracking changes in the data.

[0054] When extracting data, the extraction unit can evaluate the quality of the data and preferentially extract high-quality data. For example, the extraction unit can evaluate the completeness and consistency of the data and extract high-quality data. The extraction unit can also evaluate the reliability of the data and extract highly reliable data. Furthermore, the extraction unit can evaluate the accuracy of the data and extract highly accurate data. In this way, by taking the quality of the data into consideration, highly reliable information can be provided.

[0055] When collecting data, the collection unit can determine the collection priority based on the importance of the data. For example, the collection unit can prioritize collecting data with high importance. The collection unit can also postpone collecting data with low importance. Furthermore, the collection unit can adjust the collection priority based on the importance specified by the user. This allows for efficient data collection by prioritizing the collection of important data.

[0056] The output unit can visualize the data at the time of output, allowing the user to intuitively understand the data. For example, the output unit can convert the data into graphs or charts and display them visually. The output unit can also generate a heat map of the data to visually show the distribution of the data. Furthermore, the output unit can provide an interactive dashboard of the data, allowing the user to analyze the data while manipulating it. Thus, visualizing the data makes it easier for the user to intuitively understand the data.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The analysis unit automatically analyzes data in different formats and locations. For example, it analyzes data in different formats and locations, such as Excel files, PDFs, databases, and cloud storage. The analysis unit analyzes cell data in Excel files and extracts data containing specific keywords. It can also analyze text data in PDF files and extract data containing specific phrases. It can also analyze table data in databases and extract records that match specific conditions. The analysis unit also analyzes data stored in cloud storage to extract the necessary information. Step 2: The extraction unit extracts the necessary information from the data analyzed by the analysis unit. For example, it extracts data related to a specific project or a specific period. The extraction unit extracts related data based on the project ID or project name. It can also filter data by specifying the start and end dates. Step 3: The collection unit collects data based on the information extracted by the extraction unit. For example, the collection unit collects data based on conditions specified by the user. To collect data related to a specific project, the collection unit collects data by specifying a project ID. In addition, to collect data related to a specific period, the collection unit can also collect data by specifying a start date and an end date. Step 4: The output unit outputs the data collected by the collection unit. For example, it outputs the collected data in the form of reports, graphs, tables, etc. The output unit generates a report based on the collected data and provides it to the user. It can also generate graphs based on the collected data to display the data visually. Furthermore, it can generate tables based on the collected data to display the data details.

[0059] (Example 2) The data analysis system according to an embodiment of the present invention is a system that automatically analyzes data stored in various formats and locations within a company, collects the necessary data, and outputs it. This data analysis system automatically analyzes data in different formats and locations, extracts the necessary information from the analyzed data, and collects data based on conditions specified by the user. The collected data is output by the data analysis system, and the data source is also indicated, making it easy to make corrections and provide external explanations. This significantly reduces the man-hours required for data collection and output. For example, the data analysis system automatically analyzes data stored in various formats and locations within a company. For example, it targets data in different formats and locations such as Excel files, PDFs, databases, and cloud storage. The data analysis system analyzes this data and extracts the necessary information. Next, the data analysis system collects the necessary data based on the extracted information. For example, it collects data based on conditions specified by the user, such as data related to a specific project or data related to a specific period. The collected data is output by the data analysis system. The output format is provided in various formats such as reports, graphs, and tables, depending on the format specified by the user. In addition, the data source is also indicated in the output, making it easy to make corrections and provide external explanations. This system significantly reduces the time and effort required for data collection and output. For example, tasks such as data collection, analysis, and report creation, which were previously done manually, are automated, shortening working hours and enabling more efficient operations. As a result, data analysis systems can significantly reduce the time and effort required for data collection and output.

[0060] The data analysis system according to this embodiment comprises an analysis unit, an extraction unit, a collection unit, and an output unit. The analysis unit automatically analyzes data in different formats and locations. For example, the analysis unit analyzes data in different formats and locations such as Excel files, PDFs, databases, and cloud storage. For example, the analysis unit analyzes cell data in an Excel file and extracts data containing specific keywords. The analysis unit can also analyze text data in a PDF file and extract data containing specific phrases. Furthermore, the analysis unit can analyze table data in a database and extract records that match specific conditions. Data stored in cloud storage is also analyzed by the analysis unit, and the necessary information is extracted. The extraction unit extracts the necessary information from the data analyzed by the analysis unit. For example, the extraction unit extracts data related to a specific project or data related to a specific period. For example, the extraction unit extracts relevant data based on a project ID or project name. The extraction unit can also filter data by specifying a start date and end date to extract data related to a specific period. The collection unit collects data based on the information extracted by the extraction unit. For example, the collection unit collects data based on conditions specified by the user. For instance, the collection unit can collect data by specifying a project ID to collect data related to a specific project. The collection unit can also collect data by specifying a start date and end date to collect data for a specific period. The output unit outputs the data collected by the collection unit. For example, the output unit outputs the collected data in the form of reports, graphs, tables, etc. For example, the output unit generates a report based on the collected data and provides it to the user. The output unit can also generate a graph based on the collected data to visually display the data. Furthermore, the output unit can generate a table based on the collected data to display the data details. As a result, the data analysis system according to this embodiment can significantly reduce the man-hours required for data collection and output.

[0061] The analysis unit can analyze data in different formats and locations, such as Excel files, PDFs, databases, and cloud storage. For example, the analysis unit can analyze cell data in an Excel file and extract data containing specific keywords. For example, the analysis unit can specify a specific sheet in an Excel file to analyze the data. The analysis unit can also analyze text data in a PDF file and extract data containing specific phrases. For example, the analysis unit can specify a specific page in a PDF file to analyze the data. The analysis unit can also analyze table data in a database and extract records that match specific conditions. For example, the analysis unit can specify a specific table in a database to analyze the data. Data stored in cloud storage can also be analyzed by the analysis unit to extract required information. For example, the analysis unit can specify a specific folder in cloud storage to analyze the data. This allows required information to be extracted from a wide range of data sources by analyzing data in different formats and locations.

[0062] The extraction unit can extract data related to a specific project or data related to a specific period from the analyzed data. The extraction unit extracts related data based on, for example, a project ID or a project name. For example, the extraction unit can filter data by specifying a project ID. The extraction unit can also filter data by specifying a start date and an end date to extract data related to a specific period. For example, the extraction unit can filter data by specifying a start date and an end date to extract data related to a specific period. This allows the user to efficiently collect information they need by extracting data related to a specific project or period.

[0063] The collection unit can collect data based on conditions specified by a user. For example, in order to collect data related to a specific project, the collection unit collects data by specifying a project ID. For example, the collection unit can collect data by specifying a project ID. The collection unit can also collect data by specifying a start date and an end date in order to collect data related to a specific period. For example, the collection unit can collect data by specifying a start date and an end date. This allows required information to be collected efficiently by collecting data based on conditions specified by a user.

[0064] The output unit can output the collected data in the form of a report, graph, or table. For example, the output unit can generate a report based on the collected data and provide it to the user. For example, the output unit can generate a report based on the collected data and provide it to the user. The output unit can also generate a graph based on the collected data and visually display the data. For example, the output unit can generate a graph based on the collected data and visually display the data. Furthermore, the output unit can generate a table based on the collected data and display details of the data. For example, the output unit can generate a table based on the collected data and display details of the data. In this way, by outputting the collected data in various formats, the user can more easily visually understand the data.

[0065] The output unit can display the data source in the output. For example, the output unit can display the data source in the output. By showing the data source in the output, corrections and external explanations can be easily made.

[0066] The analysis unit can estimate the user's emotions and adjust the priority of analysis based on the estimated user's emotions. For example, when the user is feeling stressed, the analysis unit can prioritize analyzing data with high importance. For example, when the user is feeling stressed, the analysis unit can prioritize analyzing data with high importance. Furthermore, when the user is relaxed, the analysis unit can analyze overall data in a balanced manner. For example, when the user is relaxed, the analysis unit can analyze overall data in a balanced manner. Furthermore, when the user is in a hurry, the analysis unit can narrow the scope of analysis to quickly obtain results. For example, when the user is in a hurry, the analysis unit can narrow the scope of analysis to quickly obtain results. In this way, by adjusting the priority of analysis according to the user's emotions, analysis that meets the user's needs is possible.

[0067] During analysis, the analysis unit can evaluate the reliability of the data and prioritize analyzing highly reliable data. For example, the analysis unit can check the source of the data and prioritize analyzing highly reliable data. For example, the analysis unit can check the source of the data and prioritize analyzing highly reliable data. The analysis unit can also evaluate the update frequency of the data and prioritize analyzing the latest data. For example, the analysis unit can evaluate the update frequency of the data and prioritize analyzing the latest data. Furthermore, the analysis unit can check the consistency of the data and prioritize analyzing consistent data. For example, the analysis unit can check the consistency of the data and prioritize analyzing consistent data. This prioritizes analyzing highly reliable data, thereby improving the accuracy of the analysis results.

[0068] During analysis, the analysis unit can apply different analysis algorithms depending on the format of the data. For example, the analysis unit applies a natural language processing algorithm to text data. For example, the analysis unit can apply a natural language processing algorithm to text data. The analysis unit can also apply a statistical analysis algorithm to numerical data. For example, the analysis unit can apply a statistical analysis algorithm to numerical data. Furthermore, the analysis unit can also apply an image recognition algorithm to image data. For example, the analysis unit can apply an image recognition algorithm to image data. In this way, by applying an analysis algorithm depending on the format of the data, the accuracy of the analysis is improved.

[0069] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, when the user is nervous, the analysis unit can provide a simple, highly visible display method. For example, when the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, when the user is relaxed, the analysis unit can also provide a display method including detailed information. For example, when the user is relaxed, the analysis unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. For example, when the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. In this way, by adjusting the display method of the analysis results according to the user's emotions, the user can easily understand the results.

[0070] The analysis unit can perform analysis based on the geographical distribution of the data. For example, the analysis unit can map the geographical distribution of the data and perform analysis for each region. The analysis unit can also analyze data trends while considering geographical factors. For example, the analysis unit can analyze data trends while considering geographical factors. Furthermore, the analysis unit can identify geographical clusters and perform detailed analysis for each cluster. For example, the analysis unit can identify geographical clusters and perform detailed analysis for each cluster. This makes detailed analysis for each region possible by considering the geographical distribution of the data.

[0071] The analysis unit can improve the accuracy of the analysis by referring to relevant literature during the analysis. For example, the analysis unit can automatically search for relevant literature and incorporate it into the analysis. For example, the analysis unit can automatically search for relevant literature and incorporate it into the analysis. The analysis unit can also incorporate citation information from relevant literature into the analysis. For example, the analysis unit can incorporate citation information from relevant literature into the analysis. Furthermore, the analysis unit can add summaries of relevant literature to the analysis results. For example, the analysis unit can add summaries of relevant literature to the analysis results. This improves the accuracy of the analysis by referring to relevant literature.

[0072] The extraction unit can estimate the user's emotions and determine the priority of information to be extracted based on the estimated user's emotions. For example, when the user is feeling stressed, the extraction unit can prioritize extracting information of high importance. For example, when the user is feeling stressed, the extraction unit can prioritize extracting information of high importance. Furthermore, when the user is relaxed, the extraction unit can extract overall information in a balanced manner. For example, when the user is relaxed, the extraction unit can extract overall information in a balanced manner. Furthermore, when the user is in a hurry, the extraction unit can narrow the range of extraction to quickly obtain results. For example, when the user is in a hurry, the extraction unit can narrow the range of extraction to quickly obtain results. In this way, by determining the priority of information according to the user's emotions, it is possible to extract information that meets the user's needs.

[0073] The extraction unit can improve the accuracy of extraction based on the interrelationships of data during extraction. The extraction unit, for example, analyzes the interrelationships of data and extracts highly related information. For example, the extraction unit can analyze the interrelationships of data and extract highly related information. The extraction unit can also calculate a correlation coefficient of data and preferentially extract highly correlated data. For example, the extraction unit can calculate a correlation coefficient of data and preferentially extract highly correlated data. Furthermore, the extraction unit can perform a network analysis of data and extract important nodes. For example, the extraction unit can perform a network analysis of data and extract important nodes. In this way, the accuracy of extraction is improved by taking the interrelationships of data into consideration.

[0074] The extraction unit can perform extraction while taking into consideration attribute information of the data submitter. The extraction unit extracts important information while taking into consideration, for example, the job title and department of the data submitter. For example, the extraction unit can extract important information while taking into consideration the job title and department of the data submitter. The extraction unit can also evaluate the past performance of the data submitter and extract highly reliable information. For example, the extraction unit can evaluate the past performance of the data submitter and extract highly reliable information. Furthermore, the extraction unit can extract highly relevant information while taking into consideration the field of expertise of the data submitter. For example, the extraction unit can extract highly relevant information while taking into consideration the field of expertise of the data submitter. In this way, highly reliable information can be extracted by taking into consideration the attribute information of the data submitter.

[0075] The extraction unit can estimate the user's emotions and adjust the display method of the extraction results based on the estimated user's emotions. For example, when the user is nervous, the extraction unit can provide a simple and highly visible display method. For example, when the user is nervous, the extraction unit can provide a simple and highly visible display method. Furthermore, when the user is relaxed, the extraction unit can also provide a display method including detailed information. For example, when the user is relaxed, the extraction unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the extraction unit can also provide a display method that focuses on the main points. For example, when the user is in a hurry, the extraction unit can provide a display method that focuses on the main points. In this way, by adjusting the display method of the extraction results according to the user's emotions, the user can easily understand the results.

[0076] The extraction unit can perform extraction based on the geographical distribution of the data. For example, the extraction unit can map the geographical distribution of the data and extract information for each region. The extraction unit can also extract data trends by considering geographical factors. For example, the extraction unit can extract data trends by considering geographical factors. Furthermore, the extraction unit can identify geographical clusters and extract detailed information for each cluster. For example, the extraction unit can identify geographical clusters and extract detailed information for each cluster. This allows for the extraction of detailed information for each region by considering the geographical distribution of the data.

[0077] The extraction unit can improve the accuracy of the extraction by referring to related literature during the extraction process. For example, the extraction unit can automatically search for related literature and incorporate it into the extraction. For example, the extraction unit can automatically search for related literature and incorporate it into the extraction. The extraction unit can also incorporate citation information from related literature into the extraction. For example, the extraction unit can incorporate citation information from related literature into the extraction. Furthermore, the extraction unit can add summaries of related literature to the extraction results. For example, the extraction unit can add summaries of related literature to the extraction results. This improves the accuracy of the extraction by referring to related literature.

[0078] The data collection unit can estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if the user is stressed, the data collection unit can prioritize collecting high-priority data. Furthermore, if the user is relaxed, the data collection unit can collect a balanced mix of data. Additionally, if the user is in a hurry, the data collection unit can narrow the scope of data collection to obtain results quickly. This allows for data collection tailored to the user's needs by prioritizing data according to their emotions.

[0079] The data collection unit can evaluate the reliability of the data during collection and prioritize the collection of highly reliable data. For example, the data collection unit can verify the source of the data and prioritize the collection of highly reliable data. The data collection unit can also evaluate the frequency of data updates and prioritize the collection of the most recent data. For example, the data collection unit can evaluate the frequency of data updates and prioritize the collection of the most recent data. Furthermore, the data collection unit can check the consistency of the data and prioritize the collection of consistent data. For example, the data collection unit can check the consistency of the data and prioritize the collection of consistent data. By prioritizing the collection of highly reliable data, the accuracy of the collection results is improved.

[0080] The data collection unit can apply different collection algorithms depending on the data format during collection. For example, it can apply a natural language processing algorithm to text data. It can also apply a statistical analysis algorithm to numerical data. Furthermore, it can apply an image recognition algorithm to image data. This improves the accuracy of data collection by applying a collection algorithm appropriate to the data format.

[0081] The data collection unit can estimate the user's emotions and adjust the display method of the collected results based on the estimated emotions. For example, if the user is nervous, the data collection unit can provide a simple and easy-to-read display method. For example, if the user is nervous, the data collection unit can provide a simple and easy-to-read display method. For example, if the user is relaxed, the data collection unit can provide a display method that includes detailed information. For example, if the user is relaxed, the data collection unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the data collection unit can provide a display method that focuses on the essentials. For example, if the user is in a hurry, the data collection unit can provide a display method that focuses on the essentials. In this way, by adjusting the display method of the collected results according to the user's emotions, it becomes easier for the user to understand the results.

[0082] The collection unit can perform collection based on the geographical distribution of data at the time of collection. For example, the collection unit can map the geographical distribution of data and collect information for each region. For example, the collection unit can map the geographical distribution of data and collect information for each region. The collection unit can also collect data trends taking geographical factors into consideration. For example, the collection unit can collect data trends taking geographical factors into consideration. Furthermore, the collection unit can identify geographical clusters and collect detailed information for each cluster. For example, the collection unit can identify geographical clusters and collect detailed information for each cluster. In this way, detailed information for each region can be collected by taking the geographical distribution of data into consideration.

[0083] The collection unit can improve the accuracy of the collection by referring to literature related to the data during collection. The collection unit, for example, automatically searches for literature related to the data and reflects it in the collection. For example, the collection unit can automatically search for literature related to the data and reflect it in the collection. The collection unit can also incorporate citation information of the related literature into the collection. For example, the collection unit can incorporate citation information of the related literature into the collection. Furthermore, the collection unit can add summaries of the related literature to the collection results. For example, the collection unit can add summaries of the related literature to the collection results. This improves the accuracy of the collection by referring to the related literature.

[0084] The output unit can estimate the user's emotions and adjust the output format based on the estimated user's emotions. For example, when the user is nervous, the output unit can provide a simple, highly visible output format. For example, when the user is nervous, the output unit can provide a simple, highly visible output format. Furthermore, when the user is relaxed, the output unit can provide an output format including detailed information. For example, when the user is relaxed, the output unit can provide an output format including detailed information. Furthermore, when the user is in a hurry, the output unit can provide an output format that focuses on the main points. For example, when the user is in a hurry, the output unit can provide an output format that focuses on the main points. In this way, by adjusting the output format according to the user's emotions, the user can easily understand the results.

[0085] The output unit can adjust the level of detail of the output based on the importance of the data at the time of output. For example, the output unit can add a detailed explanation to data with high importance. For example, the output unit can add a detailed explanation to data with high importance. The output unit can also add a concise explanation to data with low importance. For example, the output unit can add a concise explanation to data with low importance. Furthermore, the output unit can change the format of the output according to the importance. For example, the output unit can change the format of the output according to the importance. In this way, by adjusting the level of detail of the output based on the importance of the data, it is possible to efficiently provide the information that the user needs.

[0086] The output unit can apply different output algorithms depending on the category of data at the time of output. For example, the output unit can apply a natural language generation algorithm to text data. For example, the output unit can apply a natural language generation algorithm to text data. The output unit can also apply a statistical analysis algorithm to numerical data. For example, the output unit can apply a statistical analysis algorithm to numerical data. The output unit can also apply an image generation algorithm to image data. For example, the output unit can apply an image generation algorithm to image data. In this way, by applying an output algorithm depending on the category of data, the accuracy of the output is improved.

[0087] The output unit can estimate the user's emotions and adjust the order of outputs based on the estimated user's emotions. For example, when the user is nervous, the output unit can display important information first. For example, when the user is nervous, the output unit can display important information first. Furthermore, when the user is relaxed, the output unit can display overall information in a balanced manner. For example, when the user is relaxed, the output unit can display overall information in a balanced manner. Furthermore, when the user is in a hurry, the output unit can display information that emphasizes the main points first. For example, when the user is in a hurry, the output unit can display information that emphasizes the main points first. In this way, adjusting the order of outputs according to the user's emotions makes it easier for the user to understand the results.

[0088] At the time of output, the output unit can determine the priority of the output based on the time of submission of the data. The output unit, for example, prioritizes output of the latest data. For example, the output unit can prioritize output of the latest data. The output unit can also postpone output of older data. For example, the output unit can postpone output of older data. Furthermore, the output unit can also adjust the order of the output based on the time of submission. For example, the output unit can adjust the order of the output based on the time of submission. In this way, by determining the priority of the output based on the time of submission of the data, the latest information can be provided preferentially.

[0089] The output unit can adjust the order of output based on the relevance of the data at the time of output. The output unit, for example, prioritizes output of highly relevant data. For example, the output unit can prioritize output of highly relevant data. Furthermore, the output unit can postpone output of less relevant data. For example, the output unit can postpone output of less relevant data. Furthermore, the output unit can adjust the order of output based on the relevance of the data. For example, the output unit can adjust the order of output based on the relevance of the data. In this way, by adjusting the order of output based on the relevance of the data, highly relevant information can be provided preferentially. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned analysis unit, extraction unit, collection unit, and output unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 or the processor 28 of the data processing device 12, and automatically analyzes data in different formats and locations. The extraction unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and extracts necessary information from the analyzed data. The collection unit is implemented, 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 collects data based on the extracted information. The output unit is implemented, for example, by the output device 40 of the smart device 14 or the specific processing unit 290 of the data processing device 12, and outputs the collected data in the form of a report, graph, table, etc. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned analysis unit, extraction unit, collection unit, and output unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart glasses 214 or the processor 28 of the data processing device 12, and automatically analyzes data in different formats and locations. The extraction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and extracts necessary information from the analyzed data. The collection 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 collects data based on the extracted information. The output unit is realized, for example, by the speaker 240 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and outputs the collected data in the form of a report, graph, table, etc. === Hard Collateral 1-3 === Each of the multiple elements described above, including the analysis unit, extraction unit, collection unit, and output unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314 or the processor 28 of the data processing unit 12, and automatically analyzes data in different formats and locations. The extraction unit is implemented by the identification processing unit 290 of the data processing unit 12, and extracts necessary information from the analyzed data. The collection unit is implemented by the control unit 46A of the headset terminal 314 or the identification processing unit 290 of the data processing unit 12, and collects data based on the extracted information. The output unit is implemented by the display 343 of the headset terminal 314 or the identification processing unit 290 of the data processing unit 12, and outputs the collected data in the form of reports, graphs, tables, etc. === Hard Collateral 1-4 === Each of the multiple elements described above, including the analysis unit, extraction unit, collection unit, and output unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414 or the processor 28 of the data processing unit 12, and automatically analyzes data in different formats and locations. The extraction unit is implemented by the specific processing unit 290 of the data processing unit 12, and extracts necessary information from the analyzed data. The collection unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12, and collects data based on the extracted information. The output unit is implemented by the speaker 240 of the robot 414 or the specific processing unit 290 of the data processing unit 12, and outputs the collected data in the form of reports, graphs, tables, etc.

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

[0091] The data analysis system may further include a duplicate detection unit that detects duplicate data and automatically removes the duplicate data. For example, the duplicate detection unit may detect and remove duplicates when the same data exists in multiple formats or locations. The duplicate detection unit may also compare the content of data and treat highly similar data as duplicates. Furthermore, the duplicate detection unit may detect only specific data as duplicates based on user-specified conditions. This eliminates data duplication, improving data accuracy and efficiency.

[0092] When analyzing data, the analysis unit can track changes in the data and detect patterns of change. For example, the analysis unit can analyze changes in the data over time and detect trends. The analysis unit can also detect abnormal changes and issue alerts. Furthermore, the analysis unit can make future predictions based on changes in the data. This allows deeper insights to be gained by tracking changes in the data.

[0093] When extracting data, the extraction unit can evaluate the quality of the data and preferentially extract high-quality data. For example, the extraction unit can evaluate the completeness and consistency of the data and extract high-quality data. The extraction unit can also evaluate the reliability of the data and extract highly reliable data. Furthermore, the extraction unit can evaluate the accuracy of the data and extract highly accurate data. In this way, by taking the quality of the data into consideration, highly reliable information can be provided.

[0094] When collecting data, the collection unit can determine the collection priority based on the importance of the data. For example, the collection unit can prioritize collecting data with high importance. The collection unit can also postpone collecting data with low importance. Furthermore, the collection unit can adjust the collection priority based on the importance specified by the user. This allows for efficient data collection by prioritizing the collection of important data.

[0095] The output unit can visualize the data at the time of output, allowing the user to intuitively understand the data. For example, the output unit can convert the data into graphs or charts and display them visually. The output unit can also generate a heat map of the data to visually show the distribution of the data. Furthermore, the output unit can provide an interactive dashboard of the data, allowing the user to analyze the data while manipulating it. Thus, visualizing the data makes it easier for the user to intuitively understand the data.

[0096] The analysis unit can estimate the user's emotions and adjust the depth of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis range can be narrowed to provide results quickly. In this way, by adjusting the depth of the analysis according to the user's emotions, analysis that meets the user's needs can be performed.

[0097] The extraction unit can estimate the user's emotions and adjust the level of detail of the extracted data based on the estimated user emotions. For example, if the user is nervous, the extraction unit can provide concise, to-the-point data. If the user is relaxed, the extraction unit can provide detailed data. Furthermore, if the user is in a hurry, the extraction range can be narrowed to quickly provide results. In this way, by adjusting the level of detail of the extracted data according to the user's emotions, the information the user needs can be efficiently provided.

[0098] The data collection unit can estimate the user's emotions and adjust the amount of data collected based on those estimates. For example, if the user is stressed, the unit can collect only the minimum necessary data. Conversely, if the user is relaxed, it can collect more detailed data. Furthermore, if the user is in a hurry, the scope of data collection can be narrowed to produce results quickly. This allows for data collection tailored to the user's needs by adjusting the amount of data collected according to their emotions.

[0099] The output unit can estimate the user's emotions and adjust the timing of the output based on those emotions. For example, if the user is nervous, the output can be provided quickly. If the user is relaxed, the output can include more detailed information. Furthermore, if the user is in a hurry, a concise output can be provided quickly. By adjusting the timing of the output according to the user's emotions, the user can more easily understand the results quickly.

[0100] The output unit can estimate the user's emotions and adjust the output format based on those emotions. For example, if the user is nervous, a simple and highly visible format can be provided. If the user is relaxed, a format containing detailed information can be provided. Furthermore, if the user is in a hurry, a format that gets straight to the point can be provided. By adjusting the output format according to the user's emotions, the results become easier for the user to understand.

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

[0102] Step 1: The analysis unit automatically analyzes data in different formats and locations. For example, it analyzes data in different formats and locations, such as Excel files, PDFs, databases, and cloud storage. The analysis unit analyzes cell data in Excel files and extracts data containing specific keywords. It can also analyze text data in PDF files and extract data containing specific phrases. It can also analyze table data in databases and extract records that match specific conditions. The analysis unit also analyzes data stored in cloud storage to extract the necessary information. Step 2: The extraction unit extracts the necessary information from the data analyzed by the analysis unit. For example, it extracts data related to a specific project or a specific period. The extraction unit extracts related data based on the project ID or project name. It can also filter data by specifying the start and end dates. Step 3: The collection unit collects data based on the information extracted by the extraction unit. For example, the collection unit collects data based on conditions specified by the user. To collect data related to a specific project, the collection unit collects data by specifying a project ID. In addition, to collect data related to a specific period, the collection unit can also collect data by specifying a start date and an end date. Step 4: The output unit outputs the data collected by the collection unit. For example, it outputs the collected data in the form of reports, graphs, tables, etc. The output unit generates a report based on the collected data and provides it to the user. It can also generate graphs based on the collected data to display the data visually. Furthermore, it can generate tables based on the collected data to display the data details.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An analysis unit that automatically analyzes data in different formats and locations; an extraction unit that extracts necessary information from the data analyzed by the analysis unit; a collection unit that collects data based on the information extracted by the extraction unit; an output unit that outputs the data collected by the collection unit; A system characterized by:

2. The analysis unit Analyze data in different formats and locations, including Excel files, PDFs, databases, and cloud storage 2. The system of claim 1.

3. The extraction unit Extract data from the parsed data that is relevant to a specific project or a specific time period 2. The system of claim 1.

4. The collecting unit Collect data based on user-specified criteria 2. The system of claim 1.

5. The output unit Output collected data in the form of reports, graphs, and tables 2. The system of claim 1.

6. The output unit Show data source in output 2. The system of claim 1.

7. The analysis unit Estimate user emotions and adjust analysis priorities based on the estimated user emotions 2. The system of claim 1.

8. The analysis unit During analysis, evaluate the reliability of the data and prioritize analysis of reliable data.

2. The system of claim 1.

Images