system

Abstract

The system according to the embodiment aims to enable efficient information comprehension by graphing actual performance figures and linking them to budget figures. [Solution] A system according to an embodiment includes a collection unit, a graphing unit, and a linking unit. The collection unit collects actual figures. The graphing unit graphs the actual figures collected by the collection unit. The linking unit links the actual figures graphed by the graphing unit to budget figures.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the task of graphing actual figures and linking them to budget figures was time-consuming, making it difficult to grasp information efficiently.

[0005] The system according to the embodiment aims to enable efficient information comprehension by graphing actual performance figures and linking them to budget figures. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a graphing unit, and a linking unit. The collection unit collects actual figures. The graphing unit graphs the actual figures collected by the collection unit. The linking unit links the actual figures graphed by the graphing unit to budget figures. [Effects of the Invention]

[0007] The system according to the embodiment can enable efficient information comprehension by graphing actual performance figures and linking them to budget figures. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A data management system according to an embodiment of the present invention collects actual results, graphs them, and links them to budgeted figures. This data management system graphs actual results obtained through a search for actual results slips and links them to budgeted figures, thereby reducing the need for each headquarters employee to create numerical report materials and enabling higher-level employees to grasp the situation at a glance. Specifically, the system comprises the following steps: First, the actual results obtained through the search for actual results slips are collected. This process is performed automatically. For example, data such as sales performance and cost performance for each department is collected. Next, the collected actual results are graphed. The graphing is performed automatically and displayed in a visually easy-to-understand format, such as a bar graph or line graph. Furthermore, the graphed actual results are linked to budgeted figures. This facilitates comparison of actual results and budgeted figures, reducing the need for each headquarters employee to create numerical report materials. For example, a graph overlaying actual results and budgeted figures may be displayed. This system allows higher-level employees to grasp the status of actual results and budgeted figures at a glance. For example, a graph showing each department's sales performance and budget achievement status may be displayed at a glance. This allows the data management system to automatically collect actual figures, graph them, and link them to budget figures, reducing the need to create numerical report documents and making them understandable at a glance even to higher-level employees.

[0029] A data management system according to an embodiment includes a collection unit, a graphing unit, and a linking unit. The collection unit collects actual results. The actual results include, but are not limited to, sales results, cost results, and profit results. The collection unit automatically collects data such as sales results and cost results for each department. For example, the collection unit collects sales results for each department daily and stores the data in a database. The collection unit can also collect cost results monthly and store the data in a database. The collection unit can also collect profit results quarterly and store the data in a database. The graphing unit graphs the actual results collected by the collection unit. The graphing can be performed in the form of, for example, a bar graph, a line graph, a pie chart, or the like, but is not limited to, these examples. For example, the graphing unit displays the collected sales results as a bar graph. The graphing unit can also display the collected cost results as a line graph. The graphing unit can also display the collected profit results as a pie chart. The linking unit links the actual figures graphed by the graphing unit to the budget figures. Linking is performed, for example, by displaying a graph in which the actual figures and the budget figures are superimposed, but is not limited to this example. For example, the linking unit displays a bar graph in which the sales results and the budget figures are superimposed. The linking unit can also display a line graph in which the cost results and the budget figures are superimposed. Furthermore, the linking unit can display a pie chart in which the profit results and the budget figures are superimposed. In this way, the data management system according to the embodiment collects actual figures, graphs them, and links them to budget figures, thereby reducing the need to create numerical report materials and enabling even higher-level employees to understand at a glance.

[0030] The collection unit can automatically collect data on sales or cost performance of each department. For example, the collection unit collects sales performance of each department daily and stores it in a database. For example, the collection unit automatically collects sales performance of each department and stores it in a database. The collection unit can also collect cost performance monthly and store it in a database. For example, the collection unit automatically collects cost performance of each department and stores it in a database. The collection unit can also collect profit performance quarterly and store it in a database. For example, the collection unit automatically collects profit performance of each department and stores it in a database. This automatically collects data such as sales performance and cost performance of each department, thereby reducing manual data collection work. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs sales performance data of each department into AI, which can automatically collect and store the data.

[0031] The graphing unit can graph the collected performance figures in a visually easy-to-understand format. The graphing unit, for example, displays the collected performance figures as a bar graph. For example, the graphing unit displays the collected sales figures as a bar graph. The graphing unit can also display the collected cost figures as a line graph. For example, the graphing unit displays the collected cost figures as a line graph. The graphing unit can also display the collected profit figures as a pie chart. For example, the graphing unit displays the collected profit figures as a pie chart. This makes it easier to understand the data by graphing the collected performance figures in a visually easy-to-understand format. Some or all of the above-mentioned processing in the graphing unit may be performed using, for example, AI, or may be performed without using AI. For example, the graphing unit can input the collected performance figures into AI, and the AI ​​can automatically graph the figures.

[0032] The graphing unit can graph the performance figures using a bar graph or a line graph. The graphing unit, for example, displays the collected performance figures as a bar graph. For example, the graphing unit displays the collected sales figures as a bar graph. The graphing unit can also display the collected cost figures as a line graph. For example, the graphing unit displays the collected cost figures as a line graph. The graphing unit can also display the collected profit figures as a pie chart. For example, the graphing unit displays the collected profit figures as a pie chart. This allows for diversifying data visualization by graphing performance figures using bar graphs, line graphs, etc. Some or all of the above-described processing in the graphing unit may be performed using, for example, AI, or may be performed without using AI. For example, the graphing unit can input the collected performance figures into AI, which then automatically generates the graph.

[0033] The linking unit can display a graph in which the graphed actual figures and budget figures are superimposed. The linking unit, for example, displays a bar graph in which the graphed actual figures and budget figures are superimposed. For example, the linking unit displays a bar graph in which the graphed sales figures and budget figures are superimposed. The linking unit can also display a line graph in which the cost figures and budget figures are superimposed. For example, the linking unit displays a line graph in which the cost figures and budget figures are superimposed. The linking unit can also display a pie chart in which the profit figures and budget figures are superimposed. For example, the linking unit displays a pie chart in which the profit figures and budget figures are superimposed. In this way, by displaying a graph in which the graphed actual figures and budget figures are superimposed, it becomes easy to compare the actual figures and the budget. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input the graphed actual figures and budget figures into AI, and the AI ​​can automatically display the superimposed graph.

[0034] The linking unit can facilitate the comparison of actual results and budgeted figures. The linking unit, for example, displays a graph in which actual results and budgeted figures are superimposed. For example, the linking unit displays a bar graph in which actual sales figures and budgeted figures are superimposed. The linking unit can also display a line graph in which actual costs and budgeted figures are superimposed. For example, the linking unit displays a line graph in which actual costs and budgeted figures are superimposed. The linking unit can also display a pie chart in which actual profits and budgeted figures are superimposed. For example, the linking unit displays a pie chart in which actual profits and budgeted figures are superimposed. This facilitates the comparison of actual results and budgeted figures, thereby reducing the need for the creation of numerical report materials. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input actual results and budgeted figures into AI, which then automatically performs the comparison.

[0035] The collection unit can evaluate the reliability of the data when collecting sales performance and cost performance of each department and collect only highly reliable data. For example, the collection unit can check the source of the data and collect only from highly reliable data sources. For example, the collection unit can check the data source of sales performance and cost performance of each department and collect only from highly reliable data sources. The collection unit can also check the consistency of the data and prioritize collecting data that does not have outliers or missing values. For example, the collection unit can check the consistency of the collected data and prioritize collecting data that does not have outliers or missing values. Furthermore, the collection unit can collect data that does not have abnormal fluctuations compared with past data. For example, the collection unit collects data that does not have abnormal fluctuations compared with past data. This allows the reliability of the data to be evaluated and only highly reliable data to be collected. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the data reliability evaluation into AI, which can collect only highly reliable data.

[0036] When collecting performance figures, the collection unit can detect abnormal values ​​by comparing them with past data and automatically correct the abnormal values. For example, the collection unit detects and corrects abnormally high or low values ​​by comparing them with past data. For example, the collection unit detects and corrects abnormally high or low values ​​by comparing them with past data. Furthermore, when an abnormal value is detected, the collection unit can also correct it based on a past average value. For example, when an abnormal value is detected, the collection unit corrects it based on a past average value. Furthermore, when an abnormal value is detected, the collection unit can obtain complementary data from other reliable data sources and correct it. For example, when an abnormal value is detected, the collection unit obtains complementary data from other reliable data sources and corrects it. This automatically corrects the abnormal value, thereby improving the accuracy of the data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the detected and corrected abnormal values ​​into AI, which can then automatically correct the abnormal value.

[0037] When collecting performance figures, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of each department. For example, the collection unit prioritizes collecting nearby data based on the location of each department. For example, the collection unit prioritizes collecting nearby data based on the location of each department. The collection unit can also prioritize collecting highly geographically relevant data and perform analysis by region. For example, the collection unit prioritizes collecting highly geographically relevant data and perform analysis by region. Furthermore, the collection unit can also determine the priority of data based on geographical distance. For example, the collection unit determines the priority of data based on geographical distance. In this way, highly relevant data can be collected preferentially by taking geographical location information into consideration. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographical location information into AI, which can then preferentially collect highly relevant data.

[0038] When collecting performance figures, the collection unit can analyze the social media activities of each department and collect related data. The collection unit, for example, monitors the social media activities of each department and collects related performance figures. For example, the collection unit monitors the social media activities of each department and collects related performance figures. The collection unit can also analyze reactions and comments on social media and reflect them in the performance figures. For example, the collection unit analyzes reactions and comments on social media and reflects them in the performance figures. Furthermore, the collection unit can prioritize collecting related data based on social media trends. For example, the collection unit prioritizes collecting related data based on social media trends. In this way, related data can be collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media activity data into AI, which collects related data.

[0039] The graphing unit can adjust the level of detail of the graph based on the importance of the performance figures when graphing. For example, the graphing unit displays performance figures with high importance in a detailed graph and figures with low importance in a simplified graph. For example, the graphing unit displays performance figures with high importance in a detailed graph and figures with low importance in a simplified graph. The graphing unit can also adjust the color and size of the graph depending on the importance of the performance figures. For example, the graphing unit adjusts the color and size to highlight performance figures with high importance. Furthermore, the graphing unit can display performance figures with high importance in multiple graph formats to enable detailed analysis. For example, the graphing unit displays performance figures with high importance in both a bar graph and a line graph. In this way, important data can be displayed in detail by adjusting the level of detail of the graph based on the importance of the performance figures. Some or all of the above-mentioned processing in the graphing unit may be performed using, for example, AI, or may be performed without AI. For example, the graphing unit can input the importance of the performance figures into AI, which can adjust the level of detail of the graph.

[0040] The graphing unit can apply different graph algorithms depending on the category of the actual figures when graphing. For example, the graphing unit displays actual sales as a bar graph and actual costs as a line graph. For example, the graphing unit displays actual sales as a bar graph and actual costs as a line graph. The graphing unit can also select and display the optimal graph algorithm depending on the category of the actual figures. For example, the graphing unit selects and displays a bar graph for actual sales and a line graph for actual costs. Furthermore, when displaying actual figures from multiple categories together in a single graph, the graphing unit can combine different graph algorithms to display them. For example, the graphing unit displays actual sales and actual costs together in a single graph, combining a bar graph and a line graph. In this way, by applying different graph algorithms depending on the category of the actual figures, data visualization is optimized. Some or all of the above-mentioned processing in the graphing unit may be performed using, for example, AI, or may be performed without using AI. For example, the graphing unit can input categories of performance figures into the AI, which can then apply the optimal graph algorithm.

[0041] When graphing, the graphing unit can determine the priority of graphs based on the submission date of the performance figures. The graphing unit, for example, prioritizes graphing and displaying the most recent performance figures. For example, the graphing unit prioritizes graphing and displaying the most recent performance figures. The graphing unit can also simplify and display performance figures that were submitted earlier. For example, the graphing unit can simplify and display performance figures that were submitted earlier. Furthermore, the graphing unit can also determine the display order of graphs based on the submission date. For example, the graphing unit determines the display order of graphs based on the submission date. In this way, by determining the priority of graphs based on the submission date of the performance figures, the most recent data can be displayed preferentially. Some or all of the above-mentioned processing in the graphing unit may be performed using, for example, AI, or may be performed without using AI. For example, the graphing unit can input the submission date of the performance figures into AI, and the AI ​​can determine the priority of graphs.

[0042] The graphing unit can adjust the order of the graphs based on the relevance of the performance figures when graphing. For example, the graphing unit prioritizes displaying highly relevant performance figures and postpones displaying less relevant figures. For example, the graphing unit prioritizes displaying highly relevant performance figures and postpones displaying less relevant figures. The graphing unit can also determine the display order of the graphs based on the relevance of the performance figures. For example, the graphing unit determines the display order of the graphs based on the relevance of the performance figures. Furthermore, the graphing unit can also display highly relevant performance figures together in a single graph. For example, the graphing unit displays highly relevant performance figures together in a single graph. In this way, by adjusting the order of the graphs based on the relevance of the performance figures, highly relevant data can be displayed preferentially. Some or all of the above-described processing in the graphing unit may be performed using, or without, AI. For example, the graphing unit can input the relevance of the performance figures into AI, which can adjust the order of the graphs.

[0043] The linking unit can improve the accuracy of linking by taking into account the interrelationship between actual results figures and budget figures during linking. The linking unit, for example, analyzes the interrelationship between actual results figures and budget figures and selects the optimal linking method. For example, the linking unit analyzes the interrelationship between actual results figures and budget figures and selects the optimal linking method. The linking unit can also prioritize linking data with strong interrelationships. For example, the linking unit prioritizes linking data with strong interrelationships. Furthermore, the linking unit can improve the accuracy of linking data with weak interrelationships by using complementary data. For example, the linking unit improves the accuracy of linking data with weak interrelationships by using complementary data for data with weak interrelationships. In this way, the accuracy of linking is improved by taking into account the interrelationship between actual results figures and budget figures. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input the interrelationship between actual results figures and budget figures into AI, which can improve the accuracy of linking.

[0044] When linking, the linking unit can link the actual figures and the budget figures while taking into consideration the attribute information of the person who submitted the figures. The linking unit can link the actual figures and the budget figures, for example, by taking into consideration the position and department of the person who submitted the figures. For example, the linking unit can link the actual figures and the budget figures by taking into consideration the position and department of the person who submitted the figures. The linking unit can also select the optimal linking method based on the submitter's past performance. For example, the linking unit selects the optimal linking method based on the submitter's past performance. Furthermore, the linking unit can prioritize linking highly relevant data based on the submitter's attribute information. For example, the linking unit prioritizes linking highly relevant data based on the submitter's attribute information. In this way, highly relevant data can be prioritized by taking the submitter's attribute information into consideration. Some or all of the above-described processing in the linking unit can be performed using, for example, AI, or can be performed without using AI. For example, the linking unit can input the submitter's attribute information into AI, which can select the optimal linking method.

[0045] The linking unit can link data taking into account the geographical distribution of actual results figures and budget figures. For example, the linking unit prioritizes linking geographically close data. For example, the linking unit prioritizes linking geographically close data. The linking unit can also link highly related data taking into account geographical distribution. For example, the linking unit links highly related data taking into account geographical distribution. Furthermore, the linking unit can select an optimal linking method based on geographical distance. For example, the linking unit selects an optimal linking method based on geographical distance. In this way, highly related data can be linked preferentially by taking geographical distribution into account. Some or all of the above-described processing in the linking unit may be performed using, or without, AI. For example, the linking unit can input the geographical distribution into AI, which can select an optimal linking method.

[0046] The linking unit can improve the accuracy of linking by referring to related literature between actual results figures and budget figures during linking. For example, the linking unit analyzes the interrelationship between actual results figures and budget figures based on related literature to improve the accuracy of linking. For example, the linking unit analyzes the interrelationship between actual results figures and budget figures based on related literature to improve the accuracy of linking. The linking unit can also select an optimal linking method by referring to related literature. For example, the linking unit selects an optimal linking method by referring to related literature. Furthermore, the linking unit can also improve the accuracy of linking by using complementary data based on related literature. For example, the linking unit improves the accuracy of linking by using complementary data based on related literature. In this way, the accuracy of linking is improved by referring to related literature. Some or all of the above-mentioned processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input related literature into AI, which improves the accuracy of linking.

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

[0048] The data management system can further include an alert unit. The alert unit can issue an alert if the actual figures collected by the collection unit deviate significantly from the budgeted figures. For example, if actual sales are significantly lower than the budgeted figures, the alert unit sends a notification to the manager. The alert unit can also immediately issue a warning if actual costs are significantly higher than the budget. Furthermore, the alert unit can also issue an alert if actual profits do not reach the budgeted target. This allows the manager to take corrective measures quickly, improving the accuracy of budget management.

[0049] The data management system can further include a forecasting unit. The forecasting unit can predict future results based on the actual results collected by the collection unit. For example, the forecasting unit can analyze trends in sales results and predict future sales. It can also predict future costs based on data on actual costs. It can also predict future profits based on data on actual profits. This allows managers to obtain information that is useful when making future budget plans.

[0050] The data management system can further include a feedback unit. The feedback unit can collect feedback from users and use it to improve the system. For example, if a user gives an opinion on how to display a graph, the feedback unit collects the opinion and reflects it in the graphing unit. Also, if a user suggests improvements to the data collection method, the feedback unit can collect the suggestion and reflect it in the collection unit. Furthermore, if a user gives an opinion on the usability of the entire system, the feedback unit can collect the opinion and use it to improve the entire system. This makes it possible to improve the system by reflecting user opinions.

[0051] The data management system may further include a training department. The training department may provide training to help users learn how to use the system. For example, the training department may provide video tutorials that explain the basic operations of the system. It may also provide detailed guides on how to create graphs and collect data. It may also provide online courses that help users learn advanced features of the system. This allows users to acquire the skills to use the system effectively.

[0052] The data management system may further include a customization section. The customization section may provide settings that allow the user to customize the display and functionality of the system. For example, the customization section may allow the user to select a preferred graph type. The customization section may also allow the user to set the display order and filtering conditions for the data. The customization section may also provide customization options that allow the user to highlight specific data items. This allows the user to customize the system to suit their needs.

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

[0054] Step 1: The collection department collects actual figures. These figures include sales figures, cost figures, and profit figures. The collection department automatically collects data such as sales figures and cost figures from each department and stores them in a database. For example, the collection department collects sales figures daily, cost figures monthly, and profit figures quarterly. Step 2: The graphing unit graphs the performance figures collected by the collection unit. Graphing is done in the form of bar graphs, line graphs, pie charts, etc. For example, sales performance is displayed as a bar graph, cost performance as a line graph, and profit performance as a pie chart. Step 3: The linking unit links the actual figures graphed by the graphing unit to the budget figures. Linking is performed by displaying a graph in which the actual figures and budget figures are superimposed. For example, it displays a bar graph in which sales results and budget figures are superimposed, a line graph in which cost results and budget figures are superimposed, and a pie chart in which profit results and budget figures are superimposed.

[0055] (Example 2) A data management system according to an embodiment of the present invention collects actual results, graphs them, and links them to budgeted figures. This data management system graphs actual results obtained through a search for actual results slips and links them to budgeted figures, thereby reducing the need for each headquarters employee to create numerical report materials and enabling higher-level employees to grasp the situation at a glance. Specifically, the system comprises the following steps: First, the actual results obtained through the search for actual results slips are collected. This process is performed automatically. For example, data such as sales performance and cost performance for each department is collected. Next, the collected actual results are graphed. The graphing is performed automatically and displayed in a visually easy-to-understand format, such as a bar graph or line graph. Furthermore, the graphed actual results are linked to budgeted figures. This facilitates comparison of actual results and budgeted figures, reducing the need for each headquarters employee to create numerical report materials. For example, a graph overlaying actual results and budgeted figures may be displayed. This system allows higher-level employees to grasp the status of actual results and budgeted figures at a glance. For example, a graph showing each department's sales performance and budget achievement status may be displayed at a glance. This allows the data management system to automatically collect actual figures, graph them, and link them to budget figures, reducing the need to create numerical report documents and making them understandable at a glance even to higher-level employees.

[0056] A data management system according to an embodiment includes a collection unit, a graphing unit, and a linking unit. The collection unit collects actual results. The actual results include, but are not limited to, sales results, cost results, and profit results. The collection unit automatically collects data such as sales results and cost results for each department. For example, the collection unit collects sales results for each department daily and stores the data in a database. The collection unit can also collect cost results monthly and store the data in a database. The collection unit can also collect profit results quarterly and store the data in a database. The graphing unit graphs the actual results collected by the collection unit. The graphing can be performed in the form of, for example, a bar graph, a line graph, a pie chart, or the like, but is not limited to, these examples. For example, the graphing unit displays the collected sales results as a bar graph. The graphing unit can also display the collected cost results as a line graph. The graphing unit can also display the collected profit results as a pie chart. The linking unit links the actual figures graphed by the graphing unit to the budget figures. Linking is performed, for example, by displaying a graph in which the actual figures and the budget figures are superimposed, but is not limited to this example. For example, the linking unit displays a bar graph in which the sales results and the budget figures are superimposed. The linking unit can also display a line graph in which the cost results and the budget figures are superimposed. Furthermore, the linking unit can display a pie chart in which the profit results and the budget figures are superimposed. In this way, the data management system according to the embodiment collects actual figures, graphs them, and links them to budget figures, thereby reducing the need to create numerical report materials and enabling even higher-level employees to understand at a glance.

[0057] The collection unit can automatically collect data on sales or cost performance of each department. For example, the collection unit collects sales performance of each department daily and stores it in a database. For example, the collection unit automatically collects sales performance of each department and stores it in a database. The collection unit can also collect cost performance monthly and store it in a database. For example, the collection unit automatically collects cost performance of each department and stores it in a database. The collection unit can also collect profit performance quarterly and store it in a database. For example, the collection unit automatically collects profit performance of each department and stores it in a database. This automatically collects data such as sales performance and cost performance of each department, thereby reducing manual data collection work. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs sales performance data of each department into AI, which can automatically collect and store the data.

[0058] The graphing unit can graph the collected performance figures in a visually easy-to-understand format. The graphing unit, for example, displays the collected performance figures as a bar graph. For example, the graphing unit displays the collected sales figures as a bar graph. The graphing unit can also display the collected cost figures as a line graph. For example, the graphing unit displays the collected cost figures as a line graph. The graphing unit can also display the collected profit figures as a pie chart. For example, the graphing unit displays the collected profit figures as a pie chart. This makes it easier to understand the data by graphing the collected performance figures in a visually easy-to-understand format. Some or all of the above-mentioned processing in the graphing unit may be performed using, for example, AI, or may be performed without using AI. For example, the graphing unit can input the collected performance figures into AI, and the AI ​​can automatically graph the figures.

[0059] The graphing unit can graph the performance figures using a bar graph or a line graph. The graphing unit, for example, displays the collected performance figures as a bar graph. For example, the graphing unit displays the collected sales figures as a bar graph. The graphing unit can also display the collected cost figures as a line graph. For example, the graphing unit displays the collected cost figures as a line graph. The graphing unit can also display the collected profit figures as a pie chart. For example, the graphing unit displays the collected profit figures as a pie chart. This allows for diversifying data visualization by graphing performance figures using bar graphs, line graphs, etc. Some or all of the above-described processing in the graphing unit may be performed using, for example, AI, or may be performed without using AI. For example, the graphing unit can input the collected performance figures into AI, which then automatically generates the graph.

[0060] The linking unit can display a graph in which the graphed actual figures and budget figures are superimposed. The linking unit, for example, displays a bar graph in which the graphed actual figures and budget figures are superimposed. For example, the linking unit displays a bar graph in which the graphed sales figures and budget figures are superimposed. The linking unit can also display a line graph in which the cost figures and budget figures are superimposed. For example, the linking unit displays a line graph in which the cost figures and budget figures are superimposed. The linking unit can also display a pie chart in which the profit figures and budget figures are superimposed. For example, the linking unit displays a pie chart in which the profit figures and budget figures are superimposed. In this way, by displaying a graph in which the graphed actual figures and budget figures are superimposed, it becomes easy to compare the actual figures and the budget. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input the graphed actual figures and budget figures into AI, and the AI ​​can automatically display the superimposed graph.

[0061] The linking unit can facilitate the comparison of actual results and budgeted figures. The linking unit, for example, displays a graph in which actual results and budgeted figures are superimposed. For example, the linking unit displays a bar graph in which actual sales figures and budgeted figures are superimposed. The linking unit can also display a line graph in which actual costs and budgeted figures are superimposed. For example, the linking unit displays a line graph in which actual costs and budgeted figures are superimposed. The linking unit can also display a pie chart in which actual profits and budgeted figures are superimposed. For example, the linking unit displays a pie chart in which actual profits and budgeted figures are superimposed. This facilitates the comparison of actual results and budgeted figures, thereby reducing the need for the creation of numerical report materials. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input actual results and budgeted figures into AI, which then automatically performs the comparison.

[0062] The collection unit can estimate the user's emotions and adjust the timing of collecting performance figures based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit delays the collection timing and collects data when the user is relaxed. For example, the collection unit estimates the user's emotions and delays the collection timing when the user is feeling stressed. The collection unit can also collect performance figures immediately and update data quickly when the user is relaxed. For example, the collection unit estimates the user's emotions and collects data immediately when the user is relaxed. Furthermore, the collection unit can advance the collection timing to quickly collect performance figures when the user is in a hurry. For example, the collection unit estimates the user's emotions and advances the collection timing when the user is in a hurry. This adjusts the collection timing based on the user's emotions, thereby reducing the burden on the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's emotion data into the generation AI, and the generation AI may adjust the collection timing.

[0063] The collection unit can evaluate the reliability of the data when collecting sales performance and cost performance of each department and collect only highly reliable data. For example, the collection unit can check the source of the data and collect only from highly reliable data sources. For example, the collection unit can check the data source of sales performance and cost performance of each department and collect only from highly reliable data sources. The collection unit can also check the consistency of the data and prioritize collecting data that does not have outliers or missing values. For example, the collection unit can check the consistency of the collected data and prioritize collecting data that does not have outliers or missing values. Furthermore, the collection unit can collect data that does not have abnormal fluctuations compared with past data. For example, the collection unit collects data that does not have abnormal fluctuations compared with past data. This allows the reliability of the data to be evaluated and only highly reliable data to be collected. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the data reliability evaluation into AI, which can collect only highly reliable data.

[0064] When collecting performance figures, the collection unit can detect abnormal values ​​by comparing them with past data and automatically correct the abnormal values. For example, the collection unit detects and corrects abnormally high or low values ​​by comparing them with past data. For example, the collection unit detects and corrects abnormally high or low values ​​by comparing them with past data. Furthermore, when an abnormal value is detected, the collection unit can also correct it based on a past average value. For example, when an abnormal value is detected, the collection unit corrects it based on a past average value. Furthermore, when an abnormal value is detected, the collection unit can obtain complementary data from other reliable data sources and correct it. For example, when an abnormal value is detected, the collection unit obtains complementary data from other reliable data sources and corrects it. This automatically corrects the abnormal value, thereby improving the accuracy of the data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the detected and corrected abnormal values ​​into AI, which can then automatically correct the abnormal value.

[0065] The collection unit can estimate the user's emotions and determine the priority of the performance figures to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit postpones collecting less important data and prioritizes collecting more important data. For example, the collection unit estimates the user's emotions and postpones collecting less important data when the user is feeling stressed. The collection unit can also collect all data equally when the user is relaxed. For example, the collection unit estimates the user's emotions and collects all data equally when the user is relaxed. Furthermore, the collection unit can prioritize collecting the most important data when the user is in a hurry. For example, the collection unit estimates the user's emotions and prioritizes collecting the most important data when the user is in a hurry. In this way, by determining the priority of the performance figures to be collected based on the user's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data into the generation AI and determine the priority of the performance figures to be collected by the generation AI.

[0066] When collecting performance figures, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of each department. For example, the collection unit prioritizes collecting nearby data based on the location of each department. For example, the collection unit prioritizes collecting nearby data based on the location of each department. The collection unit can also prioritize collecting highly geographically relevant data and perform analysis by region. For example, the collection unit prioritizes collecting highly geographically relevant data and perform analysis by region. Furthermore, the collection unit can also determine the priority of data based on geographical distance. For example, the collection unit determines the priority of data based on geographical distance. In this way, highly relevant data can be collected preferentially by taking geographical location information into consideration. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographical location information into AI, which can then preferentially collect highly relevant data.

[0067] When collecting performance figures, the collection unit can analyze the social media activities of each department and collect related data. The collection unit, for example, monitors the social media activities of each department and collects related performance figures. For example, the collection unit monitors the social media activities of each department and collects related performance figures. The collection unit can also analyze reactions and comments on social media and reflect them in the performance figures. For example, the collection unit analyzes reactions and comments on social media and reflects them in the performance figures. Furthermore, the collection unit can prioritize collecting related data based on social media trends. For example, the collection unit prioritizes collecting related data based on social media trends. In this way, related data can be collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media activity data into AI, which collects related data.

[0068] The graphing unit can estimate the user's emotions and adjust the graph representation method based on the estimated user emotions. For example, if the user is feeling stressed, the graphing unit displays a simple, highly visible graph. For example, if the user is feeling stressed, the graphing unit can estimate the user's emotions and display a simple, highly visible graph. The graphing unit can also display a graph containing detailed information if the user is relaxed. For example, the graphing unit can estimate the user's emotions and display a graph containing detailed information if the user is relaxed. Furthermore, the graphing unit can also display a concise graph that highlights the main points if the user is in a hurry. For example, the graphing unit can estimate the user's emotions and display a concise graph that highlights the main points if the user is in a hurry. This allows the graph representation method to be adjusted based on the user's emotions, making it easier for the user to read. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the graphing unit may be performed using, for example, AI, or may be performed without using AI. For example, the graphing unit may input user emotion data into a generation AI, which may then adjust the graph representation method.

[0069] The graphing unit can adjust the level of detail of the graph based on the importance of the performance figures when graphing. For example, the graphing unit displays performance figures with high importance in a detailed graph and figures with low importance in a simplified graph. For example, the graphing unit displays performance figures with high importance in a detailed graph and figures with low importance in a simplified graph. The graphing unit can also adjust the color and size of the graph depending on the importance of the performance figures. For example, the graphing unit adjusts the color and size to highlight performance figures with high importance. Furthermore, the graphing unit can display performance figures with high importance in multiple graph formats to enable detailed analysis. For example, the graphing unit displays performance figures with high importance in both a bar graph and a line graph. In this way, important data can be displayed in detail by adjusting the level of detail of the graph based on the importance of the performance figures. Some or all of the above-mentioned processing in the graphing unit may be performed using, for example, AI, or may be performed without AI. For example, the graphing unit can input the importance of the performance figures into AI, which can adjust the level of detail of the graph.

[0070] The graphing unit can apply different graph algorithms depending on the category of the actual figures when graphing. For example, the graphing unit displays actual sales as a bar graph and actual costs as a line graph. For example, the graphing unit displays actual sales as a bar graph and actual costs as a line graph. The graphing unit can also select and display the optimal graph algorithm depending on the category of the actual figures. For example, the graphing unit selects and displays a bar graph for actual sales and a line graph for actual costs. Furthermore, when displaying actual figures from multiple categories together in a single graph, the graphing unit can combine different graph algorithms to display them. For example, the graphing unit displays actual sales and actual costs together in a single graph, combining a bar graph and a line graph. In this way, by applying different graph algorithms depending on the category of the actual figures, data visualization is optimized. Some or all of the above-mentioned processing in the graphing unit may be performed using, for example, AI, or may be performed without using AI. For example, the graphing unit can input categories of performance figures into the AI, which can then apply the optimal graph algorithm.

[0071] The graphing unit can estimate the user's emotions and adjust the length of the graph based on the estimated user's emotions. For example, if the user is feeling stressed, the graphing unit displays a short, concise graph. For example, if the user is feeling stressed, the graphing unit can estimate the user's emotions and display a short, concise graph. The graphing unit can also display a longer graph containing more detailed information if the user is relaxed. For example, the graphing unit can estimate the user's emotions and display a longer graph containing more detailed information if the user is relaxed. Furthermore, the graphing unit can adjust the length of the graph so that the user can understand it quickly if the user is in a hurry. For example, the graphing unit can estimate the user's emotions and adjust the length of the graph so that the user can understand it quickly if the user is in a hurry. In this way, by adjusting the length of the graph based on the user's emotions, it is possible to provide a graph that is easy for the user to read. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the graphing unit may be performed using, for example, AI, or may be performed without using AI. For example, the graphing unit may input user emotion data to a generation AI, which may then adjust the length of the graph.

[0072] When graphing, the graphing unit can determine the priority of graphs based on the submission date of the performance figures. The graphing unit, for example, prioritizes graphing and displaying the most recent performance figures. For example, the graphing unit prioritizes graphing and displaying the most recent performance figures. The graphing unit can also simplify and display performance figures that were submitted earlier. For example, the graphing unit can simplify and display performance figures that were submitted earlier. Furthermore, the graphing unit can also determine the display order of graphs based on the submission date. For example, the graphing unit determines the display order of graphs based on the submission date. In this way, by determining the priority of graphs based on the submission date of the performance figures, the most recent data can be displayed preferentially. Some or all of the above-mentioned processing in the graphing unit may be performed using, for example, AI, or may be performed without using AI. For example, the graphing unit can input the submission date of the performance figures into AI, and the AI ​​can determine the priority of graphs.

[0073] The graphing unit can adjust the order of the graphs based on the relevance of the performance figures when graphing. For example, the graphing unit prioritizes displaying highly relevant performance figures and postpones displaying less relevant figures. For example, the graphing unit prioritizes displaying highly relevant performance figures and postpones displaying less relevant figures. The graphing unit can also determine the display order of the graphs based on the relevance of the performance figures. For example, the graphing unit determines the display order of the graphs based on the relevance of the performance figures. Furthermore, the graphing unit can also display highly relevant performance figures together in a single graph. For example, the graphing unit displays highly relevant performance figures together in a single graph. In this way, by adjusting the order of the graphs based on the relevance of the performance figures, highly relevant data can be displayed preferentially. Some or all of the above-described processing in the graphing unit may be performed using, or without, AI. For example, the graphing unit can input the relevance of the performance figures into AI, which can adjust the order of the graphs.

[0074] The linking unit can estimate the user's emotion and adjust the linking method based on the estimated user's emotion. For example, the linking unit provides a simple linking method when the user is stressed. For example, the linking unit estimates the user's emotion and provides a simple linking method when the user is stressed. The linking unit can also provide a detailed linking method when the user is relaxed. For example, the linking unit estimates the user's emotion and provides a detailed linking method when the user is relaxed. The linking unit can also provide a quick linking method when the user is in a hurry. For example, the linking unit estimates the user's emotion and provides a quick linking method when the user is in a hurry. This allows the linking method to be adjusted based on the user's emotion, thereby providing an optimal linking method for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit may input user emotion data to the generation AI, and the generation AI may adjust the linking method.

[0075] The linking unit can improve the accuracy of linking by taking into account the interrelationship between actual results figures and budget figures during linking. The linking unit, for example, analyzes the interrelationship between actual results figures and budget figures and selects the optimal linking method. For example, the linking unit analyzes the interrelationship between actual results figures and budget figures and selects the optimal linking method. The linking unit can also prioritize linking data with strong interrelationships. For example, the linking unit prioritizes linking data with strong interrelationships. Furthermore, the linking unit can improve the accuracy of linking data with weak interrelationships by using complementary data. For example, the linking unit improves the accuracy of linking data with weak interrelationships by using complementary data for data with weak interrelationships. In this way, the accuracy of linking is improved by taking into account the interrelationship between actual results figures and budget figures. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input the interrelationship between actual results figures and budget figures into AI, which can improve the accuracy of linking.

[0076] When linking, the linking unit can link the actual figures and the budget figures while taking into consideration the attribute information of the person who submitted the figures. The linking unit can link the actual figures and the budget figures, for example, by taking into consideration the position and department of the person who submitted the figures. For example, the linking unit can link the actual figures and the budget figures by taking into consideration the position and department of the person who submitted the figures. The linking unit can also select the optimal linking method based on the submitter's past performance. For example, the linking unit selects the optimal linking method based on the submitter's past performance. Furthermore, the linking unit can prioritize linking highly relevant data based on the submitter's attribute information. For example, the linking unit prioritizes linking highly relevant data based on the submitter's attribute information. In this way, highly relevant data can be prioritized by taking the submitter's attribute information into consideration. Some or all of the above-described processing in the linking unit can be performed using, for example, AI, or can be performed without using AI. For example, the linking unit can input the submitter's attribute information into AI, which can select the optimal linking method.

[0077] The linking unit can estimate the user's emotions and adjust the order in which the linking results are displayed based on the estimated user emotions. For example, if the user is feeling stressed, the linking unit displays important results first. For example, the linking unit estimates the user's emotions and displays important results first when the user is feeling stressed. The linking unit can also display detailed results in an orderly manner when the user is relaxed. For example, the linking unit estimates the user's emotions and displays detailed results in an orderly manner when the user is relaxed. Furthermore, the linking unit can also display results that emphasize the main points first when the user is in a hurry. For example, the linking unit estimates the user's emotions and displays results that emphasize the main points first when the user is in a hurry. In this way, by adjusting the display order based on the user's emotions, the results can be displayed in an order that is easy for the user to view. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit may input user emotion data to the generation AI, and the generation AI may adjust the display order.

[0078] The linking unit can link data taking into account the geographical distribution of actual results figures and budget figures. For example, the linking unit prioritizes linking geographically close data. For example, the linking unit prioritizes linking geographically close data. The linking unit can also link highly related data taking into account geographical distribution. For example, the linking unit links highly related data taking into account geographical distribution. Furthermore, the linking unit can select an optimal linking method based on geographical distance. For example, the linking unit selects an optimal linking method based on geographical distance. In this way, highly related data can be linked preferentially by taking geographical distribution into account. Some or all of the above-described processing in the linking unit may be performed using, or without, AI. For example, the linking unit can input the geographical distribution into AI, which can select an optimal linking method.

[0079] The linking unit can improve the accuracy of linking by referring to related literature between actual results figures and budget figures during linking. For example, the linking unit analyzes the interrelationship between actual results figures and budget figures based on related literature to improve the accuracy of linking. For example, the linking unit analyzes the interrelationship between actual results figures and budget figures based on related literature to improve the accuracy of linking. The linking unit can also select an optimal linking method by referring to related literature. For example, the linking unit selects an optimal linking method by referring to related literature. Furthermore, the linking unit can also improve the accuracy of linking by using complementary data based on related literature. For example, the linking unit improves the accuracy of linking by using complementary data based on related literature. In this way, the accuracy of linking is improved by referring to related literature. Some or all of the above-mentioned processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input related literature into AI, which improves the accuracy of linking. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, graphing unit, and linking unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 and automatically collects data such as sales results and cost results of each department. The graphing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and displays the collected performance figures in the form of a bar graph, line graph, or the like. The linking unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and displays a graph in which performance figures and budget figures are superimposed. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, graphing unit, and linking unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 and automatically collects data such as sales results and cost results of each department. The graphing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and displays the collected performance figures in the form of a bar graph, line graph, or the like. The linking unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and displays a graph in which performance figures and budget figures are superimposed. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, graphing unit, and linking unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314 and automatically collects data such as actual sales and actual costs of each department. The graphing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and displays the collected actual figures in the form of a bar graph, line graph, or the like. The linking unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and displays a graph in which actual figures and budget figures are superimposed. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, graphing unit, and linking unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 and automatically collects data such as sales results and cost results of each department. The graphing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and displays the collected performance figures in the form of a bar graph, line graph, or the like. The linking unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and displays a graph in which performance figures and budget figures are superimposed.

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

[0081] The data management system can further include an alert unit. The alert unit can issue an alert if the actual figures collected by the collection unit deviate significantly from the budgeted figures. For example, if actual sales are significantly lower than the budgeted figures, the alert unit sends a notification to the manager. The alert unit can also immediately issue a warning if actual costs are significantly higher than the budget. Furthermore, the alert unit can also issue an alert if actual profits do not reach the budgeted target. This allows the manager to take corrective measures quickly, improving the accuracy of budget management.

[0082] The data management system can further include a forecasting unit. The forecasting unit can predict future results based on the actual results collected by the collection unit. For example, the forecasting unit can analyze trends in sales results and predict future sales. It can also predict future costs based on data on actual costs. It can also predict future profits based on data on actual profits. This allows managers to obtain information that is useful when making future budget plans.

[0083] The data management system can further include a feedback unit. The feedback unit can collect feedback from users and use it to improve the system. For example, if a user gives an opinion on how to display a graph, the feedback unit collects the opinion and reflects it in the graphing unit. Also, if a user suggests improvements to the data collection method, the feedback unit can collect the suggestion and reflect it in the collection unit. Furthermore, if a user gives an opinion on the usability of the entire system, the feedback unit can collect the opinion and use it to improve the entire system. This makes it possible to improve the system by reflecting user opinions.

[0084] The data management system may further include a training department. The training department may provide training to help users learn how to use the system. For example, the training department may provide video tutorials that explain the basic operations of the system. It may also provide detailed guides on how to create graphs and collect data. It may also provide online courses that help users learn advanced features of the system. This allows users to acquire the skills to use the system effectively.

[0085] The data management system may further include a customization section. The customization section may provide settings that allow the user to customize the display and functionality of the system. For example, the customization section may allow the user to select a preferred graph type. The customization section may also allow the user to set the display order and filtering conditions for the data. The customization section may also provide customization options that allow the user to highlight specific data items. This allows the user to customize the system to suit their needs.

[0086] The data management system may further include an emotion estimation unit. The emotion estimation unit may estimate the user's emotion and adjust the system's operation based on the estimated emotion. For example, if the user is feeling stressed, the system may provide a simple interface to reduce the user's burden. If the user is relaxed, the system may display detailed information to allow the user to perform in-depth analysis. Furthermore, if the user is in a hurry, the system may display important information with priority to support quick decision-making. This enables flexible system operation according to the user's emotion.

[0087] The data management system may further include an emotion feedback unit. The emotion feedback unit may monitor the user's emotions in real time and reflect them in the system's operation. For example, if the user is feeling stressed, the system may simplify operations to reduce the user's burden. If the user is relaxed, the system may provide detailed information to allow the user to perform in-depth analysis. Furthermore, if the user is in a hurry, the system may prioritize the display of important information to support quick decision-making. This allows the system to operate flexibly in accordance with the user's emotions.

[0088] The data management system may further include a sentiment analysis unit. The sentiment analysis unit may analyze the user's sentiment data and use the results to improve the system. For example, if the user feels stressed about a particular operation, the sentiment analysis unit may suggest improvements to simplify that operation. Also, if the user shows interest in particular information, the sentiment analysis unit may suggest improvements to provide that information in more detail. Furthermore, based on the user's sentiment data, the sentiment analysis unit may suggest improvements to improve the usability of the entire system. This makes it possible to improve the system based on the user's sentiment.

[0089] The data management system can further include an emotion prediction unit. The emotion prediction unit can predict future emotions based on the user's past emotion data and adjust the system's operation. For example, if the user has felt stressed about a specific operation in the past, the system can issue a warning before performing that operation. Also, if the user has shown interest in specific information in the past, that information can be displayed preferentially. Furthermore, the operation of the entire system can be optimized based on the user's past emotion data. This enables flexible system operation based on the user's emotions.

[0090] The data management system may further include an emotion adaptation unit. The emotion adaptation unit may dynamically change the system interface and functions according to the user's emotions. For example, if the user is feeling stressed, the system may provide a simple interface to reduce the user's burden. If the user is relaxed, the system may display detailed information to enable the user to perform in-depth analysis. Furthermore, if the user is in a hurry, the system may display important information with priority to support quick decision-making. This enables flexible system operation according to the user's emotions.

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

[0092] Step 1: The collection department collects actual figures. These figures include sales figures, cost figures, and profit figures. The collection department automatically collects data such as sales figures and cost figures from each department and stores them in a database. For example, the collection department collects sales figures daily, cost figures monthly, and profit figures quarterly. Step 2: The graphing unit graphs the performance figures collected by the collection unit. Graphing is done in the form of bar graphs, line graphs, pie charts, etc. For example, sales performance is displayed as a bar graph, cost performance as a line graph, and profit performance as a pie chart. Step 3: The linking unit links the actual figures graphed by the graphing unit to the budget figures. Linking is performed by displaying a graph in which the actual figures and budget figures are superimposed. For example, it displays a bar graph in which sales results and budget figures are superimposed, a line graph in which cost results and budget figures are superimposed, and a pie chart in which profit results and budget figures are superimposed.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

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

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

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

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

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

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

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

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

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

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

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

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

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

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

[0127] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0130] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

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

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

[0136] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

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

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

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

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

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

[0142] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0144] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

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

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

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

[0151] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

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

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

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

[0155] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0156] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

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

[0158] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

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

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

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

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

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

[0164] [Explanation of symbols]

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

Claims

1. a collection department that collects performance figures; a graphing unit that graphs the performance figures collected by the collecting unit; a linking unit that links the actual figures graphed by the graphing unit with budget figures; Equipped with A system characterized by:

2. 2. The system according to claim 1, wherein the collection unit automatically collects data on sales performance or cost performance of each department.

3. The graphing unit Graph the collected performance figures in a visually easy-to-understand format 2. The system of claim 1.

4. The system according to claim 1 , wherein the graphing unit graphs the performance figures using a bar graph or a line graph.

5. The linking unit Display a graph that overlays the graphed actual figures with the budget figures 2. The system of claim 1.

6. The linking unit Facilitate comparison of actual and budgeted figures 2. The system of claim 1.

7. The collecting unit To estimate a user's feelings and adjust the timing of collecting performance figures based on the estimated user's feelings.

2. The system of claim 1.

8. The collecting unit When collecting sales and cost results from each department, evaluate the reliability of the data and collect only reliable data.

2. The system of claim 1.

Images