system

Abstract

The system according to the embodiment aims to automate the estimation process and reduce errors. [Solution] The system according to the embodiment comprises an import unit, a calculation unit, an error detection unit, a format conversion unit, and an update unit. The import unit acquires information from an existing Excel file. The calculation unit performs calculations based on the information acquired by the import unit. The error detection unit automatically detects and corrects errors in the calculations performed by the calculation unit. The format conversion unit automatically converts the calculation results corrected by the error detection unit into a predetermined format. The update unit immediately updates the calculation results converted by the format conversion unit whenever the Excel data is changed.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies are prone to errors in the calculation process, requiring manual corrections, which can reduce efficiency.

[0005] The system according to the embodiment aims to automate the estimation process and reduce errors. [Means for solving the problem]

[0006] The system according to this embodiment comprises an import unit, a calculation unit, an error detection unit, a format conversion unit, and an update unit. The import unit obtains information from an existing Excel file. The calculation unit performs calculations based on the information obtained by the import unit. The error detection unit automatically detects and corrects errors in the calculations performed by the calculation unit. The format conversion unit automatically converts the calculation results corrected by the error detection unit into a predetermined format. The update unit immediately updates the calculation results converted by the format conversion unit whenever the Excel data is changed. [Effects of the Invention]

[0007] The system according to this embodiment can automate the calculation process and reduce errors. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The estimation process efficiency system according to an embodiment of the present invention is a system for streamlining a company's estimation process. This system directly imports information from an existing Excel file, performs estimations, automatically detects and corrects estimation errors, and automatically converts the estimation results into a predetermined format. Furthermore, the estimation results are updated in real time whenever the data in Excel is changed. In addition, the estimation process can be saved and reused. This mechanism enables increased efficiency in estimation work and a reduction in errors. For example, information is directly imported from an existing Excel file. At this time, the AI ​​analyzes the data entered in each cell of the Excel file and extracts the necessary information. For example, it can automatically extract information necessary for estimation, such as sales data and cost data. Next, estimations are performed based on the imported information. The AI ​​automatically performs the calculations necessary for estimation and generates estimation results. At this time, if estimation errors occur, the AI ​​automatically detects and corrects the errors. For example, it can automatically detect and correct calculation errors and data inconsistencies. Furthermore, the estimation results are automatically converted into a predetermined format. For example, the estimation results can be converted into a predetermined format such as a PDF or Excel file. This makes it easy to share calculation results. Furthermore, the calculation results are updated in real time whenever the Excel data is changed. For example, if sales data changes, the calculation results are automatically updated. This ensures that you always have the latest calculation results. Finally, the calculation process can be saved and reused. For example, past calculation processes can be saved and reused when performing similar calculations. This improves the efficiency of calculation work. This mechanism enables increased efficiency and reduced errors in calculation work. For example, automating calculation tasks that were previously done manually can significantly reduce working time. Additionally, automatically detecting and correcting calculation errors improves the accuracy of the calculation results. Thus, the calculation process efficiency system achieves increased efficiency and reduced errors in calculation work.

[0029] The trial calculation process efficiency improvement system according to the embodiment includes an import unit, a trial calculation unit, an error detection unit, a format conversion unit, and an update unit. The import unit acquires information from an existing Excel file. For example, the import unit analyzes data entered in each cell of the Excel file and extracts necessary information. For example, the import unit can automatically extract information necessary for trial calculation, such as sales data and cost data. The trial calculation unit performs trial calculations based on the information acquired by the import unit. For example, the trial calculation unit calculates profits and losses based on the sales data and cost data. The error detection unit automatically detects and corrects errors in the trial calculations performed by the trial calculation unit. For example, the error detection unit automatically detects and corrects calculation errors and data inconsistencies. The format conversion unit automatically converts the trial calculation results corrected by the error detection unit into a predetermined format. For example, the format conversion unit converts the trial calculation results into a predetermined format, such as a PDF or Excel file. The update unit updates the trial calculation results converted by the format conversion unit in real time whenever the Excel data is changed. For example, when sales data is changed, the update unit automatically updates the estimate results. As a result, the estimate process efficiency improvement system according to the embodiment can improve the efficiency of estimate work and reduce errors.

[0030] The estimation process efficiency improvement system includes a storage unit that stores and reuses estimation processes. The storage unit can store and reuse estimation processes. For example, the storage unit can store past estimation processes and reuse them when performing similar estimations. This enables the estimation process to be reused, thereby improving efficiency. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the estimation process into AI and have the AI ​​store and reuse the estimation process.

[0031] The import unit can analyze data entered in each cell of an Excel file and extract necessary information. The import unit can, for example, analyze data entered in each cell of an Excel file and extract necessary information. For example, the import unit can automatically extract information necessary for calculations, such as sales data and cost data. The import unit can also extract specific data by specifying a cell range of the Excel file. For example, the import unit can extract only sales data by specifying a specific cell range. This allows necessary information to be automatically extracted from the Excel file. Some or all of the above-described processing in the import unit can be performed using, or without, AI. For example, the import unit can input data from the Excel file into AI and have the AI ​​extract the necessary information.

[0032] The error detection unit can automatically detect and correct calculation errors and data inconsistencies. For example, the error detection unit can automatically detect and correct calculation errors and data inconsistencies. For example, the error detection unit can automatically detect and correct formula errors and inconsistencies in calculation results. The error detection unit can also automatically detect and correct data type inconsistencies and missing data. For example, the error detection unit can detect data type inconsistencies and convert them to the appropriate data type. This allows for automatic detection and correction of calculation errors. Some or all of the above-mentioned processing in the error detection unit may be performed using, or without, AI. For example, the error detection unit can input calculation data into AI and have the AI ​​detect and correct errors.

[0033] The format conversion unit can convert the trial calculation results into a PDF or Excel file. For example, the format conversion unit can convert the trial calculation results into a PDF or Excel file. For example, the format conversion unit can convert the trial calculation results into PDF format and share them. The format conversion unit can also convert the trial calculation results into Excel format and save them in an editable state. For example, the format conversion unit can convert the trial calculation results into Excel format and share them with other users. This allows the trial calculation results to be automatically converted into a predetermined format. Some or all of the above-described processing in the format conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the format conversion unit can input the trial calculation results into AI and have the AI ​​convert them into PDF or Excel format.

[0034] The update unit can automatically update the trial calculation results every time the Excel data is changed. The update unit automatically updates the trial calculation results, for example, every time the Excel data is changed. For example, the update unit can automatically update the trial calculation results when sales data is changed. The update unit can also automatically update the trial calculation results when cost data is changed. For example, the update unit can update the trial calculation results in real time when cost data is changed. This allows the trial calculation results to be updated in real time. Some or all of the above-mentioned processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit may cause AI to detect changes to the Excel data and update the trial calculation results.

[0035] The import unit can analyze the metadata of an Excel file during import and select an appropriate import method. For example, the import unit can analyze the creation date and last modified date of an Excel file and prioritize importing the latest data. It can also analyze the creator information of an Excel file and prioritize importing highly reliable data. Furthermore, the import unit can analyze the size and number of sheets of an Excel file and select an efficient import method. For example, if an Excel file is large, the import unit can select a method to split and import it. This allows for the selection of the optimal import method by analyzing the metadata of the Excel file. Some or all of the above processes in the import unit may be performed using AI, or not. For example, the import unit can input the metadata of an Excel file into an AI and have the AI ​​select the optimal import method.

[0036] The import unit can apply different import algorithms depending on the version and format of the Excel file during import. For example, the import unit can apply different import algorithms depending on the version and format of the Excel file during import. For example, if the Excel file is an old version, the import unit can apply a compatible import algorithm. Furthermore, if the Excel file has a different format, the import unit can perform an appropriate conversion process before importing. Furthermore, if the Excel file contains macros, the import unit can disable the macros before importing. For example, the import unit can disable macros in the Excel file and apply a method for safe import. This allows an appropriate import algorithm to be applied depending on the version and format of the Excel file. Some or all of the above-described processing in the import unit can be performed using, or without, AI. For example, the import unit can input version and format information of the Excel file into AI and have the AI ​​apply an appropriate import algorithm.

[0037] The import unit can prioritize importing highly relevant data by taking into account the user's geographical location information during import. For example, the import unit prioritizes importing highly relevant data by taking into account the user's geographical location information during import. For example, if the user is in a specific region, the import unit can prioritize importing data related to that region. Furthermore, if the user is traveling, the import unit can prioritize importing data closest to the user's current location. Furthermore, if the user is in a specific country, the import unit can prioritize importing data related to that country. For example, the import unit can automatically select and import highly relevant data based on the user's geographical location information. This allows highly relevant data to be imported by taking into account the user's geographical location information. Some or all of the above-described processing by the import unit may be performed using, for example, AI, or may be performed without using AI. For example, the import unit can input the user's geographical location information into AI and cause the AI ​​to select and import highly relevant data.

[0038] The import unit can analyze a user's social media activity and import relevant data during the import process. For example, the import unit can import relevant data based on information shared by the user on social media. It can also import relevant data based on information about accounts followed by the user on social media. Furthermore, it can import relevant data based on information about groups the user participates in on social media. For example, the import unit can analyze a user's social media activity, automatically select highly relevant data, and import it. This allows for the analysis of a user's social media activity and the import of relevant data. Some or all of the above processing in the import unit may be performed using AI, or not. For example, the import unit can input the user's social media activity data into an AI and have the AI ​​select and import relevant data.

[0039] The calculation unit can select the optimal calculation method by referring to past calculation data during the calculation process. For example, the calculation unit can select the most efficient calculation method based on past calculation data. The calculation unit can also select a calculation method with fewer errors from past calculation data. Furthermore, the calculation unit can analyze past calculation data and select the most accurate calculation method. For example, the calculation unit can select a method to improve the accuracy of the calculation based on past calculation data. This allows the calculation unit to select the optimal calculation method by referring to past calculation data. Some or all of the above processes in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input past calculation data into AI and have AI select the optimal calculation method.

[0040] The estimation unit can apply different estimation algorithms depending on the purpose of the estimation when performing the estimation. For example, the estimation unit can apply different estimation algorithms depending on the purpose of the estimation when performing the estimation. For example, if the purpose is cost reduction, the estimation unit can apply an estimation algorithm specialized for cost reduction. Furthermore, if the purpose is sales increase, the estimation unit can apply an estimation algorithm specialized for sales increase. Furthermore, if the purpose is risk management, the estimation unit can apply an estimation algorithm specialized for risk management. For example, the estimation unit can select and apply an optimal estimation algorithm depending on the purpose of the estimation. This allows different estimation algorithms to be applied depending on the purpose of the estimation. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input the purpose of the estimation into AI and have the AI ​​apply an appropriate estimation algorithm.

[0041] The estimation unit can select the optimal estimation method by taking into account the user's industry information during estimation. For example, the estimation unit selects the optimal estimation method by taking into account the user's industry information during estimation. For example, if the user is engaged in the manufacturing industry, the estimation unit can select an estimation method specialized for the manufacturing industry. Furthermore, if the user is engaged in the service industry, the estimation unit can select an estimation method specialized for the service industry. Furthermore, if the user is engaged in the financial industry, the estimation unit can select an estimation method specialized for the financial industry. For example, the estimation unit can select the optimal estimation method based on the user's industry information. This makes it possible to select the optimal estimation method by taking into account the user's industry information. Some or all of the above-mentioned processing in the estimation unit may be performed using AI, for example, or may be performed without using AI. For example, the estimation unit can input the user's industry information into AI and have the AI ​​select the optimal estimation method.

[0042] The estimation unit can improve the accuracy of its estimations by referring to the user's past project data during the estimation process. For example, the estimation unit can improve the accuracy of its estimations by referring to the user's past project data during the estimation process. For example, the estimation unit can improve the accuracy of its estimations based on the user's past project data. The estimation unit can also select an estimation method with fewer errors from the user's past project data. Furthermore, the estimation unit can analyze the user's past project data and select the most accurate estimation method. For example, the estimation unit can select a method to improve the accuracy of its estimations based on the user's past project data. This allows the estimation unit to improve the accuracy of its estimations by referring to the user's past project data. Some or all of the above-described processes in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input the user's past project data into AI and have AI perform the estimation accuracy improvement.

[0043] The error detection unit can select the optimal error detection method by referring to past error data when an error is detected. For example, the error detection unit can select the most efficient error detection method based on past error data. The error detection unit can also select an error detection method with fewer errors from past error data. Furthermore, the error detection unit can analyze past error data and select the most accurate error detection method. For example, the error detection unit can select a method to improve the accuracy of error detection based on past error data. This allows the unit to select the optimal error detection method by referring to past error data. Some or all of the above processing in the error detection unit may be performed using AI, for example, or without using AI. For example, the error detection unit can input past error data into AI and have AI select the optimal error detection method.

[0044] The error detection unit can apply different error correction algorithms depending on the type of error when detecting an error. For example, the error detection unit can apply different error correction algorithms depending on the type of error when detecting an error. For example, in the case of a calculation error, the error detection unit can apply an algorithm that corrects the calculation error. Furthermore, in the case of a data inconsistency, the error detection unit can apply an algorithm that corrects the inconsistency. Furthermore, in the case of a format error, the error detection unit can apply an algorithm that corrects the format error. For example, the error detection unit can select and apply an optimal error correction algorithm depending on the type of error. This makes it possible to apply an appropriate error correction algorithm depending on the type of error. Some or all of the above-mentioned processing in the error detection unit can be performed using, or without, AI. For example, the error detection unit can input the type of error to AI and cause the AI ​​to apply an appropriate error correction algorithm.

[0045] The error detection unit can select the optimal error detection method by taking into account the user's industry information when detecting an error. For example, the error detection unit can select the optimal error detection method by taking into account the user's industry information when detecting an error. For example, if the user is engaged in the manufacturing industry, the error detection unit can select an error detection method specialized for the manufacturing industry. Furthermore, if the user is engaged in the service industry, the error detection unit can select an error detection method specialized for the service industry. Furthermore, if the user is engaged in the financial industry, the error detection unit can select an error detection method specialized for the financial industry. For example, the error detection unit can select the optimal error detection method based on the user's industry information. This allows the optimal error detection method to be selected by taking into account the user's industry information. Some or all of the above-described processing in the error detection unit can be performed using, for example, AI, or without AI. For example, the error detection unit can input the user's industry information into AI and have the AI ​​select the optimal error detection method.

[0046] The error detection unit can improve the accuracy of error detection by referring to the user's past error data when detecting an error. The error detection unit can improve the accuracy of error detection by referring to the user's past error data, for example, when detecting an error. For example, the error detection unit can improve the accuracy of error detection based on the user's past error data. The error detection unit can also select an error detection method with fewer errors from the user's past error data. Furthermore, the error detection unit can analyze the user's past error data and select the most accurate error detection method. For example, the error detection unit can select a method for improving the accuracy of error detection based on the user's past error data. This makes it possible to improve the accuracy of error detection by referring to the user's past error data. Some or all of the above-described processing in the error detection unit can be performed using, or without, AI, for example. For example, the error detection unit can input the user's past error data into AI and have the AI ​​improve the accuracy of error detection.

[0047] The format conversion unit can select an optimal format conversion method by referring to past format conversion data during format conversion. For example, the format conversion unit can select the optimal format conversion method by referring to past format conversion data during format conversion. For example, the format conversion unit can select the most efficient format conversion method based on past format conversion data. The format conversion unit can also select a format conversion method with fewer errors from the past format conversion data. Furthermore, the format conversion unit can analyze past format conversion data and select the most accurate format conversion method. For example, the format conversion unit can select a method that improves the accuracy of format conversion based on the past format conversion data. This allows the optimal format conversion method to be selected by referring to the past format conversion data. Some or all of the above-described processing in the format conversion unit may be performed using, or without, AI. For example, the format conversion unit can input past format conversion data into AI and have the AI ​​select the optimal format conversion method.

[0048] The format conversion unit can apply different conversion algorithms depending on the target format during format conversion. For example, when converting to PDF format, the format conversion unit can apply a conversion algorithm specialized for PDF. Similarly, when converting to Excel format, the format conversion unit can apply a conversion algorithm specialized for Excel. Furthermore, when converting to CSV format, the format conversion unit can apply a conversion algorithm specialized for CSV. For example, the format conversion unit can select and apply the optimal conversion algorithm depending on the target format. This ensures that the appropriate conversion algorithm is applied according to the target format. Some or all of the above-described processes in the format conversion unit may be performed using AI, or without AI. For example, the format conversion unit can input target format information into AI and have AI perform the application of an appropriate conversion algorithm.

[0049] The format conversion unit can select the optimal format conversion method by taking into account the user's industry information during format conversion. For example, the format conversion unit selects the optimal format conversion method by taking into account the user's industry information during format conversion. For example, if the user is engaged in the manufacturing industry, the format conversion unit can select a format conversion method specialized for the manufacturing industry. Furthermore, if the user is engaged in the service industry, the format conversion unit can select a format conversion method specialized for the service industry. Furthermore, if the user is engaged in the financial industry, the format conversion unit can select a format conversion method specialized for the financial industry. For example, the format conversion unit can select the optimal format conversion method based on the user's industry information. This makes it possible to select the optimal format conversion method by taking into account the user's industry information. Some or all of the above-described processing in the format conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the format conversion unit can input the user's industry information into AI and have the AI ​​select the optimal format conversion method.

[0050] The format conversion unit can improve the accuracy of the conversion by referring to the user's past format conversion data during format conversion. For example, the format conversion unit can improve the accuracy of the conversion by referring to the user's past format conversion data during format conversion. For example, the format conversion unit can improve the accuracy of the conversion based on the user's past format conversion data. The format conversion unit can also select a format conversion method with fewer errors from the user's past format conversion data. Furthermore, the format conversion unit can analyze the user's past format conversion data and select the most accurate format conversion method. For example, the format conversion unit can select a method for improving the accuracy of the conversion based on the user's past format conversion data. This makes it possible to improve the accuracy of the conversion by referring to the user's past format conversion data. Some or all of the above-described processing in the format conversion unit may be performed using, or without, AI. For example, the format conversion unit can input the user's past format conversion data into AI and have the AI ​​improve the accuracy of the conversion.

[0051] The update unit can select the optimal update method by referring to past update data during an update. The update unit, for example, selects the optimal update method by referring to past update data during an update. For example, the update unit can select the most efficient update method based on past update data. The update unit can also select an update method with fewer errors from the past update data. Furthermore, the update unit can analyze past update data and select the most accurate update method. For example, the update unit can select a method that improves update accuracy based on past update data. This makes it possible to select the optimal update method by referring to the past update data. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input past update data into AI and have the AI ​​select the optimal update method.

[0052] The update unit can apply different update algorithms depending on the update frequency during an update. The update unit can apply different update algorithms depending on the update frequency during an update, for example. For example, the update unit can apply a real-time update algorithm when frequent updates are required. The update unit can also apply a schedule-based update algorithm when periodic updates are required. Furthermore, the update unit can apply a one-time update algorithm when only a one-time update is required. For example, the update unit can select and apply an optimal update algorithm depending on the update frequency. This makes it possible to apply an appropriate update algorithm depending on the update frequency. Some or all of the above-described processing in the update unit can be performed using, for example, AI, or can be performed without using AI. For example, the update unit can input update frequency information to AI and cause the AI ​​to apply an appropriate update algorithm.

[0053] The update unit can select the optimal update method by taking into account the user's industry information during an update. For example, the update unit selects the optimal update method by taking into account the user's industry information during an update. For example, if the user is engaged in the manufacturing industry, the update unit can select an update method specialized for the manufacturing industry. Furthermore, if the user is engaged in the service industry, the update unit can select an update method specialized for the service industry. Furthermore, if the user is engaged in the financial industry, the update unit can select an update method specialized for the financial industry. For example, the update unit can select the optimal update method based on the user's industry information. This makes it possible to select the optimal update method by taking into account the user's industry information. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input the user's industry information into AI and have the AI ​​select the optimal update method.

[0054] The update unit can improve the accuracy of updates by referring to the user's past update data during the update process. For example, the update unit can improve the accuracy of updates by referring to the user's past update data during the update process. For example, the update unit can improve the accuracy of updates based on the user's past update data. The update unit can also select an update method with fewer errors from the user's past update data. Furthermore, the update unit can analyze the user's past update data and select the most accurate update method. For example, the update unit can select a method to improve the accuracy of updates based on the user's past update data. This allows the update unit to improve the accuracy of updates by referring to the user's past update data. Some or all of the above processing in the update unit may be performed using AI, for example, or without using AI. For example, the update unit can input the user's past update data into AI and have AI perform the update accuracy improvement.

[0055] The storage unit can select the optimal storage method by referring to past saved data during storage. For example, the storage unit can select the optimal storage method by referring to past saved data during storage. For example, the storage unit can select the most efficient storage method based on past saved data. The storage unit can also select a storage method with fewer errors from past saved data. Furthermore, the storage unit can analyze past saved data and select the most accurate storage method. For example, the storage unit can select a method to improve the accuracy of storage based on past saved data. This allows the storage unit to select the optimal storage method by referring to past saved data. Some or all of the above processes in the storage unit may be performed using AI, for example, or without using AI. For example, the storage unit can input past saved data into AI and have the AI ​​select the optimal storage method.

[0056] The storage unit can select the optimal storage method by taking into account the user's industry information when saving. For example, the storage unit selects the optimal storage method by taking into account the user's industry information when saving. For example, if the user is engaged in the manufacturing industry, the storage unit can select a storage method specialized for the manufacturing industry. Furthermore, if the user is engaged in the service industry, the storage unit can select a storage method specialized for the service industry. Furthermore, if the user is engaged in the financial industry, the storage unit can select a storage method specialized for the financial industry. For example, the storage unit can select the optimal storage method based on the user's industry information. This allows the optimal storage method to be selected by taking into account the user's industry information. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the user's industry information into AI and have the AI ​​select the optimal storage method.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The trial calculation process efficiency improvement system can further include a data visualization unit. The data visualization unit can display trial calculation results in a visual format such as a graph or chart. For example, sales data can be displayed in a line graph, allowing users to visually check monthly sales trends. Cost data can be displayed in a pie chart, allowing users to grasp the proportion of each cost item at a glance. Profit data can be displayed in a bar graph, allowing users to compare increases and decreases in profits for each month. This makes the trial calculation results visually easier to understand.

[0059] The estimation process efficiency improvement system may further include a notification unit. The notification unit may notify the user of the estimation results and error detection results. For example, when the estimation results are generated, the user may be notified by email. Also, when an error is detected, the user may be notified by push notification. Furthermore, when the estimation results are updated, the user may be notified in real time. This allows the user to quickly understand the estimation results and error detection results.

[0060] The trial calculation process efficiency improvement system can further include a voice input unit. The voice input unit allows the user to input trial calculation data by voice. For example, if the user inputs "This month's sales are 1 million yen" by voice, the data is automatically input into the system. The voice input unit can also display trial calculation results in response to the user's voice command. For example, if the user commands by voice, "Tell me this month's profit," the system will display the trial calculation results. This allows the user to input trial calculation data and check the results without using their hands.

[0061] The estimation process efficiency improvement system can further include a data backup unit. The data backup unit can periodically back up estimation data and estimation results. For example, automatic backups can be performed every night to prevent data loss. The backup data is also stored in cloud storage and can be restored at any time. Furthermore, the backup data is encrypted before storage, ensuring security. This improves the safety and reliability of the estimation data.

[0062] The trial calculation process efficiency improvement system can further include a data filtering unit. The data filtering unit can filter the data used for trial calculations and extract only the necessary data. For example, trial calculations can be performed by extracting only sales data for a specific period. It can also extract only data that meets specific conditions. Furthermore, the data filtering unit can eliminate data duplication and provide data for accurate trial calculations. This can improve the accuracy of trial calculations.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The import unit obtains information from an existing Excel file. For example, the import unit analyzes the data entered in each cell of the Excel file and extracts the necessary information. It can automatically extract information necessary for calculations, such as sales data and cost data. Step 2: The calculation unit performs calculations based on the information obtained by the import unit. For example, the calculation unit calculates profits and losses based on sales data and cost data. Step 3: The error detection unit automatically detects and corrects errors in the calculations performed by the estimation unit. For example, the error detection unit automatically detects and corrects calculation errors and data inconsistencies. Step 4: The format conversion unit automatically converts the trial calculation results corrected by the error detection unit into a predetermined format. For example, the format conversion unit converts the trial calculation results into a predetermined format such as a PDF or Excel file. Step 5: The update unit updates the calculation results converted by the format conversion unit in real time whenever the Excel data is changed. For example, if sales data is changed, the update unit automatically updates the calculation results.

[0065] (Example 2) The estimation process efficiency system according to an embodiment of the present invention is a system for streamlining a company's estimation process. This system directly imports information from an existing Excel file, performs estimations, automatically detects and corrects estimation errors, and automatically converts the estimation results into a predetermined format. Furthermore, the estimation results are updated in real time whenever the data in Excel is changed. In addition, the estimation process can be saved and reused. This mechanism enables increased efficiency in estimation work and a reduction in errors. For example, information is directly imported from an existing Excel file. At this time, the AI ​​analyzes the data entered in each cell of the Excel file and extracts the necessary information. For example, it can automatically extract information necessary for estimation, such as sales data and cost data. Next, estimations are performed based on the imported information. The AI ​​automatically performs the calculations necessary for estimation and generates estimation results. At this time, if estimation errors occur, the AI ​​automatically detects and corrects the errors. For example, it can automatically detect and correct calculation errors and data inconsistencies. Furthermore, the estimation results are automatically converted into a predetermined format. For example, the estimation results can be converted into a predetermined format such as a PDF or Excel file. This makes it easy to share calculation results. Furthermore, the calculation results are updated in real time whenever the Excel data is changed. For example, if sales data changes, the calculation results are automatically updated. This ensures that you always have the latest calculation results. Finally, the calculation process can be saved and reused. For example, past calculation processes can be saved and reused when performing similar calculations. This improves the efficiency of calculation work. This mechanism enables increased efficiency and reduced errors in calculation work. For example, automating calculation tasks that were previously done manually can significantly reduce working time. Additionally, automatically detecting and correcting calculation errors improves the accuracy of the calculation results. Thus, the calculation process efficiency system achieves increased efficiency and reduced errors in calculation work.

[0066] The trial calculation process efficiency improvement system according to the embodiment includes an import unit, a trial calculation unit, an error detection unit, a format conversion unit, and an update unit. The import unit acquires information from an existing Excel file. For example, the import unit analyzes data entered in each cell of the Excel file and extracts necessary information. For example, the import unit can automatically extract information necessary for trial calculation, such as sales data and cost data. The trial calculation unit performs trial calculations based on the information acquired by the import unit. For example, the trial calculation unit calculates profits and losses based on the sales data and cost data. The error detection unit automatically detects and corrects errors in the trial calculations performed by the trial calculation unit. For example, the error detection unit automatically detects and corrects calculation errors and data inconsistencies. The format conversion unit automatically converts the trial calculation results corrected by the error detection unit into a predetermined format. For example, the format conversion unit converts the trial calculation results into a predetermined format, such as a PDF or Excel file. The update unit updates the trial calculation results converted by the format conversion unit in real time whenever the Excel data is changed. For example, when sales data is changed, the update unit automatically updates the estimate results. As a result, the estimate process efficiency improvement system according to the embodiment can improve the efficiency of estimate work and reduce errors.

[0067] The estimation process efficiency improvement system includes a storage unit that stores and reuses estimation processes. The storage unit can store and reuse estimation processes. For example, the storage unit can store past estimation processes and reuse them when performing similar estimations. This enables the estimation process to be reused, thereby improving efficiency. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the estimation process into AI and have the AI ​​store and reuse the estimation process.

[0068] The import unit can analyze data entered in each cell of an Excel file and extract necessary information. The import unit can, for example, analyze data entered in each cell of an Excel file and extract necessary information. For example, the import unit can automatically extract information necessary for calculations, such as sales data and cost data. The import unit can also extract specific data by specifying a cell range of the Excel file. For example, the import unit can extract only sales data by specifying a specific cell range. This allows necessary information to be automatically extracted from the Excel file. Some or all of the above-described processing in the import unit can be performed using, or without, AI. For example, the import unit can input data from the Excel file into AI and have the AI ​​extract the necessary information.

[0069] The error detection unit can automatically detect and correct calculation errors and data inconsistencies. For example, the error detection unit can automatically detect and correct calculation errors and data inconsistencies. For example, the error detection unit can automatically detect and correct formula errors and inconsistencies in calculation results. The error detection unit can also automatically detect and correct data type inconsistencies and missing data. For example, the error detection unit can detect data type inconsistencies and convert them to the appropriate data type. This allows for automatic detection and correction of calculation errors. Some or all of the above-mentioned processing in the error detection unit may be performed using, or without, AI. For example, the error detection unit can input calculation data into AI and have the AI ​​detect and correct errors.

[0070] The format conversion unit can convert the trial calculation results into a PDF or Excel file. For example, the format conversion unit can convert the trial calculation results into a PDF or Excel file. For example, the format conversion unit can convert the trial calculation results into PDF format and share them. The format conversion unit can also convert the trial calculation results into Excel format and save them in an editable state. For example, the format conversion unit can convert the trial calculation results into Excel format and share them with other users. This allows the trial calculation results to be automatically converted into a predetermined format. Some or all of the above-described processing in the format conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the format conversion unit can input the trial calculation results into AI and have the AI ​​convert them into PDF or Excel format.

[0071] The update unit can automatically update the trial calculation results every time the Excel data is changed. The update unit automatically updates the trial calculation results, for example, every time the Excel data is changed. For example, the update unit can automatically update the trial calculation results when sales data is changed. The update unit can also automatically update the trial calculation results when cost data is changed. For example, the update unit can update the trial calculation results in real time when cost data is changed. This allows the trial calculation results to be updated in real time. Some or all of the above-mentioned processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit may cause AI to detect changes to the Excel data and update the trial calculation results.

[0072] The import unit can estimate the user's emotions and adjust the import timing based on the estimated emotions. For example, if the user is stressed, the import unit can delay the import timing and wait until the user is relaxed. If the user is relaxed, the import unit can start the import immediately and proceed with the work efficiently. Furthermore, if the user is in a hurry, the import unit can perform the import quickly to prevent delays. This allows the import timing to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the import unit may be performed using AI or not using AI. For example, the import unit can input user emotion data into AI and have the AI ​​adjust the import timing.

[0073] The import unit can analyze the metadata of an Excel file during import and select an appropriate import method. For example, the import unit can analyze the creation date and last modified date of an Excel file and prioritize importing the latest data. It can also analyze the creator information of an Excel file and prioritize importing highly reliable data. Furthermore, the import unit can analyze the size and number of sheets of an Excel file and select an efficient import method. For example, if an Excel file is large, the import unit can select a method to split and import it. This allows for the selection of the optimal import method by analyzing the metadata of the Excel file. Some or all of the above processes in the import unit may be performed using AI, or not. For example, the import unit can input the metadata of an Excel file into an AI and have the AI ​​select the optimal import method.

[0074] The import unit can apply different import algorithms depending on the version and format of the Excel file during import. For example, the import unit can apply different import algorithms depending on the version and format of the Excel file during import. For example, if the Excel file is an old version, the import unit can apply a compatible import algorithm. Furthermore, if the Excel file has a different format, the import unit can perform an appropriate conversion process before importing. Furthermore, if the Excel file contains macros, the import unit can disable the macros before importing. For example, the import unit can disable macros in the Excel file and apply a method for safe import. This allows an appropriate import algorithm to be applied depending on the version and format of the Excel file. Some or all of the above-described processing in the import unit can be performed using, or without, AI. For example, the import unit can input version and format information of the Excel file into AI and have the AI ​​apply an appropriate import algorithm.

[0075] The import unit can estimate the user's emotions and determine the priority of the data to be imported based on the estimated user emotions. The import unit, for example, estimates the user's emotions and determines the priority of the data to be imported based on the estimated user emotions. For example, when the user is feeling stressed, the import unit can prioritize importing important data and leave less important data for later import. Furthermore, when the user is relaxed, the import unit can import all data evenly. Furthermore, when the user is in a hurry, the import unit can prioritize importing the most important data. This allows the priority of the data to be imported to be determined based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the import unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the import unit can input the user's emotion data into an AI and have the AI ​​determine the priority of the data to be imported.

[0076] The import unit can prioritize importing highly relevant data by taking into account the user's geographical location information during import. For example, the import unit prioritizes importing highly relevant data by taking into account the user's geographical location information during import. For example, if the user is in a specific region, the import unit can prioritize importing data related to that region. Furthermore, if the user is traveling, the import unit can prioritize importing data closest to the user's current location. Furthermore, if the user is in a specific country, the import unit can prioritize importing data related to that country. For example, the import unit can automatically select and import highly relevant data based on the user's geographical location information. This allows highly relevant data to be imported by taking into account the user's geographical location information. Some or all of the above-described processing by the import unit may be performed using, for example, AI, or may be performed without using AI. For example, the import unit can input the user's geographical location information into AI and cause the AI ​​to select and import highly relevant data.

[0077] The import unit can analyze a user's social media activity and import relevant data during the import process. For example, the import unit can import relevant data based on information shared by the user on social media. It can also import relevant data based on information about accounts followed by the user on social media. Furthermore, it can import relevant data based on information about groups the user participates in on social media. For example, the import unit can analyze a user's social media activity, automatically select highly relevant data, and import it. This allows for the analysis of a user's social media activity and the import of relevant data. Some or all of the above processing in the import unit may be performed using AI, or not. For example, the import unit can input the user's social media activity data into an AI and have the AI ​​select and import relevant data.

[0078] The estimation unit can estimate the user's emotions and adjust the presentation method of the estimation calculation based on the estimated user's emotions. For example, the estimation unit can estimate the user's emotions and adjust the presentation method of the estimation calculation based on the estimated user's emotions. For example, if the user is feeling stressed, the estimation unit can display a simple, highly visible estimation result. Furthermore, if the user is relaxed, the estimation unit can display a detailed estimation result. Furthermore, if the user is in a hurry, the estimation unit can display a summary of the estimation result. This allows the presentation method of the estimation calculation to be adjusted according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the estimation unit may be performed using an AI, for example, or without an AI. For example, the estimation unit can input the user's emotion data into an AI and have the AI ​​adjust the presentation method of the estimation calculation.

[0079] The calculation unit can select the optimal calculation method by referring to past calculation data during the calculation process. For example, the calculation unit can select the most efficient calculation method based on past calculation data. The calculation unit can also select a calculation method with fewer errors from past calculation data. Furthermore, the calculation unit can analyze past calculation data and select the most accurate calculation method. For example, the calculation unit can select a method to improve the accuracy of the calculation based on past calculation data. This allows the calculation unit to select the optimal calculation method by referring to past calculation data. Some or all of the above processes in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input past calculation data into AI and have AI select the optimal calculation method.

[0080] The estimation unit can apply different estimation algorithms depending on the purpose of the estimation when performing the estimation. For example, the estimation unit can apply different estimation algorithms depending on the purpose of the estimation when performing the estimation. For example, if the purpose is cost reduction, the estimation unit can apply an estimation algorithm specialized for cost reduction. Furthermore, if the purpose is sales increase, the estimation unit can apply an estimation algorithm specialized for sales increase. Furthermore, if the purpose is risk management, the estimation unit can apply an estimation algorithm specialized for risk management. For example, the estimation unit can select and apply an optimal estimation algorithm depending on the purpose of the estimation. This allows different estimation algorithms to be applied depending on the purpose of the estimation. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input the purpose of the estimation into AI and have the AI ​​apply an appropriate estimation algorithm.

[0081] The estimation unit can estimate the user's emotions and determine the priority of the estimation calculations based on the estimated user emotions. For example, the estimation unit can estimate the user's emotions and determine the priority of the estimation calculations based on the estimated user emotions. For example, when the user is feeling stressed, the estimation unit can postpone less important estimation calculations and prioritize important estimation calculations. Furthermore, when the user is relaxed, the estimation unit can perform all estimation calculations equally. Furthermore, when the user is in a hurry, the estimation unit can also perform the most important estimation calculations as the highest priority. This allows the priority of the estimation calculations to be determined according to the user's emotions. 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-mentioned processing in the estimation unit may be performed using an AI, for example, or without an AI. For example, the estimation unit can input the user's emotion data into an AI and have the AI ​​determine the priority of the estimation calculations.

[0082] The estimation unit can select the optimal estimation method by taking into account the user's industry information during estimation. For example, the estimation unit selects the optimal estimation method by taking into account the user's industry information during estimation. For example, if the user is engaged in the manufacturing industry, the estimation unit can select an estimation method specialized for the manufacturing industry. Furthermore, if the user is engaged in the service industry, the estimation unit can select an estimation method specialized for the service industry. Furthermore, if the user is engaged in the financial industry, the estimation unit can select an estimation method specialized for the financial industry. For example, the estimation unit can select the optimal estimation method based on the user's industry information. This makes it possible to select the optimal estimation method by taking into account the user's industry information. Some or all of the above-mentioned processing in the estimation unit may be performed using AI, for example, or may be performed without using AI. For example, the estimation unit can input the user's industry information into AI and have the AI ​​select the optimal estimation method.

[0083] The estimation unit can improve the accuracy of its estimations by referring to the user's past project data during the estimation process. For example, the estimation unit can improve the accuracy of its estimations by referring to the user's past project data during the estimation process. For example, the estimation unit can improve the accuracy of its estimations based on the user's past project data. The estimation unit can also select an estimation method with fewer errors from the user's past project data. Furthermore, the estimation unit can analyze the user's past project data and select the most accurate estimation method. For example, the estimation unit can select a method to improve the accuracy of its estimations based on the user's past project data. This allows the estimation unit to improve the accuracy of its estimations by referring to the user's past project data. Some or all of the above-described processes in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input the user's past project data into AI and have AI perform the estimation accuracy improvement.

[0084] The error detection unit can estimate the user's emotions and adjust the error detection method based on the estimated emotions. For example, the error detection unit can provide a simple and highly visible error detection method when the user is stressed. It can also provide a detailed error detection method when the user is relaxed. Furthermore, it can provide a concise error detection method when the user is in a hurry. This allows the error detection method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the error detection unit may be performed using AI or not using AI. For example, the error detection unit can input user emotion data into AI and have the AI ​​adjust the error detection method.

[0085] The error detection unit can select the optimal error detection method by referring to past error data when an error is detected. For example, the error detection unit can select the most efficient error detection method based on past error data. The error detection unit can also select an error detection method with fewer errors from past error data. Furthermore, the error detection unit can analyze past error data and select the most accurate error detection method. For example, the error detection unit can select a method to improve the accuracy of error detection based on past error data. This allows the unit to select the optimal error detection method by referring to past error data. Some or all of the above processing in the error detection unit may be performed using AI, for example, or without using AI. For example, the error detection unit can input past error data into AI and have AI select the optimal error detection method.

[0086] The error detection unit can apply different error correction algorithms depending on the type of error when detecting an error. For example, the error detection unit can apply different error correction algorithms depending on the type of error when detecting an error. For example, in the case of a calculation error, the error detection unit can apply an algorithm that corrects the calculation error. Furthermore, in the case of a data inconsistency, the error detection unit can apply an algorithm that corrects the inconsistency. Furthermore, in the case of a format error, the error detection unit can apply an algorithm that corrects the format error. For example, the error detection unit can select and apply an optimal error correction algorithm depending on the type of error. This makes it possible to apply an appropriate error correction algorithm depending on the type of error. Some or all of the above-mentioned processing in the error detection unit can be performed using, or without, AI. For example, the error detection unit can input the type of error to AI and cause the AI ​​to apply an appropriate error correction algorithm.

[0087] The error detection unit can estimate the user's emotions and determine the priority of error correction based on the estimated user emotions. The error detection unit, for example, estimates the user's emotions and determines the priority of error correction based on the estimated user emotions. For example, if the user is stressed, the error detection unit can prioritize correcting important errors and postpone correcting less important errors. Furthermore, if the user is relaxed, the error detection unit can correct all errors equally. Furthermore, if the user is in a hurry, the error detection unit can prioritize correcting the most important errors. This allows the priority of error correction to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI 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 error detection unit may be performed using, for example, an AI, or without an AI. For example, the error detection unit can input the user's emotion data into an AI and have the AI ​​determine the priority of error correction.

[0088] The error detection unit can select the optimal error detection method by taking into account the user's industry information when detecting an error. For example, the error detection unit can select the optimal error detection method by taking into account the user's industry information when detecting an error. For example, if the user is engaged in the manufacturing industry, the error detection unit can select an error detection method specialized for the manufacturing industry. Furthermore, if the user is engaged in the service industry, the error detection unit can select an error detection method specialized for the service industry. Furthermore, if the user is engaged in the financial industry, the error detection unit can select an error detection method specialized for the financial industry. For example, the error detection unit can select the optimal error detection method based on the user's industry information. This allows the optimal error detection method to be selected by taking into account the user's industry information. Some or all of the above-described processing in the error detection unit can be performed using, for example, AI, or without AI. For example, the error detection unit can input the user's industry information into AI and have the AI ​​select the optimal error detection method.

[0089] The error detection unit can improve the accuracy of error detection by referring to the user's past error data when detecting an error. The error detection unit can improve the accuracy of error detection by referring to the user's past error data, for example, when detecting an error. For example, the error detection unit can improve the accuracy of error detection based on the user's past error data. The error detection unit can also select an error detection method with fewer errors from the user's past error data. Furthermore, the error detection unit can analyze the user's past error data and select the most accurate error detection method. For example, the error detection unit can select a method for improving the accuracy of error detection based on the user's past error data. This makes it possible to improve the accuracy of error detection by referring to the user's past error data. Some or all of the above-described processing in the error detection unit can be performed using, or without, AI, for example. For example, the error detection unit can input the user's past error data into AI and have the AI ​​improve the accuracy of error detection.

[0090] The format conversion unit can estimate the user's emotion and adjust the format conversion method based on the estimated user's emotion. For example, the format conversion unit can estimate the user's emotion and adjust the format conversion method based on the estimated user's emotion. For example, if the user is feeling stressed, the format conversion unit can convert the data into a simple, highly visible format. If the user is relaxed, the format conversion unit can convert the data into a format containing detailed information. If the user is in a hurry, the format conversion unit can convert the data into a format that focuses on the main points. This allows the format conversion method to be adjusted according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, such as 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 format conversion unit can be performed using, for example, an AI, or without an AI. For example, the format conversion unit can input the user's emotion data into an AI and have the AI ​​adjust the format conversion method.

[0091] The format conversion unit can select an optimal format conversion method by referring to past format conversion data during format conversion. For example, the format conversion unit can select the optimal format conversion method by referring to past format conversion data during format conversion. For example, the format conversion unit can select the most efficient format conversion method based on past format conversion data. The format conversion unit can also select a format conversion method with fewer errors from the past format conversion data. Furthermore, the format conversion unit can analyze past format conversion data and select the most accurate format conversion method. For example, the format conversion unit can select a method that improves the accuracy of format conversion based on the past format conversion data. This allows the optimal format conversion method to be selected by referring to the past format conversion data. Some or all of the above-described processing in the format conversion unit may be performed using, or without, AI. For example, the format conversion unit can input past format conversion data into AI and have the AI ​​select the optimal format conversion method.

[0092] The format conversion unit can apply different conversion algorithms depending on the target format during format conversion. For example, when converting to PDF format, the format conversion unit can apply a conversion algorithm specialized for PDF. Similarly, when converting to Excel format, the format conversion unit can apply a conversion algorithm specialized for Excel. Furthermore, when converting to CSV format, the format conversion unit can apply a conversion algorithm specialized for CSV. For example, the format conversion unit can select and apply the optimal conversion algorithm depending on the target format. This ensures that the appropriate conversion algorithm is applied according to the target format. Some or all of the above-described processes in the format conversion unit may be performed using AI, or without AI. For example, the format conversion unit can input target format information into AI and have AI perform the application of an appropriate conversion algorithm.

[0093] The format conversion unit can estimate the user's emotions and determine the priority of format conversion based on the estimated user's emotions. The format conversion unit, for example, estimates the user's emotions and determines the priority of format conversion based on the estimated user's emotions. For example, when the user is stressed, the format conversion unit can prioritize important format conversions and postpone less important format conversions. Furthermore, when the user is relaxed, the format conversion unit can perform all format conversions equally. Furthermore, when the user is in a hurry, the format conversion unit can also prioritize the most important format conversion. This allows the priority of format conversions to be determined according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the format conversion unit may be performed using, for example, an AI, or without an AI. For example, the format conversion unit can input the user's emotion data into an AI and have the AI ​​determine the priority of format conversions.

[0094] The format conversion unit can select the optimal format conversion method by taking into account the user's industry information during format conversion. For example, the format conversion unit selects the optimal format conversion method by taking into account the user's industry information during format conversion. For example, if the user is engaged in the manufacturing industry, the format conversion unit can select a format conversion method specialized for the manufacturing industry. Furthermore, if the user is engaged in the service industry, the format conversion unit can select a format conversion method specialized for the service industry. Furthermore, if the user is engaged in the financial industry, the format conversion unit can select a format conversion method specialized for the financial industry. For example, the format conversion unit can select the optimal format conversion method based on the user's industry information. This makes it possible to select the optimal format conversion method by taking into account the user's industry information. Some or all of the above-described processing in the format conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the format conversion unit can input the user's industry information into AI and have the AI ​​select the optimal format conversion method.

[0095] The format conversion unit can improve the accuracy of the conversion by referring to the user's past format conversion data during format conversion. For example, the format conversion unit can improve the accuracy of the conversion by referring to the user's past format conversion data during format conversion. For example, the format conversion unit can improve the accuracy of the conversion based on the user's past format conversion data. The format conversion unit can also select a format conversion method with fewer errors from the user's past format conversion data. Furthermore, the format conversion unit can analyze the user's past format conversion data and select the most accurate format conversion method. For example, the format conversion unit can select a method for improving the accuracy of the conversion based on the user's past format conversion data. This makes it possible to improve the accuracy of the conversion by referring to the user's past format conversion data. Some or all of the above-described processing in the format conversion unit may be performed using, or without, AI. For example, the format conversion unit can input the user's past format conversion data into AI and have the AI ​​improve the accuracy of the conversion.

[0096] The update unit can estimate the user's emotion and adjust the timing of updates based on the estimated user's emotion. The update unit, for example, estimates the user's emotion and adjusts the timing of updates based on the estimated user's emotion. For example, if the user is feeling stressed, the update unit can delay the timing of updates and wait until the user is relaxed. Furthermore, if the user is relaxed, the update unit can immediately start updates and efficiently proceed with work. Furthermore, if the user is in a hurry, the update unit can quickly perform updates to prevent work delays. This allows the timing of updates to be adjusted according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the update unit may be performed using an AI, for example, or without an AI. For example, the update unit can input the user's emotion data into an AI and have the AI ​​adjust the timing of updates.

[0097] The update unit can select the optimal update method by referring to past update data during an update. The update unit, for example, selects the optimal update method by referring to past update data during an update. For example, the update unit can select the most efficient update method based on past update data. The update unit can also select an update method with fewer errors from the past update data. Furthermore, the update unit can analyze past update data and select the most accurate update method. For example, the update unit can select a method that improves update accuracy based on past update data. This makes it possible to select the optimal update method by referring to the past update data. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input past update data into AI and have the AI ​​select the optimal update method.

[0098] The update unit can apply different update algorithms depending on the update frequency during an update. The update unit can apply different update algorithms depending on the update frequency during an update, for example. For example, the update unit can apply a real-time update algorithm when frequent updates are required. The update unit can also apply a schedule-based update algorithm when periodic updates are required. Furthermore, the update unit can apply a one-time update algorithm when only a one-time update is required. For example, the update unit can select and apply an optimal update algorithm depending on the update frequency. This makes it possible to apply an appropriate update algorithm depending on the update frequency. Some or all of the above-described processing in the update unit can be performed using, for example, AI, or can be performed without using AI. For example, the update unit can input update frequency information to AI and cause the AI ​​to apply an appropriate update algorithm.

[0099] The update unit can estimate the user's emotions and determine the priority of updates based on the estimated user emotions. The update unit, for example, estimates the user's emotions and determines the priority of updates based on the estimated user emotions. For example, when the user is feeling stressed, the update unit can postpone less important updates and prioritize important updates. Furthermore, when the user is relaxed, the update unit can perform all updates equally. Furthermore, when the user is in a hurry, the update unit can prioritize the most important updates. This allows the priority of updates to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the update unit may be performed using an AI, for example, or without an AI. For example, the update unit can input the user's emotion data into an AI and have the AI ​​determine the priority of updates.

[0100] The update unit can select the optimal update method by taking into account the user's industry information during an update. For example, the update unit selects the optimal update method by taking into account the user's industry information during an update. For example, if the user is engaged in the manufacturing industry, the update unit can select an update method specialized for the manufacturing industry. Furthermore, if the user is engaged in the service industry, the update unit can select an update method specialized for the service industry. Furthermore, if the user is engaged in the financial industry, the update unit can select an update method specialized for the financial industry. For example, the update unit can select the optimal update method based on the user's industry information. This makes it possible to select the optimal update method by taking into account the user's industry information. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input the user's industry information into AI and have the AI ​​select the optimal update method.

[0101] The update unit can improve the accuracy of updates by referring to the user's past update data during the update process. For example, the update unit can improve the accuracy of updates by referring to the user's past update data during the update process. For example, the update unit can improve the accuracy of updates based on the user's past update data. The update unit can also select an update method with fewer errors from the user's past update data. Furthermore, the update unit can analyze the user's past update data and select the most accurate update method. For example, the update unit can select a method to improve the accuracy of updates based on the user's past update data. This allows the update unit to improve the accuracy of updates by referring to the user's past update data. Some or all of the above processing in the update unit may be performed using AI, for example, or without using AI. For example, the update unit can input the user's past update data into AI and have AI perform the update accuracy improvement.

[0102] The storage unit can estimate the user's emotions and adjust the timing of saving based on the estimated user emotions. The storage unit, for example, estimates the user's emotions and adjusts the timing of saving based on the estimated user emotions. For example, if the user is feeling stressed, the storage unit can delay the timing of saving and wait until the user is relaxed. Furthermore, if the user is relaxed, the storage unit can immediately start saving, allowing the user to proceed with work efficiently. Furthermore, if the user is in a hurry, the storage unit can quickly save to prevent work delays. This allows the timing of saving to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the storage unit may be performed using an AI, for example, or without an AI. For example, the storage unit can input the user's emotion data into an AI and have the AI ​​adjust the timing of saving.

[0103] The storage unit can select the optimal storage method by referring to past saved data during storage. For example, the storage unit can select the optimal storage method by referring to past saved data during storage. For example, the storage unit can select the most efficient storage method based on past saved data. The storage unit can also select a storage method with fewer errors from past saved data. Furthermore, the storage unit can analyze past saved data and select the most accurate storage method. For example, the storage unit can select a method to improve the accuracy of storage based on past saved data. This allows the storage unit to select the optimal storage method by referring to past saved data. Some or all of the above processes in the storage unit may be performed using AI, for example, or without using AI. For example, the storage unit can input past saved data into AI and have the AI ​​select the optimal storage method.

[0104] The storage unit can estimate the user's emotions and determine the priority of saving based on the estimated user emotions. The storage unit, for example, estimates the user's emotions and determines the priority of saving based on the estimated user emotions. For example, when the user is feeling stressed, the storage unit can prioritize important saving over less important saving. Furthermore, when the user is relaxed, the storage unit can equally save all data. Furthermore, when the user is in a hurry, the storage unit can prioritize the most important saving. This allows the priority of saving to be determined according to the user's emotions. 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 storage unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the storage unit can input the user's emotion data into an AI and have the AI ​​determine the priority of saving.

[0105] The storage unit can select the optimal storage method by taking into account the user's industry information when saving. For example, the storage unit selects the optimal storage method by taking into account the user's industry information when saving. For example, if the user is engaged in the manufacturing industry, the storage unit can select a storage method specialized for the manufacturing industry. Furthermore, if the user is engaged in the service industry, the storage unit can select a storage method specialized for the service industry. Furthermore, if the user is engaged in the financial industry, the storage unit can select a storage method specialized for the financial industry. For example, the storage unit can select the optimal storage method based on the user's industry information. This allows the optimal storage method to be selected by taking into account the user's industry information. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the user's industry information into AI and have the AI ​​select the optimal storage method. === Hard Collateral 1-1 === Each of the multiple elements, including the import unit, estimation unit, error detection unit, format conversion unit, and update unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the import unit is implemented by the control unit 46A of the smart device 14 and acquires information from an Excel file. The estimation unit is implemented by the specific processing unit 290 of the data processing device 12 and performs estimation based on the acquired information. The error detection unit is implemented by the specific processing unit 290 of the data processing device 12 and automatically detects and corrects estimation errors. The format conversion unit is implemented by the control unit 46A of the smart device 14 and converts the estimation results into a predetermined format. The update unit is implemented by the specific processing unit 290 of the data processing device 12 and updates the estimation results in real time whenever the Excel data is changed. === Hard Collateral 1-2 === Each of the multiple elements, including the import unit, estimation unit, error detection unit, format conversion unit, and update 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 import unit is realized by the control unit 46A of the smart glasses 214 and acquires information from an Excel file. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs estimation based on the acquired information. The error detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically detects and corrects estimation errors. The format conversion unit is realized, for example, by the control unit 46A of the smart glasses 214 and converts the estimation results into a predetermined format. The update unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and updates the estimation results in real time whenever the Excel data is changed. === Hard Collateral 1-3 === Each of the multiple elements described above, including the import unit, calculation unit, error detection unit, format conversion unit, and update unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the import unit is implemented by the control unit 46A of the headset terminal 314 and acquires information from an Excel file. The calculation unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs calculations based on the acquired information. The error detection unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically detects and corrects calculation errors. The format conversion unit is implemented by the control unit 46A of the headset terminal 314 and converts the calculation results into a predetermined format. The update unit is implemented by the specific processing unit 290 of the data processing unit 12 and updates the calculation results in real time whenever the data in Excel is changed. === Hard Collateral 1-4 === Each of the multiple elements described above, including the import unit, calculation unit, error detection unit, format conversion unit, and update unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the import unit is implemented by the control unit 46A of the robot 414 and acquires information from an Excel file. The calculation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and performs calculations based on the acquired information. The error detection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically detects and corrects calculation errors. The format conversion unit is implemented by, for example, the control unit 46A of the robot 414 and converts the calculation results into a predetermined format. The update unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and updates the calculation results in real time whenever the data in Excel is changed.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The trial calculation process efficiency improvement system can further include a data visualization unit. The data visualization unit can display trial calculation results in a visual format such as a graph or chart. For example, sales data can be displayed in a line graph, allowing users to visually check monthly sales trends. Cost data can be displayed in a pie chart, allowing users to grasp the proportion of each cost item at a glance. Profit data can be displayed in a bar graph, allowing users to compare increases and decreases in profits for each month. This makes the trial calculation results visually easier to understand.

[0108] The estimation process efficiency improvement system may further include a notification unit. The notification unit may notify the user of the estimation results and error detection results. For example, when the estimation results are generated, the user may be notified by email. Also, when an error is detected, the user may be notified by push notification. Furthermore, when the estimation results are updated, the user may be notified in real time. This allows the user to quickly understand the estimation results and error detection results.

[0109] The trial calculation process efficiency improvement system can further include a voice input unit. The voice input unit allows the user to input trial calculation data by voice. For example, if the user inputs "This month's sales are 1 million yen" by voice, the data is automatically input into the system. The voice input unit can also display trial calculation results in response to the user's voice command. For example, if the user commands by voice, "Tell me this month's profit," the system will display the trial calculation results. This allows the user to input trial calculation data and check the results without using their hands.

[0110] The estimation process efficiency improvement system can further include a data backup unit. The data backup unit can periodically back up estimation data and estimation results. For example, automatic backups can be performed every night to prevent data loss. The backup data is also stored in cloud storage and can be restored at any time. Furthermore, the backup data is encrypted before storage, ensuring security. This improves the safety and reliability of the estimation data.

[0111] The trial calculation process efficiency improvement system can further include a data filtering unit. The data filtering unit can filter the data used for trial calculations and extract only the necessary data. For example, trial calculations can be performed by extracting only sales data for a specific period. It can also extract only data that meets specific conditions. Furthermore, the data filtering unit can eliminate data duplication and provide data for accurate trial calculations. This can improve the accuracy of trial calculations.

[0112] The estimation process efficiency improvement 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's operation may be slowed down to reduce the user's burden. Also, if the user is relaxed, the system may operate quickly to efficiently proceed with the work. Furthermore, if the user is in a hurry, the system's operation may be optimized to provide results quickly. This allows the system's operation to be adjusted according to the user's emotion.

[0113] The calculation process optimization system can also be equipped with an emotional feedback unit. This unit can provide real-time feedback on the user's emotions and adjust the system's operation accordingly. For example, if the user is stressed, the system can offer advice to help them relax. If the user is relaxed, the system can offer suggestions for working more efficiently. Furthermore, if the user is in a hurry, the system can suggest the best way to deliver results quickly. This allows the system's operation to be optimized according to the user's emotions.

[0114] The calculation process optimization system can also be equipped with an emotion logging section. This section records changes in the user's emotions as a log, which can then be analyzed later. For example, it can record the level of stress the user felt during calculation work and analyze its causes later. It can also record periods when the user is relaxed, allowing for concentration of calculation work during those times. Furthermore, it can record changes in the user's emotions when they are in a hurry, providing data to find more efficient work methods. This allows for understanding changes in the user's emotions and providing data to improve work efficiency.

[0115] The calculation process optimization system can also be equipped with an emotion prediction unit. This unit can predict future emotions based on the user's past emotional data. For example, if a user is prone to stress during certain times, it can suggest avoiding calculation work during those times. It can also concentrate calculation work during times when the user is more relaxed. Furthermore, it can predict emotional changes when a user is in a hurry and suggest more efficient work methods. This allows the system to predict user emotions and provide an optimal work environment.

[0116] The estimation process efficiency improvement system can further include an emotion monitoring unit. The emotion monitoring unit can monitor the user's emotions in real time and adjust the system's operation. For example, if the user is feeling stressed, the system can automatically adjust the pace of work to reduce the user's burden. Also, if the user is relaxed, the system can provide the optimal method for efficiently progressing with the work. Furthermore, if the user is in a hurry, the system can suggest the optimal method for quickly providing results. In this way, the system's operation can be adjusted in real time according to the user's emotions.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The import unit obtains information from an existing Excel file. For example, the import unit analyzes the data entered in each cell of the Excel file and extracts the necessary information. It can automatically extract information necessary for calculations, such as sales data and cost data. Step 2: The calculation unit performs calculations based on the information obtained by the import unit. For example, the calculation unit calculates profits and losses based on sales data and cost data. Step 3: The error detection unit automatically detects and corrects errors in the calculations performed by the estimation unit. For example, the error detection unit automatically detects and corrects calculation errors and data inconsistencies. Step 4: The format conversion unit automatically converts the trial calculation results corrected by the error detection unit into a predetermined format. For example, the format conversion unit converts the trial calculation results into a predetermined format such as a PDF or Excel file. Step 5: The update unit updates the calculation results converted by the format conversion unit in real time whenever the Excel data is changed. For example, if sales data is changed, the update unit automatically updates the calculation results.

[0119] 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.

[0120] 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.

[0121] 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.

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

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The 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.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 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.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the 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.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0137] The data processing system 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.

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

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The 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.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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. The import section retrieves information from an existing Excel file, A calculation unit performs calculations based on the information obtained by the aforementioned import unit, An error detection unit that automatically detects and corrects errors in the calculations performed by the calculation unit, A format conversion unit that automatically converts the calculation results corrected by the error detection unit into a predetermined format, The update unit instantly updates the calculation results converted by the format conversion unit whenever the Excel data is changed, Equipped with A system characterized by:

2. It includes a storage unit for saving and reusing the calculation process.

2. The system of claim 1.

3. The import unit Analyze the data entered in each cell of an Excel file and extract the necessary information.

2. The system of claim 1.

4. The error detection unit Automatically detects and corrects calculation errors and data inconsistencies.

2. The system of claim 1.

5. The format conversion unit Convert the calculation results to a PDF or Excel file.

2. The system of claim 1.

6. The update unit Automatically update the calculation results whenever the data in Excel is changed.

2. The system of claim 1.

7. The import unit It estimates the user's sentiment and adjusts the timing of the import based on the estimated user sentiment.

2. The system of claim 1.

8. The import unit During import, the system analyzes the metadata of the Excel file and selects the appropriate import method.

2. The system of claim 1.

9. The import unit During import, different import algorithms are applied depending on the version and format of the Excel file.

2. The system of claim 1.

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