System

Abstract

An object of a system according to an embodiment is to perform budget and performance aggregation of a business plan by effectively utilizing data in a cloud storage.SOLUTION: A system includes an acquisition unit, an analysis unit, and a generation unit. The acquisition unit acquires data from a cloud storage. The analysis unit analyzes the data acquired by the acquisition unit. The generation unit generates a report based on the data analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to effectively utilize data in cloud storage to compile forecasts and actual results for business plans, and there is room for improvement.

[0005] The system according to the embodiment aims to effectively utilize data in cloud storage to compile forecasts and actual results for business plans. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, and a generation unit. The acquisition unit acquires data from cloud storage. The analysis unit analyzes the data acquired by the acquisition unit. The generation unit generates a report based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively utilize data in cloud storage to compile forecasts and actual results for business plans. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention is designed for companies facing challenges in compiling business plan budgets and actual results. This system retrieves data from cloud storage, analyzes it using a generation AI, and generates reports. For example, data stored in cloud storage, such as PowerPoint presentations, Excel documents, and Zoom audio recordings, is input into the system. The generation AI then analyzes this data and compiles the business plan's budget and actual results. For example, it analyzes PowerPoint slides, Excel numerical data, and Zoom audio recordings to identify progress and issues with the business plan. Based on the analysis results, the generation AI then generates a report for strategic discussions. This report includes the business plan's progress, issues, and improvement measures. For example, if a specific project is delayed, the system can propose the cause and countermeasures. This transforms monthly business plan reviews from simple numerical reports to strategic discussions. Users can develop specific strategies based on the reports generated by the generation AI, thereby improving business efficiency and business improvement. Furthermore, because the system uses data stored in cloud storage, there is no need for individual development; by utilizing already accumulated data, the system can be put into practical use sooner. This allows the system to efficiently compile budgets and actual results for business plans and provide reports for strategic discussions. For example, when a user inputs PowerPoint, Excel documents, and Zoom audio saved in cloud storage into the system, the generation AI analyzes this data and compiles budgets and actual results for the business plan. The generation AI then generates a report based on the analysis results and provides it to the user. The user can use this report to hold strategic discussions and come up with measures to improve the business. In this way, utilizing generation AI makes it possible to efficiently compile budgets and actual results for business plans and enable strategic discussions.

[0029] The business plan budget / actual data compilation system according to the embodiment includes an acquisition unit, an analysis unit, and a generation unit. The acquisition unit acquires data from cloud storage. Examples of cloud storage include, but are not limited to, Google® Drive, Dropbox®, and OneDrive®. The acquisition unit acquires, for example, PowerPoint presentations, Excel documents, and Zoom audio data stored in the cloud storage. The acquisition unit can also analyze the update frequency of data in the cloud storage and set an optimal acquisition schedule. For example, if data is updated frequently, the acquisition unit can acquire data frequently to maintain the latest information. The analysis unit uses a generation AI to analyze the data acquired by the acquisition unit. The analysis is performed based on, for example, the content of PowerPoint slides, numerical data in Excel, and the content of discussions in Zoom audio, but is not limited to these examples. For example, the generation AI performs text analysis of the content of PowerPoint slides and extracts important points. The generation AI can also perform statistical analysis of numerical data in Excel to evaluate the progress of the business plan. The generation AI can also convert the content of discussions in Zoom audio into text using speech recognition technology and extract the key points of the discussion. The generation unit generates a report based on the data analyzed by the analysis unit. The report may be provided in, for example, a PDF format, a slideshow format, or a text format, but is not limited to these examples. The generation unit generates, for example, a report for strategic discussions based on the analysis results. If a specific project is delayed, the generation unit can also propose causes and countermeasures. Furthermore, the generation unit can generate a report including the progress of the business plan, problems, and improvement measures. As a result, the business plan budget / actual data compilation system according to the embodiment can efficiently compile budget / actual data for the business plan and provide a report for strategic discussions. For example, when a user inputs PowerPoint, Excel documents, and Zoom audio files stored in cloud storage into the system, the generation AI analyzes the data and compiles the budget / actual data for the business plan. The generation AI then generates a report based on the analysis results and provides it to the user. The user can use this report to hold strategic discussions and develop business improvement measures.In this way, by utilizing generative AI, the calculation of forecasts and actual results for business plans can be made more efficient, enabling strategic discussions.

[0030] The acquisition unit can acquire PPT, Excel documents, and Zoom audio data stored in cloud storage. The acquisition unit, for example, acquires PPT files stored in cloud storage. PPT files include slideshow presentations. The acquisition unit can also acquire Excel documents. Excel documents include spreadsheets, graphs, tables, etc. The acquisition unit can also acquire Zoom audio data. Zoom audio includes meeting recordings and audio files. This enables comprehensive data analysis by acquiring a variety of data stored in cloud storage. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data stored in cloud storage into a generation AI and have the generation AI acquire the data.

[0031] The analysis unit can analyze the content of PowerPoint slides, numerical data in Excel, and the content of discussions in Zoom audio to extract progress and issues in the business plan. For example, the analysis unit performs text analysis of the content of PowerPoint slides to extract key points. For example, the generation AI analyzes the text of the slides and extracts keywords and phrases. The analysis unit can also perform statistical analysis of numerical data in Excel to evaluate the progress of the business plan. For example, the generation AI analyzes numerical data and compares budgets with actual results. Furthermore, the analysis unit can convert the content of discussions in Zoom audio into text using voice recognition technology to extract key points from the discussion. For example, the generation AI analyzes audio data and extracts key points from the discussion. This allows for accurate understanding of the progress and issues in the business plan by analyzing various data sources. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the content of PowerPoint slides, numerical data in Excel, and the content of discussions in Zoom audio into the generation AI and have it perform the analysis.

[0032] The generation unit can generate a report for strategic discussions based on the analysis results. The generation unit generates a report for strategic discussions based on the analysis results, for example. The report includes the progress of the business plan, problems, and improvement measures. For example, if a specific project is delayed, the generation AI proposes the cause and countermeasures based on the analysis results. The generation unit can also generate a report that details the progress and problems of the business plan based on the analysis results. For example, the generation AI evaluates the progress of the business plan, identifies problems, and proposes improvement measures based on the analysis results. This enables strategic discussions by generating a report based on the analysis results. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the analysis results to the generation AI and cause the generation AI to generate a report.

[0033] If a specific project is delayed, the generation unit can propose the cause and countermeasures. For example, if a specific project is delayed, the generation unit proposes the cause and countermeasures. The generation AI identifies the cause of the delay based on the analysis results and proposes countermeasures. For example, the generation AI evaluates the progress of the project and identifies the cause of the delay. The generation AI can also propose specific countermeasures based on the cause of the delay. For example, it proposes countermeasures such as resource reallocation and process optimization. This enables rapid problem resolution by proposing the cause of the project delay and countermeasures. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can cause the generation AI to propose the cause of the delay and countermeasures.

[0034] The generation unit can generate a report including the progress, problems, and improvement measures of the business plan. The generation unit generates, for example, a report including the progress, problems, and improvement measures of the business plan. The report details the progress, problems, and improvement measures of the business plan. For example, the generation AI evaluates the progress of the business plan based on the analysis results, identifies problems, and proposes improvement measures. The generation unit can also provide the report in PDF format, slide format, text format, or the like. For example, the generation AI generates a PDF report based on the analysis results and provides it to the user. This makes it easier to grasp the overall picture of the business plan by generating a comprehensive report. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the analysis results to the generation AI and cause the generation AI to generate a report.

[0035] The acquisition unit can analyze the update frequency of data in the cloud storage and set an acquisition schedule. The acquisition unit, for example, analyzes the update frequency of data in the cloud storage and sets an optimal acquisition schedule. For example, if the data update frequency is high, the data can be acquired frequently to maintain the latest information. Alternatively, if the data update frequency is low, the data can be acquired periodically to process efficiently. Furthermore, if data updates are concentrated in a specific time period, data can be acquired according to that time period. This enables efficient data management by optimizing the acquisition schedule according to the data update frequency. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the data update frequency to the generation AI and have the generation AI set the acquisition schedule.

[0036] The acquisition unit can automatically adjust the range of data to be acquired based on the user's access authority when acquiring data. For example, if the user has administrator authority, the acquisition unit acquires all data. Also, if the user has general authority, the acquisition unit can acquire only necessary data. Furthermore, the range of data to be acquired can be adjusted according to the user's position. This enables efficient data acquisition while ensuring security by adjusting the data acquisition range according to the user's access authority. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's access authority data to the generation AI and have the generation AI adjust the data acquisition range.

[0037] When acquiring data, the acquisition unit can determine the priority of the data to be acquired by referring to the user's past data usage history. The acquisition unit, for example, prioritizes acquisition of data that the user has frequently used in the past. It can also prioritize acquisition of related data based on the type of data the user has used in the past. Furthermore, it can prioritize acquisition of data related to a specific project from the user's past usage history. Thus, by determining the priority of data based on the past data usage history, data that is important to the user can be acquired preferentially. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past data usage history into a generation AI and have the generation AI determine the priority of the data.

[0038] When acquiring data, the acquisition unit can prioritize acquiring highly relevant data based on the user's geographical location information. For example, if the user is in a specific area, the acquisition unit prioritizes acquiring data related to that area. Also, if the user is moving, the acquisition unit can acquire relevant data based on the user's current location. Furthermore, if the user is staying in a specific location, the acquisition unit can prioritize acquiring data related to that location. This allows for more appropriate data to be provided by preferentially acquiring highly relevant data based on the user's geographical location information. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information to the generation AI and cause the generation AI to acquire highly relevant data.

[0039] When acquiring data, the acquisition unit can analyze the user's social media activities and acquire related data. The acquisition unit can, for example, acquire related data based on information shared by the user on social media. The acquisition unit can also acquire related data by analyzing the user's social media activities. Furthermore, the acquisition unit can also acquire related data by referring to the activities of the user's friends on social media. In this way, by acquiring related data based on social media activities, it is possible to provide data that meets the user's needs. Some or all of the above-mentioned processing in the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input the user's social media activity data into the generation AI and cause the generation AI to acquire related data.

[0040] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring data. The acquisition unit can, for example, adjust the type of data to acquire based on feedback provided by the user in the past. The acquisition unit can also adjust the frequency of data acquisition by referring to the user's past feedback. Furthermore, the acquisition unit can adjust the priority of data acquisition based on the user's feedback. This makes it possible to acquire data according to the user's needs by customizing the data acquisition method based on past feedback. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the acquisition method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on important data. Also, the analysis unit can perform a simplified analysis on less important data. Furthermore, the analysis priority can be adjusted according to the importance of the data. This enables efficient data analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a statistical analysis algorithm to numerical data. Also, it can apply a natural language processing algorithm to text data. Furthermore, it can apply a voice analysis algorithm to voice data. In this way, by applying an analysis algorithm according to the data category, more accurate analysis results can be obtained. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the data category to the generation AI and have the generation AI apply the analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also adjust the level of analysis detail by referring to the user's past analysis results. Furthermore, the analysis unit can adjust the priority of the analysis based on the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the analysis priority based on the time of data submission. For example, the analysis unit prioritizes analysis of data whose submission deadline is approaching. It can also postpone data whose submission deadline has passed. Furthermore, it can adjust the analysis schedule based on the time of submission. This enables efficient data analysis by determining the analysis priority based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data submission to the generation AI and have the generation AI determine the analysis priority.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. It can also postpone analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the data. This enables efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Alternatively, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the method of expressing the analysis results can be adjusted according to the user's level of expertise. This allows the use of technical terms in the analysis to be adjusted according to the user's level of expertise, making it possible to provide analysis results that are easy for the user to understand. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise into the generation AI and have the generation AI use technical terms in the analysis.

[0047] When generating a report, the generation unit can adjust the level of detail of the report based on the importance of the analysis results. For example, the generation unit can provide a detailed report for important analysis results. Also, the generation unit can provide a simplified report for less important analysis results. Furthermore, the priority of the report can be adjusted according to the importance of the analysis results. This enables efficient report generation by adjusting the level of detail of the report according to the importance of the analysis results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the importance of the analysis results to the generation AI and cause the generation AI to adjust the level of detail of the report.

[0048] When generating a report, the generation unit can apply different report generation algorithms depending on the category of the analysis results. For example, the generation unit can generate a report by applying a statistical analysis algorithm to numerical data. Also, the generation unit can generate a report by applying a natural language processing algorithm to text data. Furthermore, the generation unit can generate a report by applying a voice analysis algorithm to voice data. In this way, by applying a report generation algorithm according to the category of the analysis results, a more accurate report can be obtained. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the category of the analysis results to the generation AI and cause the generation AI to apply the report generation algorithm.

[0049] When generating a report, the generation unit can improve the accuracy of the report by referring to the user's past report results. The generation unit, for example, adjusts the report generation algorithm based on the user's past report results. The generation unit can also adjust the level of detail of the report by referring to the user's past report results. Furthermore, the generation unit can adjust the priority of the report based on the user's past report results. In this way, the accuracy of the report is improved by referring to the past report results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past report results into the generation AI and cause the generation AI to improve the accuracy of the report.

[0050] When generating a report, the generation unit can determine the priority of the report based on the submission time of the analysis results. For example, the generation unit can preferentially reflect analysis results with an upcoming submission deadline in the report. The generation unit can also postpone analysis results whose submission time has passed. Furthermore, the report generation schedule can be adjusted based on the submission time. This enables efficient report generation by determining the priority of the report based on the submission time of the analysis results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the submission time of the analysis results to the generation AI and have the generation AI determine the priority of the reports.

[0051] The generation unit can adjust the order of reports based on the relevance of the analysis results when generating a report. For example, the generation unit can prioritize highly relevant analysis results in the report. It can also postpone less relevant analysis results. It can also adjust the order of reports based on the relevance of the analysis results. This enables efficient report generation by adjusting the order of reports based on the relevance of the analysis results. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the relevance of the analysis results into the generation AI and have the generation AI adjust the order of the reports.

[0052] When generating a report, the generation unit can adjust the use of technical terms in the report according to the user's level of expertise. For example, if the user has technical expertise, the generation unit can provide a report that uses a lot of technical terms. Alternatively, if the user does not have technical expertise, the generation unit can provide a report in simple language. Furthermore, the report's expression can be adjusted according to the user's level of expertise. This allows the user to be provided with a report that is easy to understand by adjusting the use of technical terms in the report according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's level of expertise into the generation AI and have the generation AI control the use of technical terms in the report.

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

[0054] The acquisition unit can determine the priority of data to be acquired by referring to the user's past data usage history. For example, it can prioritize acquisition of data that the user frequently used in the past. It can also prioritize acquisition of related data based on the type of data the user used in the past. It can also prioritize acquisition of data related to a specific project from the user's past usage history. In this way, by determining the priority of data based on the past data usage history, it is possible to prioritize acquisition of data that is important to the user. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past data usage history into the generation AI and have the generation AI determine the priority of the data.

[0055] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, a detailed analysis can be performed on important data. A simplified analysis can also be performed on less important data. Furthermore, the priority of the analysis can be adjusted according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0056] When generating a report, the generation unit can adjust the level of detail of the report based on the importance of the analysis results. For example, a detailed report can be provided for important analysis results. A simplified report can also be provided for less important analysis results. Furthermore, the priority of the report can be adjusted according to the importance of the analysis results. This enables efficient report generation by adjusting the level of detail of the report according to the importance of the analysis results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the importance of the analysis results to the generation AI and cause the generation AI to adjust the level of detail of the report.

[0057] When acquiring data, the acquisition unit can prioritize acquiring highly relevant data based on the user's geographical location information. For example, if the user is in a specific area, data related to that area can be acquired preferentially. Also, if the user is moving, relevant data can be acquired based on the user's current location. Furthermore, if the user is staying in a specific location, data related to that location can be acquired preferentially. This allows for more appropriate data to be provided by preferentially acquiring highly relevant data based on the user's geographical location information. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information to the generation AI and cause the generation AI to acquire highly relevant data.

[0058] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, a statistical analysis algorithm can be applied to numerical data. A natural language processing algorithm can also be applied to text data. Furthermore, a voice analysis algorithm can be applied to voice data. In this way, by applying an analysis algorithm depending on the data category, more accurate analysis results can be obtained. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and have the generation AI apply the analysis algorithm.

[0059] When generating a report, the generation unit can determine the priority of the report based on the submission time of the analysis results. For example, analysis results with an upcoming submission deadline can be reflected preferentially in the report. Analysis results whose submission time has passed can also be postponed. Furthermore, the report generation schedule can be adjusted based on the submission time. This enables efficient report generation by determining the priority of reports based on the submission time of the analysis results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the submission time of the analysis results to the generation AI and have the generation AI determine the priority of the reports.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The acquisition unit acquires data from cloud storage. Examples of cloud storage include, but are not limited to, Google Drive, Dropbox, and OneDrive. The acquisition unit acquires, for example, PowerPoint, Excel documents, and Zoom audio data stored in the cloud storage. The acquisition unit can also analyze the update frequency of the data in the cloud storage and set an optimal acquisition schedule. For example, if the data is updated frequently, the data can be acquired frequently to maintain the latest information. Step 2: The analysis unit uses the generation AI to analyze the data acquired by the acquisition unit. The analysis is performed based on, for example, the content of PowerPoint slides, numerical data in Excel, and the content of the Zoom audio discussion, but is not limited to these examples. For example, the generation AI performs text analysis of the content of PowerPoint slides and extracts important points. The generation AI can also perform statistical analysis of numerical data in Excel to evaluate the progress of a business plan. Furthermore, the generation AI can convert the content of the Zoom audio discussion into text using voice recognition technology and extract the key points of the discussion. Step 3: The generation unit generates a report based on the data analyzed by the analysis unit. The report may be provided in, for example, PDF format, slide format, text format, or the like, but is not limited to these examples. The generation unit generates a report for strategic discussion based on the analysis results, for example. If a specific project is delayed, the generation unit can also propose the cause and countermeasures. Furthermore, the generation unit can generate a report that includes the progress of the business plan, problems, and improvement measures.

[0062] (Example 2) A system according to an embodiment of the present invention is designed for companies facing challenges in compiling business plan budgets and actual results. This system retrieves data from cloud storage, analyzes it using a generation AI, and generates reports. For example, data stored in cloud storage, such as PowerPoint presentations, Excel documents, and Zoom audio recordings, is input into the system. The generation AI then analyzes this data and compiles the business plan's budget and actual results. For example, it analyzes PowerPoint slides, Excel numerical data, and Zoom audio recordings to identify progress and issues with the business plan. Based on the analysis results, the generation AI then generates a report for strategic discussions. This report includes the business plan's progress, issues, and improvement measures. For example, if a specific project is delayed, the system can propose the cause and countermeasures. This transforms monthly business plan reviews from simple numerical reports to strategic discussions. Users can develop specific strategies based on the reports generated by the generation AI, thereby improving business efficiency and business improvement. Furthermore, because the system uses data stored in cloud storage, there is no need for individual development; by utilizing already accumulated data, the system can be put into practical use sooner. This allows the system to efficiently compile budgets and actual results for business plans and provide reports for strategic discussions. For example, when a user inputs PowerPoint, Excel documents, and Zoom audio saved in cloud storage into the system, the generation AI analyzes this data and compiles budgets and actual results for the business plan. The generation AI then generates a report based on the analysis results and provides it to the user. The user can use this report to hold strategic discussions and come up with measures to improve the business. In this way, utilizing generation AI makes it possible to efficiently compile budgets and actual results for business plans and enable strategic discussions.

[0063] The business plan budget / actual data compilation system according to the embodiment includes an acquisition unit, an analysis unit, and a generation unit. The acquisition unit acquires data from cloud storage. Examples of cloud storage include, but are not limited to, Google Drive, Dropbox, and OneDrive. The acquisition unit acquires, for example, PowerPoint presentations, Excel documents, and Zoom audio data stored in the cloud storage. The acquisition unit can also analyze the update frequency of data in the cloud storage and set an optimal acquisition schedule. For example, if the data is updated frequently, the acquisition unit can acquire the data frequently to maintain the latest information. The analysis unit uses a generation AI to analyze the data acquired by the acquisition unit. The analysis is performed based on, for example, the content of PowerPoint slides, numerical data in Excel, and the content of discussions in Zoom audio, but is not limited to these examples. For example, the generation AI performs text analysis of the content of PowerPoint slides and extracts important points. The generation AI can also perform statistical analysis of numerical data in Excel to evaluate the progress of the business plan. The generation AI can also convert the content of discussions in Zoom audio into text using voice recognition technology and extract the key points of the discussion. The generation unit generates a report based on the data analyzed by the analysis unit. The report may be provided in, for example, a PDF format, a slideshow format, or a text format, but is not limited to these examples. The generation unit generates, for example, a report for strategic discussions based on the analysis results. If a specific project is delayed, the generation unit can also propose causes and countermeasures. Furthermore, the generation unit can generate a report including the progress of the business plan, problems, and improvement measures. As a result, the business plan budget / actual data compilation system according to the embodiment can efficiently compile budget / actual data for the business plan and provide a report for strategic discussions. For example, when a user inputs PowerPoint, Excel documents, and Zoom audio files stored in cloud storage into the system, the generation AI analyzes the data and compiles the budget / actual data for the business plan. The generation AI then generates a report based on the analysis results and provides it to the user. The user can use this report to hold strategic discussions and develop business improvement measures.In this way, by utilizing generative AI, the calculation of forecasts and actual results for business plans can be made more efficient, enabling strategic discussions.

[0064] The acquisition unit can acquire PPT, Excel documents, and Zoom audio data stored in cloud storage. The acquisition unit, for example, acquires PPT files stored in cloud storage. PPT files include slideshow presentations. The acquisition unit can also acquire Excel documents. Excel documents include spreadsheets, graphs, tables, etc. The acquisition unit can also acquire Zoom audio data. Zoom audio includes meeting recordings and audio files. This enables comprehensive data analysis by acquiring a variety of data stored in cloud storage. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data stored in cloud storage into a generation AI and have the generation AI acquire the data.

[0065] The analysis unit can analyze the content of PowerPoint slides, numerical data in Excel, and the content of discussions in Zoom audio to extract progress and issues in the business plan. For example, the analysis unit performs text analysis of the content of PowerPoint slides to extract key points. For example, the generation AI analyzes the text of the slides and extracts keywords and phrases. The analysis unit can also perform statistical analysis of numerical data in Excel to evaluate the progress of the business plan. For example, the generation AI analyzes numerical data and compares budgets with actual results. Furthermore, the analysis unit can convert the content of discussions in Zoom audio into text using voice recognition technology to extract key points from the discussion. For example, the generation AI analyzes audio data and extracts key points from the discussion. This allows for accurate understanding of the progress and issues in the business plan by analyzing various data sources. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the content of PowerPoint slides, numerical data in Excel, and the content of discussions in Zoom audio into the generation AI and have it perform the analysis.

[0066] The generation unit can generate a report for strategic discussions based on the analysis results. The generation unit generates a report for strategic discussions based on the analysis results, for example. The report includes the progress of the business plan, problems, and improvement measures. For example, if a specific project is delayed, the generation AI proposes the cause and countermeasures based on the analysis results. The generation unit can also generate a report that details the progress and problems of the business plan based on the analysis results. For example, the generation AI evaluates the progress of the business plan, identifies problems, and proposes improvement measures based on the analysis results. This enables strategic discussions by generating a report based on the analysis results. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the analysis results to the generation AI and cause the generation AI to generate a report.

[0067] If a specific project is delayed, the generation unit can propose the cause and countermeasures. For example, if a specific project is delayed, the generation unit proposes the cause and countermeasures. The generation AI identifies the cause of the delay based on the analysis results and proposes countermeasures. For example, the generation AI evaluates the progress of the project and identifies the cause of the delay. The generation AI can also propose specific countermeasures based on the cause of the delay. For example, it proposes countermeasures such as resource reallocation and process optimization. This enables rapid problem resolution by proposing the cause of the project delay and countermeasures. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can cause the generation AI to propose the cause of the delay and countermeasures.

[0068] The generation unit can generate a report including the progress, problems, and improvement measures of the business plan. The generation unit generates, for example, a report including the progress, problems, and improvement measures of the business plan. The report details the progress, problems, and improvement measures of the business plan. For example, the generation AI evaluates the progress of the business plan based on the analysis results, identifies problems, and proposes improvement measures. The generation unit can also provide the report in PDF format, slide format, text format, or the like. For example, the generation AI generates a PDF report based on the analysis results and provides it to the user. This makes it easier to grasp the overall picture of the business plan by generating a comprehensive report. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the analysis results to the generation AI and cause the generation AI to generate a report.

[0069] The acquisition unit can estimate the user's emotions and adjust the timing of data acquisition based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition unit can delay data acquisition and wait until the user is relaxed. Furthermore, if the user is relaxed, the acquisition unit can immediately acquire data and quickly start analysis. Furthermore, if the user is in a hurry, the acquisition unit can prioritize data acquisition and quickly proceed with processing. This reduces the burden on the user by adjusting the timing of data acquisition 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, 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 acquisition unit can be performed using, for example, an AI, or without an AI. For example, the acquisition unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of data acquisition.

[0070] The acquisition unit can analyze the update frequency of data in the cloud storage and set an acquisition schedule. The acquisition unit, for example, analyzes the update frequency of data in the cloud storage and sets an optimal acquisition schedule. For example, if the data update frequency is high, the data can be acquired frequently to maintain the latest information. Alternatively, if the data update frequency is low, the data can be acquired periodically to process efficiently. Furthermore, if data updates are concentrated in a specific time period, data can be acquired according to that time period. This enables efficient data management by optimizing the acquisition schedule according to the data update frequency. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the data update frequency to the generation AI and have the generation AI set the acquisition schedule.

[0071] The acquisition unit can automatically adjust the range of data to be acquired based on the user's access authority when acquiring data. For example, if the user has administrator authority, the acquisition unit acquires all data. Also, if the user has general authority, the acquisition unit can acquire only necessary data. Furthermore, the range of data to be acquired can be adjusted according to the user's position. This enables efficient data acquisition while ensuring security by adjusting the data acquisition range according to the user's access authority. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's access authority data to the generation AI and have the generation AI adjust the data acquisition range.

[0072] When acquiring data, the acquisition unit can determine the priority of the data to be acquired by referring to the user's past data usage history. The acquisition unit, for example, prioritizes acquisition of data that the user has frequently used in the past. It can also prioritize acquisition of related data based on the type of data the user has used in the past. Furthermore, it can prioritize acquisition of data related to a specific project from the user's past usage history. Thus, by determining the priority of data based on the past data usage history, data that is important to the user can be acquired preferentially. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past data usage history into a generation AI and have the generation AI determine the priority of the data.

[0073] The acquisition unit can estimate the user's emotions and select the type of data to acquire based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition unit can acquire only important data to reduce the burden on the user. Furthermore, if the user is relaxed, the acquisition unit can acquire detailed data to deepen the analysis. Furthermore, if the user is in a hurry, the acquisition unit can prioritize the acquisition of data that can be processed quickly. This allows the acquisition of necessary data while reducing the burden on the user by selecting the type of data to acquire based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotion data into the generation AI and have the generation AI select the type of data.

[0074] When acquiring data, the acquisition unit can prioritize acquiring highly relevant data based on the user's geographical location information. For example, if the user is in a specific area, the acquisition unit prioritizes acquiring data related to that area. Also, if the user is moving, the acquisition unit can acquire relevant data based on the user's current location. Furthermore, if the user is staying in a specific location, the acquisition unit can prioritize acquiring data related to that location. This allows for more appropriate data to be provided by preferentially acquiring highly relevant data based on the user's geographical location information. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information to the generation AI and cause the generation AI to acquire highly relevant data.

[0075] When acquiring data, the acquisition unit can analyze the user's social media activities and acquire related data. The acquisition unit can, for example, acquire related data based on information shared by the user on social media. The acquisition unit can also acquire related data by analyzing the user's social media activities. Furthermore, the acquisition unit can also acquire related data by referring to the activities of the user's friends on social media. In this way, by acquiring related data based on social media activities, it is possible to provide data that meets the user's needs. Some or all of the above-mentioned processing in the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input the user's social media activity data into the generation AI and cause the generation AI to acquire related data.

[0076] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring data. The acquisition unit can, for example, adjust the type of data to acquire based on feedback provided by the user in the past. The acquisition unit can also adjust the frequency of data acquisition by referring to the user's past feedback. Furthermore, the acquisition unit can adjust the priority of data acquisition based on the user's feedback. This makes it possible to acquire data according to the user's needs by customizing the data acquisition method based on past feedback. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the acquisition method.

[0077] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that are easy to understand by adjusting the presentation method of the analysis 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-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0078] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on important data. Also, the analysis unit can perform a simplified analysis on less important data. Furthermore, the analysis priority can be adjusted according to the importance of the data. This enables efficient data analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0079] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a statistical analysis algorithm to numerical data. Also, it can apply a natural language processing algorithm to text data. Furthermore, it can apply a voice analysis algorithm to voice data. In this way, by applying an analysis algorithm according to the data category, more accurate analysis results can be obtained. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the data category to the generation AI and have the generation AI apply the analysis algorithm.

[0080] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also adjust the level of analysis detail by referring to the user's past analysis results. Furthermore, the analysis unit can adjust the priority of the analysis based on the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. Alternatively, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide the optimal analysis result for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0082] During analysis, the analysis unit can determine the analysis priority based on the time of data submission. For example, the analysis unit prioritizes analysis of data whose submission deadline is approaching. It can also postpone data whose submission deadline has passed. Furthermore, it can adjust the analysis schedule based on the time of submission. This enables efficient data analysis by determining the analysis priority based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data submission to the generation AI and have the generation AI determine the analysis priority.

[0083] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. It can also postpone analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the data. This enables efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0084] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Alternatively, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the method of expressing the analysis results can be adjusted according to the user's level of expertise. This allows the use of technical terms in the analysis to be adjusted according to the user's level of expertise, making it possible to provide analysis results that are easy for the user to understand. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise into the generation AI and have the generation AI use technical terms in the analysis.

[0085] The generation unit can estimate the user's emotions and adjust the presentation style of the report based on the estimated user emotions. For example, if the user is nervous, the generation unit can provide a simple, highly visible report. Alternatively, if the user is relaxed, the generation unit can provide a detailed report. Furthermore, if the user is in a hurry, the generation unit can provide a report that focuses on the main points. By adjusting the presentation style of the report according to the user's emotions, a report that is easy for the user to understand can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the presentation style of the report.

[0086] When generating a report, the generation unit can adjust the level of detail of the report based on the importance of the analysis results. For example, the generation unit can provide a detailed report for important analysis results. Also, the generation unit can provide a simplified report for less important analysis results. Furthermore, the priority of the report can be adjusted according to the importance of the analysis results. This enables efficient report generation by adjusting the level of detail of the report according to the importance of the analysis results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the importance of the analysis results to the generation AI and cause the generation AI to adjust the level of detail of the report.

[0087] When generating a report, the generation unit can apply different report generation algorithms depending on the category of the analysis results. For example, the generation unit can generate a report by applying a statistical analysis algorithm to numerical data. Also, the generation unit can generate a report by applying a natural language processing algorithm to text data. Furthermore, the generation unit can generate a report by applying a voice analysis algorithm to voice data. In this way, by applying a report generation algorithm according to the category of the analysis results, a more accurate report can be obtained. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the category of the analysis results to the generation AI and cause the generation AI to apply the report generation algorithm.

[0088] When generating a report, the generation unit can improve the accuracy of the report by referring to the user's past report results. The generation unit, for example, adjusts the report generation algorithm based on the user's past report results. The generation unit can also adjust the level of detail of the report by referring to the user's past report results. Furthermore, the generation unit can adjust the priority of the report based on the user's past report results. In this way, the accuracy of the report is improved by referring to the past report results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past report results into the generation AI and cause the generation AI to improve the accuracy of the report.

[0089] The generation unit can estimate the user's emotions and adjust the length of the report based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can provide a short, to-the-point report. Alternatively, if the user is relaxed, the generation unit can provide a longer report with detailed explanations. Furthermore, if the user is excited, the generation unit can provide a report with visually stimulating effects. By adjusting the length of the report according to the user's emotions, the optimal report can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the report.

[0090] When generating a report, the generation unit can determine the priority of the report based on the submission time of the analysis results. For example, the generation unit can preferentially reflect analysis results with an upcoming submission deadline in the report. The generation unit can also postpone analysis results whose submission time has passed. Furthermore, the report generation schedule can be adjusted based on the submission time. This enables efficient report generation by determining the priority of the report based on the submission time of the analysis results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the submission time of the analysis results to the generation AI and have the generation AI determine the priority of the reports.

[0091] The generation unit can adjust the order of reports based on the relevance of the analysis results when generating a report. For example, the generation unit can prioritize highly relevant analysis results in the report. It can also postpone less relevant analysis results. It can also adjust the order of reports based on the relevance of the analysis results. This enables efficient report generation by adjusting the order of reports based on the relevance of the analysis results. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the relevance of the analysis results into the generation AI and have the generation AI adjust the order of the reports.

[0092] When generating a report, the generation unit can adjust the use of technical terms in the report according to the user's level of expertise. For example, if the user has technical expertise, the generation unit can provide a report that uses a lot of technical terms. Alternatively, if the user does not have technical expertise, the generation unit can provide a report in simple language. Furthermore, the report's expression can be adjusted according to the user's level of expertise. This allows the user to be provided with a report that is easy to understand by adjusting the use of technical terms in the report according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's level of expertise into the generation AI and have the generation AI control the use of technical terms in the report. === Hard Collateral 1-1 === Each of the multiple elements including the acquisition unit, analysis unit, and generation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the acquisition unit acquires data such as PowerPoint presentations, Excel documents, and Zoom audio stored in cloud storage. The analysis unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the data acquired by the acquisition unit using a generation AI. The generation unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, a report is generated based on the data analyzed by the analysis unit. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, and generation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the acquisition unit acquires data such as PowerPoint presentations, Excel documents, and Zoom audio stored in cloud storage. The analysis unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the data acquired by the acquisition unit using a generation AI. The generation unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, a report is generated based on the data analyzed by the analysis unit. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, and generation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the acquisition unit acquires data such as PowerPoint presentations, Excel documents, and Zoom audio stored in cloud storage. The analysis unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the data acquired by the acquisition unit using a generation AI. The generation unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, a report is generated based on the data analyzed by the analysis unit. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, and generation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the acquisition unit acquires data such as PowerPoint presentations, Excel documents, and Zoom audio stored in cloud storage. The analysis unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the data acquired by the acquisition unit using a generation AI. The generation unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, a report is generated based on the data analyzed by the analysis unit.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The acquisition unit can determine the priority of data to be acquired by referring to the user's past data usage history. For example, it can prioritize acquisition of data that the user frequently used in the past. It can also prioritize acquisition of related data based on the type of data the user used in the past. It can also prioritize acquisition of data related to a specific project from the user's past usage history. In this way, by determining the priority of data based on the past data usage history, it is possible to prioritize acquisition of data that is important to the user. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past data usage history into the generation AI and have the generation AI determine the priority of the data.

[0095] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, a detailed analysis can be performed on important data. A simplified analysis can also be performed on less important data. Furthermore, the priority of the analysis can be adjusted according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0096] When generating a report, the generation unit can adjust the level of detail of the report based on the importance of the analysis results. For example, a detailed report can be provided for important analysis results. A simplified report can also be provided for less important analysis results. Furthermore, the priority of the report can be adjusted according to the importance of the analysis results. This enables efficient report generation by adjusting the level of detail of the report according to the importance of the analysis results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the importance of the analysis results to the generation AI and cause the generation AI to adjust the level of detail of the report.

[0097] The acquisition unit can estimate the user's emotions and adjust the timing of data acquisition based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition unit can delay data acquisition and wait until the user is relaxed. Furthermore, if the user is relaxed, the acquisition unit can immediately acquire data and quickly start analysis. Furthermore, if the user is in a hurry, the acquisition unit can prioritize data acquisition and quickly proceed with processing. This reduces the burden on the user by adjusting the timing of data acquisition 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, 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 acquisition unit can be performed using, for example, an AI, or without an AI. For example, the acquisition unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of data acquisition.

[0098] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, it can provide a simple, highly visible analysis result. If the user is relaxed, it can provide a detailed analysis result. Furthermore, if the user is in a hurry, it can provide a summary analysis result. By adjusting the presentation method of the analysis according to the user's emotions, it is possible to provide an analysis result that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0099] The generation unit can estimate the user's emotions and adjust the presentation style of the report based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible report can be provided. If the user is relaxed, a detailed report can be provided. Furthermore, if the user is in a hurry, a report that focuses on the main points can be provided. By adjusting the presentation style of the report according to the user's emotions, a report that is easy for the user to understand can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation style of the report.

[0100] When acquiring data, the acquisition unit can prioritize acquiring highly relevant data based on the user's geographical location information. For example, if the user is in a specific area, data related to that area can be acquired preferentially. Also, if the user is moving, relevant data can be acquired based on the user's current location. Furthermore, if the user is staying in a specific location, data related to that location can be acquired preferentially. This allows for more appropriate data to be provided by preferentially acquiring highly relevant data based on the user's geographical location information. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information to the generation AI and cause the generation AI to acquire highly relevant data.

[0101] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, a statistical analysis algorithm can be applied to numerical data. A natural language processing algorithm can also be applied to text data. Furthermore, a voice analysis algorithm can be applied to voice data. In this way, by applying an analysis algorithm depending on the data category, more accurate analysis results can be obtained. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and have the generation AI apply the analysis algorithm.

[0102] When generating a report, the generation unit can determine the priority of the report based on the submission time of the analysis results. For example, analysis results with an upcoming submission deadline can be reflected preferentially in the report. Analysis results whose submission time has passed can also be postponed. Furthermore, the report generation schedule can be adjusted based on the submission time. This enables efficient report generation by determining the priority of reports based on the submission time of the analysis results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the submission time of the analysis results to the generation AI and have the generation AI determine the priority of the reports.

[0103] The generation unit can estimate the user's emotions and adjust the length of the report based on the estimated user emotions. For example, if the user is in a hurry, a short, concise report can be provided. If the user is relaxed, a longer report with detailed explanations can be provided. Furthermore, if the user is excited, a report with visually stimulating effects can be provided. By adjusting the length of the report according to the user's emotions, the optimal report can be provided. The emotion estimation is realized 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 generation unit can be performed using, for example, an AI, or without an AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the report.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The acquisition unit acquires data from cloud storage. Examples of cloud storage include, but are not limited to, Google Drive, Dropbox, and OneDrive. The acquisition unit acquires, for example, PowerPoint, Excel documents, and Zoom audio data stored in the cloud storage. The acquisition unit can also analyze the update frequency of the data in the cloud storage and set an optimal acquisition schedule. For example, if the data is updated frequently, the data can be acquired frequently to maintain the latest information. Step 2: The analysis unit uses the generation AI to analyze the data acquired by the acquisition unit. The analysis is performed based on, for example, the content of PowerPoint slides, numerical data in Excel, and the content of the Zoom audio discussion, but is not limited to these examples. For example, the generation AI performs text analysis of the content of PowerPoint slides and extracts important points. The generation AI can also perform statistical analysis of numerical data in Excel to evaluate the progress of a business plan. Furthermore, the generation AI can convert the content of the Zoom audio discussion into text using voice recognition technology and extract the key points of the discussion. Step 3: The generation unit generates a report based on the data analyzed by the analysis unit. The report may be provided in, for example, PDF format, slide format, text format, or the like, but is not limited to these examples. The generation unit generates a report for strategic discussion based on the analysis results, for example. If a specific project is delayed, the generation unit can also propose the cause and countermeasures. Furthermore, the generation unit can generate a report that includes the progress of the business plan, problems, and improvement measures.

[0106] 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.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit that acquires data from cloud storage; an analysis unit that analyzes the data acquired by the acquisition unit; a generation unit that generates a report based on the data analyzed by the analysis unit. A system characterized by:

2. The acquisition unit Retrieve PowerPoint, Excel documents, and Zoom audio data stored in cloud storage The system of claim 1 .

3. The analysis unit Analyze the content of PowerPoint slides, numerical data in Excel, and the content of Zoom audio discussions to identify the progress and problems of the business plan. The system of claim 1 .

4. The generation unit Generate reports to facilitate strategic discussions based on analytical results The system of claim 1 .

5. The generation unit If a specific project is delayed, propose the cause and solutions. The system of claim 1 .

6. The generation unit Generate reports that include progress, issues, and improvement measures for business plans The system of claim 1 .

7. The acquisition unit Estimate user emotions and adjust data acquisition timing based on the estimated user emotions The system of claim 1 .

8. The acquisition unit Analyze the update frequency of data in cloud storage and set up a retrieval schedule The system of claim 1 .

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