system

Abstract

To provide a system for efficiently creating a business report and specifying a topic of an entire organization.SOLUTION: A system includes a reception section, a generation section, and an integration section. The input section inputs a business situation or a problem of progress. The generation section analyzes information inputted by the reception section, and generates a daily report and a business report. The integration section integrates reported contents generated by the generation section and specifies a problem of an entire organization.SELECTED DRAWING: Figure 1

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the process of creating business reports and identifying issues across the organization requires time and effort and is not efficient.

[0005] The system according to the embodiment aims to efficiently create business reports and identify issues across the organization.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, and an aggregation unit. The reception unit inputs business status or ongoing issues. The generation unit analyzes the information input by the reception unit and generates daily reports and business reports. The aggregation unit aggregates the report content generated by the generation unit and identifies issues across the organization. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently create business reports and identify issues across the entire organization. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The business reporting system according to an embodiment of the present invention is a system that incorporates conversational AI, such as a generation AI, to concisely summarize business status and progress challenges. This system reduces the cost of business reporting and supports the skill development and growth of employees. For example, employees can converse with the AI ​​and concisely summarize their business status and progress challenges. When an employee provides the AI ​​with information such as, "Today's work content is XX, and the progress challenge is YY," the AI ​​analyzes this information and automatically generates a daily report or business report. Next, based on the individual information in the conversation, the AI ​​provides specific improvement measures and advice tailored to each employee. For example, if an employee reports, "The XX task didn't go well," the AI ​​provides specific advice such as, "Next time, try the YY method." This supports the skill development and growth of employees. Furthermore, by aggregating the report content summarized by the AI, it is possible to identify issues for the entire organization. For example, if multiple employees report the same issue, the AI ​​identifies that issue as an organization-wide problem and proposes improvement measures. This leads to increased efficiency for the entire organization. This system reduces the cost of business reporting, supports the skill development and growth of employees, and allows for the identification and streamlining of challenges across the entire organization. Thus, the business reporting system reduces the cost of business reporting, supports the skill development and growth of employees, and identifies challenges across the entire organization.

[0029] The business reporting system according to this embodiment comprises a reception unit, a generation unit, and an aggregation unit. The reception unit receives input on business status and progress issues. Business status and progress issues include, but are not limited to, progress status, problems, achievement targets, technical problems, resource shortages, and schedule delays. For example, employees can interact with the AI ​​in the reception unit to input business status and progress issues. The generation unit uses a generation AI to analyze the information entered by the reception unit and generate daily reports and business reports. The generation AI uses, for example, a text generation AI (e.g., LLM) to generate daily reports and business reports based on the entered information. The generation unit can also use the generation AI to extract important parts of text and generate daily reports and business reports. For example, the generation AI has learned from a large amount of text data and has advanced natural language processing capabilities. The generation unit uses the generation AI to automatically generate the content of daily reports and business reports. The aggregation unit aggregates the report content generated by the generation unit and identifies issues for the entire organization. The aggregation unit, for example, stores the generated report content in a database and performs analysis to identify organizational-wide issues. Based on the generated report content, the aggregation unit can identify organizational-wide issues and propose improvement measures. For example, if multiple employees report the same issue, the aggregation unit identifies that issue as an organizational-wide problem and proposes improvement measures. In this way, the business reporting system according to the embodiment can reduce the cost of business reporting, support the skill development and growth of employees, and identify organizational-wide issues. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or not using AI. For example, the aggregation unit can input the generated report content into AI and have AI perform the identification of organizational-wide issues.

[0030] The reception desk inputs information about the status of work and any challenges in progress. This information includes, but is not limited to, progress status, problems, achievement goals, technical issues, resource shortages, and schedule delays. For example, employees can interact with the AI ​​to input information about their work status and challenges. Specifically, employees report work progress and problems to the AI ​​via voice or text input. The AI ​​uses natural language processing technology to understand the input information and store it in the database in an appropriate format. For example, if an employee reports that "Project A is 50% complete, and there is a technical problem with database performance," the AI ​​analyzes this and stores it in the database with tags such as "Project A," "50% progress," "technical problem," and "database performance degradation." Furthermore, the reception desk also has a function to check the consistency and accuracy of the input information. For example, if there are inconsistencies in what the employee has reported, the AI ​​will point them out and prompt correction. The reception desk can also detect changes in progress and the emergence of new problems by comparing current reports with past reports. This allows the reception department to efficiently and accurately collect reports from employees and provide the data necessary for subsequent processing.

[0031] The generation unit uses a generation AI to analyze information entered by the reception unit and generate daily reports and business reports. The generation AI, for example, uses a text generation AI (e.g., LLM) to generate daily reports and business reports based on the entered information. The generation unit can also use the generation AI to extract important parts of text and generate daily reports and business reports. Specifically, the generation AI analyzes the entered information and extracts important keywords and phrases. For example, it generates daily reports and business reports in the appropriate context based on information such as "50% progress," "technical problems," and "database performance degradation." Because the generation AI has learned from a large amount of text data and possesses advanced natural language processing capabilities, it can convert entered information into natural-sounding sentences. For example, it can automatically generate a report such as, "Project A is 50% complete, and currently, a technical problem of database performance degradation has occurred." Furthermore, the generation unit also has a function to customize the format and style of the generated reports. For example, the layout and content of the report can be adjusted according to a specific project or department. This allows the generation unit to automatically generate efficient and high-quality daily reports and business reports, reducing the burden on employees.

[0032] The aggregation unit aggregates the report content generated by the generation unit and identifies organizational-wide issues. For example, the aggregation unit stores the generated report content in a database and performs analysis to identify organizational-wide issues. Specifically, the aggregation unit uses data mining techniques to identify common issues and trends based on the generated report content. For example, if multiple employees report the same technical problem, that problem can be identified as an organizational-wide issue, and countermeasures can be taken as a priority. Based on the generated report content, the aggregation unit can identify organizational-wide issues and propose improvement measures. For example, if multiple employees report the same issue, the aggregation unit can identify that issue as an organizational-wide problem and propose improvement measures. As a result, the business reporting system according to the embodiment can reduce the cost of business reporting, support the skill development and growth of employees, and identify organizational-wide issues. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or not using AI. For example, the aggregation unit can input the generated report content into AI and have AI perform the identification of organizational-wide issues. Specifically, the AI ​​uses natural language processing techniques to analyze the report content and extract common keywords and phrases. This allows the aggregation department to efficiently and accurately identify issues across the entire organization and propose appropriate improvement measures.

[0033] The service provider can provide each employee with specific improvement measures and advice based on the analyzed information. For example, if an employee reports that "task XX didn't go well," the service provider can provide specific improvement measures and advice. For example, the service provider can provide specific advice such as, "Next time, try method YY." The service provider can also provide individual feedback to support the skill development and growth of employees. For example, the service provider can provide specific improvement measures and advice based on the employee's work status and progress challenges. In this way, by providing improvement measures and advice tailored to each employee, it is possible to support the individual skill development and growth. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the employee's work status and progress challenges into AI and have the AI ​​provide specific improvement measures and advice.

[0034] The feedback department can provide feedback to support the skill development and growth of employees. For example, the feedback department can provide specific feedback based on the employee's work status and progress challenges. For example, the feedback department can provide regular evaluations and real-time comments. The feedback department can also suggest specific areas for improvement. For example, the feedback department can suggest specific areas for improvement based on the employee's work status and progress challenges. This can support the skill development and growth of employees. Some or all of the above processes in the feedback department may be performed using AI, or not. For example, the feedback department can input the employee's work status and progress challenges into the AI ​​and have the AI ​​provide specific feedback.

[0035] The reception desk can analyze past business report history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also analyze the user's past reports and suggest the most efficient input method. Furthermore, the reception desk can suggest the optimal input method for a specific time period based on the user's past input history. In this way, the optimal input method can be selected by analyzing past business report history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input past business report history into AI and have the AI ​​select the optimal input method.

[0036] The reception desk can filter the input of work status and ongoing challenges based on the user's current projects and areas of interest. For example, the reception desk can prioritize inputting information related to the project the user is currently working on. It can also filter and input relevant challenges based on the user's areas of interest. Furthermore, the reception desk can suggest appropriate input fields based on the user's current work content. This allows the user to input highly relevant information by filtering based on their current projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the user's current projects and areas of interest into an AI and have the AI ​​perform the filtering.

[0037] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when inputting work status and progress issues. For example, if the user is working in a specific region, the reception desk will prioritize inputting information related to that region. The reception desk can also suggest relevant work status and issues based on the user's current location. Furthermore, if the user is on the move, the reception desk can input information related to their current location in real time. This allows for the priority input of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location information into the AI ​​and have the AI ​​input highly relevant information.

[0038] The reception desk can analyze users' social media activity and input relevant information when inputting work status and progress issues. For example, the reception desk can automatically input work content shared by users on social media. The reception desk can also extract and input relevant issues from users' social media activity. Furthermore, the reception desk can input information related to projects mentioned by users on social media. In this way, relevant information can be input by analyzing users' social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input user social media activity data into AI and have the AI ​​input relevant information.

[0039] The generation unit can adjust the level of detail in daily reports and business reports based on the importance of the tasks. For example, the generation unit can generate detailed reports for tasks of high importance. It can also generate concise reports for tasks of low importance. Furthermore, the generation unit can adjust the content of the reports according to the importance of the tasks. This allows for the generation of reports with appropriate levels of detail by adjusting the level of detail based on the importance of the tasks. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input task importance data into AI and have the AI ​​perform the adjustment of the level of detail in the reports.

[0040] The generation unit can apply different generation algorithms depending on the category of work when generating daily reports and business reports. For example, the generation unit can apply a generation algorithm specifically for project management to reports related to project management. It can also apply a generation algorithm specifically for sales operations to reports related to sales operations. Furthermore, it can apply a generation algorithm specifically for technology development to reports related to technology development. By applying different generation algorithms depending on the category of work, more appropriate reports can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input business category data into AI and have the AI ​​execute the application of the generation algorithm.

[0041] The generation unit can determine the priority of daily reports and business reports based on the submission deadlines for each task. For example, the generation unit can prioritize generating reports for tasks with approaching deadlines. It can also postpone generating reports for tasks with later submission deadlines. Furthermore, the generation unit can adjust the report generation order based on the submission deadlines. This allows reports to be generated in the appropriate order by determining the priority of reports based on the submission deadlines. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input task submission deadline data into AI and have the AI ​​perform the determination of report priorities.

[0042] The generation unit can adjust the order of daily reports and business reports based on the relevance of the tasks. For example, the generation unit can prioritize generating reports for highly relevant tasks. It can also postpone generating reports for less relevant tasks. Furthermore, the generation unit can adjust the order in which reports are generated based on the relevance of the tasks. This allows reports to be generated in an order of relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input task relevance data into AI and have the AI ​​perform the adjustment of the report order.

[0043] The aggregation unit can improve the accuracy of aggregation by considering the interrelationships of business reports during aggregation. For example, the aggregation unit analyzes the interrelationships of business reports and aggregates related reports together. The aggregation unit can also eliminate redundant information by considering the interrelationships of business reports. Furthermore, the aggregation unit can propose the optimal aggregation method based on the interrelationships of business reports. This improves the accuracy of aggregation by considering the interrelationships of business reports. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or without AI. For example, the aggregation unit can input interrelationship data of business reports into AI and have the AI ​​perform the optimization of aggregation accuracy.

[0044] The aggregation unit can perform aggregation while considering the attribute information of the submitters of business reports. For example, the aggregation unit can aggregate report content based on the submitter's job title. It can also aggregate report content based on the submitter's department. Furthermore, the aggregation unit can aggregate report content based on the submitter's years of experience. This allows for more appropriate aggregation by considering the attribute information of the submitters of business reports. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or without AI. For example, the aggregation unit can input the submitter's attribute information data into AI and have the AI ​​perform the aggregation.

[0045] The aggregation unit can aggregate business reports while considering their geographical distribution. For example, it can aggregate business reports that are geographically close together. It can also aggregate related reports while considering their geographical distribution. Furthermore, it can propose the optimal aggregation method based on the geographical distribution. This allows for the aggregation of highly relevant information by considering the geographical distribution of business reports. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or without AI. For example, the aggregation unit can input geographical distribution data into AI and have the AI ​​perform the aggregation.

[0046] The aggregation unit can improve the accuracy of aggregation by referring to relevant documents in business reports during the aggregation process. For example, the aggregation unit can refer to relevant documents in business reports to improve the accuracy of aggregation. The aggregation unit can also eliminate duplicate information based on the relevant documents. Furthermore, the aggregation unit can propose the optimal aggregation method by referring to relevant documents. This improves the accuracy of aggregation by referring to relevant documents in business reports. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or without AI. For example, the aggregation unit can input the relevant document data into AI and have AI perform the optimization of aggregation accuracy.

[0047] The service provider can adjust the level of detail when providing improvement measures and advice based on the importance of the task. For example, the service provider can provide detailed improvement measures and advice for high-importance tasks. Conversely, it can also provide concise improvement measures and advice for low-importance tasks. Furthermore, the service provider can adjust the content of improvement measures and advice according to the importance of the task. This allows for the provision of advice with an appropriate level of detail by adjusting the level of detail based on the importance of the task. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input task importance data into AI and have the AI ​​perform the adjustment of level of detail.

[0048] The service provider can apply different service provision algorithms depending on the category of work when providing improvement measures and advice. For example, the service provider can apply a service provision algorithm specifically for project management to improvement measures and advice related to project management. It can also apply a service provision algorithm specifically for sales operations to improvement measures and advice related to sales operations. Furthermore, it can apply a service provision algorithm specifically for technology development to improvement measures and advice related to technology development. This allows for the provision of more appropriate advice by applying different service provision algorithms depending on the category of work. Some or all of the above-described processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input business category data into AI and have the AI ​​execute the application of the service provision algorithm.

[0049] The service provider can prioritize tasks based on their submission deadlines when providing improvement measures and advice. For example, the service provider can prioritize tasks with approaching deadlines, and postpone providing improvement measures and advice to tasks with later submission deadlines. Furthermore, the service provider can adjust the order in which improvement measures and advice are provided based on submission deadlines. This allows for the provision of advice in an appropriate order by prioritizing tasks based on their submission deadlines. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input task submission deadline data into an AI and have the AI ​​perform the priority determination.

[0050] The service provider can adjust the order in which improvement measures and advice are provided based on the relevance of the tasks. For example, the service provider can prioritize providing improvement measures and advice to highly relevant tasks. Conversely, the service provider can also postpone providing improvement measures and advice to less relevant tasks. Furthermore, the service provider can adjust the order in which improvement measures and advice are provided based on the relevance of the tasks. This allows advice to be provided in the most relevant order by adjusting the order based on the relevance of the tasks. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input task relevance data into AI and have the AI ​​perform the order adjustment.

[0051] The feedback unit can adjust the level of detail based on the importance of the task when providing feedback. For example, the feedback unit can provide detailed feedback for high-importance tasks. It can also provide concise feedback for low-importance tasks. Furthermore, the feedback unit can adjust the content of the feedback according to the importance of the task. This allows for the provision of feedback with an appropriate level of detail by adjusting the level of detail based on the importance of the task. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input task importance data into AI and have the AI ​​perform the adjustment of the level of detail.

[0052] The feedback department can prioritize tasks based on their submission dates when providing feedback. For example, it can prioritize tasks with approaching deadlines, and postpone tasks with later submission dates. Furthermore, the feedback department can adjust the order in which feedback is provided based on submission dates. This allows feedback to be provided in an appropriate order by prioritizing tasks based on their submission dates. Some or all of the above processes in the feedback department may be performed using AI, for example, or not. For example, the feedback department can input task submission date data into an AI and have the AI ​​perform the priority determination.

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

[0054] The reception desk can analyze a user's past work report history and suggest the optimal input method. For example, it can prioritize suggesting input methods that the user has frequently used in the past (voice, text, etc.). The reception desk can also analyze the content of the user's past reports and suggest the most efficient input method. Furthermore, the reception desk can suggest the optimal input method for a specific time period based on the user's past input history. In this way, the optimal input method can be selected by analyzing past work report history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input past work report history into AI and have the AI ​​select the optimal input method.

[0055] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location. For example, if a user is working in a specific region, it will prioritize inputting information related to that region. The reception desk can also suggest relevant work situations and challenges based on the user's current location. Furthermore, if the user is on the move, the reception desk can input information related to their current location in real time. This allows for the priority input of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location information into the AI ​​and have the AI ​​input highly relevant information.

[0056] The generation unit can adjust the level of detail in daily reports and business reports based on the importance of the tasks. For example, it can generate detailed reports for tasks of high importance. The generation unit can also generate concise reports for tasks of low importance. Furthermore, the generation unit can adjust the content of the reports according to the importance of the tasks. This allows for the generation of reports with appropriate levels of detail by adjusting the level of detail based on the importance of the tasks. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input task importance data into AI and have the AI ​​perform the adjustment of the level of detail in the reports.

[0057] The reception desk can filter the input of work status and ongoing challenges based on the user's current projects and areas of interest. For example, it can prioritize inputting information related to the project the user is currently working on. The reception desk can also filter and input relevant challenges based on the user's areas of interest. Furthermore, the reception desk can suggest appropriate input fields based on the user's current work content. This allows the user to input highly relevant information by filtering based on their current projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the user's current projects and areas of interest into an AI and have the AI ​​perform the filtering.

[0058] The service provider can prioritize improvement measures and advice based on the submission deadline of each task. For example, tasks with approaching deadlines will receive priority in improvement measures and advice. Conversely, tasks with later submission deadlines may receive improvement measures and advice later. Furthermore, the service provider can adjust the order in which improvement measures and advice are provided based on the submission deadlines. This allows for the provision of advice in an appropriate order by prioritizing based on the submission deadlines of each task. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input task submission deadline data into an AI and have the AI ​​perform the priority determination.

[0059] The aggregation unit can improve the accuracy of aggregation by considering the interrelationships of business reports during aggregation. For example, it can analyze the interrelationships of business reports and aggregate related reports together. The aggregation unit can also eliminate redundant information by considering the interrelationships of business reports. Furthermore, the aggregation unit can propose the optimal aggregation method based on the interrelationships of business reports. In this way, the accuracy of aggregation is improved by considering the interrelationships of business reports. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or not using AI. For example, the aggregation unit can input interrelationship data of business reports into AI and have the AI ​​perform the optimization of aggregation accuracy.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk inputs the status of work and any ongoing challenges. These include progress, problems, achievement goals, technical issues, resource shortages, and schedule delays. Employees can interact with the AI ​​to input this information. Step 2: The generation unit uses a generation AI to analyze the information entered by the reception unit and generate daily reports and business reports. The generation AI uses a text generation AI (e.g., LLM) to generate daily reports and business reports based on the entered information. The generation unit can also use the generation AI to extract important parts of the text and generate daily reports and business reports. Step 3: The aggregation unit consolidates the reports generated by the generation unit and identifies organizational-wide issues. The aggregation unit stores the generated reports in a database and performs analysis to identify organizational-wide issues. Based on the generated reports, the aggregation unit can identify organizational-wide issues and propose solutions. For example, if multiple employees report the same issue, the aggregation unit can identify that issue as an organizational-wide problem and propose solutions.

[0062] (Example of form 2) The business reporting system according to an embodiment of the present invention is a system that incorporates conversational AI, such as a generation AI, to concisely summarize business status and progress challenges. This system reduces the cost of business reporting and supports the skill development and growth of employees. For example, employees can converse with the AI ​​and concisely summarize their business status and progress challenges. When an employee provides the AI ​​with information such as, "Today's work content is XX, and the progress challenge is YY," the AI ​​analyzes this information and automatically generates a daily report or business report. Next, based on the individual information in the conversation, the AI ​​provides specific improvement measures and advice tailored to each employee. For example, if an employee reports, "The XX task didn't go well," the AI ​​provides specific advice such as, "Next time, try the YY method." This supports the skill development and growth of employees. Furthermore, by aggregating the report content summarized by the AI, it is possible to identify issues for the entire organization. For example, if multiple employees report the same issue, the AI ​​identifies that issue as an organization-wide problem and proposes improvement measures. This leads to increased efficiency for the entire organization. This system reduces the cost of business reporting, supports the skill development and growth of employees, and allows for the identification and streamlining of challenges across the entire organization. Thus, the business reporting system reduces the cost of business reporting, supports the skill development and growth of employees, and identifies challenges across the entire organization.

[0063] The business reporting system according to this embodiment comprises a reception unit, a generation unit, and an aggregation unit. The reception unit receives input on business status and progress issues. Business status and progress issues include, but are not limited to, progress status, problems, achievement targets, technical problems, resource shortages, and schedule delays. For example, employees can interact with the AI ​​in the reception unit to input business status and progress issues. The generation unit uses a generation AI to analyze the information entered by the reception unit and generate daily reports and business reports. The generation AI uses, for example, a text generation AI (e.g., LLM) to generate daily reports and business reports based on the entered information. The generation unit can also use the generation AI to extract important parts of text and generate daily reports and business reports. For example, the generation AI has learned from a large amount of text data and has advanced natural language processing capabilities. The generation unit uses the generation AI to automatically generate the content of daily reports and business reports. The aggregation unit aggregates the report content generated by the generation unit and identifies issues for the entire organization. The aggregation unit, for example, stores the generated report content in a database and performs analysis to identify organizational-wide issues. Based on the generated report content, the aggregation unit can identify organizational-wide issues and propose improvement measures. For example, if multiple employees report the same issue, the aggregation unit identifies that issue as an organizational-wide problem and proposes improvement measures. In this way, the business reporting system according to the embodiment can reduce the cost of business reporting, support the skill development and growth of employees, and identify organizational-wide issues. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or not using AI. For example, the aggregation unit can input the generated report content into AI and have AI perform the identification of organizational-wide issues.

[0064] The reception desk inputs information about the status of work and any challenges in progress. This information includes, but is not limited to, progress status, problems, achievement goals, technical issues, resource shortages, and schedule delays. For example, employees can interact with the AI ​​to input information about their work status and challenges. Specifically, employees report work progress and problems to the AI ​​via voice or text input. The AI ​​uses natural language processing technology to understand the input information and store it in the database in an appropriate format. For example, if an employee reports that "Project A is 50% complete, and there is a technical problem with database performance," the AI ​​analyzes this and stores it in the database with tags such as "Project A," "50% progress," "technical problem," and "database performance degradation." Furthermore, the reception desk also has a function to check the consistency and accuracy of the input information. For example, if there are inconsistencies in what the employee has reported, the AI ​​will point them out and prompt correction. The reception desk can also detect changes in progress and the emergence of new problems by comparing current reports with past reports. This allows the reception department to efficiently and accurately collect reports from employees and provide the data necessary for subsequent processing.

[0065] The generation unit uses a generation AI to analyze information entered by the reception unit and generate daily reports and business reports. The generation AI, for example, uses a text generation AI (e.g., LLM) to generate daily reports and business reports based on the entered information. The generation unit can also use the generation AI to extract important parts of text and generate daily reports and business reports. Specifically, the generation AI analyzes the entered information and extracts important keywords and phrases. For example, it generates daily reports and business reports in the appropriate context based on information such as "50% progress," "technical problems," and "database performance degradation." Because the generation AI has learned from a large amount of text data and possesses advanced natural language processing capabilities, it can convert entered information into natural-sounding sentences. For example, it can automatically generate a report such as, "Project A is 50% complete, and currently, a technical problem of database performance degradation has occurred." Furthermore, the generation unit also has a function to customize the format and style of the generated reports. For example, the layout and content of the report can be adjusted according to a specific project or department. This allows the generation unit to automatically generate efficient and high-quality daily reports and business reports, reducing the burden on employees.

[0066] The aggregation unit aggregates the report content generated by the generation unit and identifies organizational-wide issues. For example, the aggregation unit stores the generated report content in a database and performs analysis to identify organizational-wide issues. Specifically, the aggregation unit uses data mining techniques to identify common issues and trends based on the generated report content. For example, if multiple employees report the same technical problem, that problem can be identified as an organizational-wide issue, and countermeasures can be taken as a priority. Based on the generated report content, the aggregation unit can identify organizational-wide issues and propose improvement measures. For example, if multiple employees report the same issue, the aggregation unit can identify that issue as an organizational-wide problem and propose improvement measures. As a result, the business reporting system according to the embodiment can reduce the cost of business reporting, support the skill development and growth of employees, and identify organizational-wide issues. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or not using AI. For example, the aggregation unit can input the generated report content into AI and have AI perform the identification of organizational-wide issues. Specifically, the AI ​​uses natural language processing techniques to analyze the report content and extract common keywords and phrases. This allows the aggregation department to efficiently and accurately identify issues across the entire organization and propose appropriate improvement measures.

[0067] The service provider can provide each employee with specific improvement measures and advice based on the analyzed information. For example, if an employee reports that "task XX didn't go well," the service provider can provide specific improvement measures and advice. For example, the service provider can provide specific advice such as, "Next time, try method YY." The service provider can also provide individual feedback to support the skill development and growth of employees. For example, the service provider can provide specific improvement measures and advice based on the employee's work status and progress challenges. In this way, by providing improvement measures and advice tailored to each employee, it is possible to support the individual skill development and growth. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the employee's work status and progress challenges into AI and have the AI ​​provide specific improvement measures and advice.

[0068] The feedback department can provide feedback to support the skill development and growth of employees. For example, the feedback department can provide specific feedback based on the employee's work status and progress challenges. For example, the feedback department can provide regular evaluations and real-time comments. The feedback department can also suggest specific areas for improvement. For example, the feedback department can suggest specific areas for improvement based on the employee's work status and progress challenges. This can support the skill development and growth of employees. Some or all of the above processes in the feedback department may be performed using AI, or not. For example, the feedback department can input the employee's work status and progress challenges into the AI ​​and have the AI ​​provide specific feedback.

[0069] The reception desk can estimate the user's emotions and adjust the timing of inputting work status and progress issues based on the estimated emotions. For example, if the user is stressed, the reception desk can delay the input timing to allow the user to input in a relaxed state. Alternatively, if the user is focused, the reception desk can prompt for immediate input to quickly record work status and issues. Furthermore, if the user is tired, the reception desk can adjust the input timing and prompt them to input after a break. This allows for inputting work status and issues at a more appropriate time by adjusting the input timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0070] The reception desk can analyze past business report history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also analyze the user's past reports and suggest the most efficient input method. Furthermore, the reception desk can suggest the optimal input method for a specific time period based on the user's past input history. In this way, the optimal input method can be selected by analyzing past business report history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input past business report history into AI and have the AI ​​select the optimal input method.

[0071] The reception desk can filter the input of work status and ongoing challenges based on the user's current projects and areas of interest. For example, the reception desk can prioritize inputting information related to the project the user is currently working on. It can also filter and input relevant challenges based on the user's areas of interest. Furthermore, the reception desk can suggest appropriate input fields based on the user's current work content. This allows the user to input highly relevant information by filtering based on their current projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the user's current projects and areas of interest into an AI and have the AI ​​perform the filtering.

[0072] The reception desk can estimate the user's emotions and, based on the estimated emotions, determine the priority of tasks and issues to be entered. For example, if the user is stressed, the reception desk may postpone less important tasks and issues. Conversely, if the user is relaxed, the reception desk may prioritize the entry of more important tasks and issues. Furthermore, if the user is in a hurry, the reception desk may prioritize the entry of the most important tasks and issues. This allows for more appropriate order of entry by determining the priority of tasks and issues according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0073] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when inputting work status and progress issues. For example, if the user is working in a specific region, the reception desk will prioritize inputting information related to that region. The reception desk can also suggest relevant work status and issues based on the user's current location. Furthermore, if the user is on the move, the reception desk can input information related to their current location in real time. This allows for the priority input of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location information into the AI ​​and have the AI ​​input highly relevant information.

[0074] The reception desk can analyze users' social media activity and input relevant information when inputting work status and progress issues. For example, the reception desk can automatically input work content shared by users on social media. The reception desk can also extract and input relevant issues from users' social media activity. Furthermore, the reception desk can input information related to projects mentioned by users on social media. In this way, relevant information can be input by analyzing users' social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input user social media activity data into AI and have the AI ​​input relevant information.

[0075] The generation unit can estimate the user's emotions and adjust the expression of daily reports and business reports based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a report using soft language. It can also generate a concise and to-the-point report if the user is stressed. Furthermore, if the user is excited, the generation unit can generate a visually appealing report. This allows for the generation of more appropriate reports by adjusting the expression of daily reports and business reports according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the expression of daily reports and business reports.

[0076] The generation unit can adjust the level of detail in daily reports and business reports based on the importance of the tasks. For example, the generation unit can generate detailed reports for tasks of high importance. It can also generate concise reports for tasks of low importance. Furthermore, the generation unit can adjust the content of the reports according to the importance of the tasks. This allows for the generation of reports with appropriate levels of detail by adjusting the level of detail based on the importance of the tasks. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input task importance data into AI and have the AI ​​perform the adjustment of the level of detail in the reports.

[0077] The generation unit can apply different generation algorithms depending on the category of work when generating daily reports and business reports. For example, the generation unit can apply a generation algorithm specifically for project management to reports related to project management. It can also apply a generation algorithm specifically for sales operations to reports related to sales operations. Furthermore, it can apply a generation algorithm specifically for technology development to reports related to technology development. By applying different generation algorithms depending on the category of work, more appropriate reports can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input business category data into AI and have the AI ​​execute the application of the generation algorithm.

[0078] The generation unit can estimate the user's emotions and adjust the length of daily reports and business reports based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise report. If the user is relaxed, the generation unit can also generate a longer report with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a report with visually stimulating effects. This allows for the generation of reports of more appropriate length by adjusting the length of daily reports and business reports according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of daily reports and business reports.

[0079] The generation unit can determine the priority of daily reports and business reports based on the submission deadlines for each task. For example, the generation unit can prioritize generating reports for tasks with approaching deadlines. It can also postpone generating reports for tasks with later submission deadlines. Furthermore, the generation unit can adjust the report generation order based on the submission deadlines. This allows reports to be generated in the appropriate order by determining the priority of reports based on the submission deadlines. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input task submission deadline data into AI and have the AI ​​perform the determination of report priorities.

[0080] The generation unit can adjust the order of daily reports and business reports based on the relevance of the tasks. For example, the generation unit can prioritize generating reports for highly relevant tasks. It can also postpone generating reports for less relevant tasks. Furthermore, the generation unit can adjust the order in which reports are generated based on the relevance of the tasks. This allows reports to be generated in an order of relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input task relevance data into AI and have the AI ​​perform the adjustment of the report order.

[0081] The aggregation unit can estimate the user's emotions and adjust the aggregation criteria based on the estimated emotions. For example, if the user is relaxed, the aggregation unit can apply aggregation criteria that include detailed information. If the user is stressed, the aggregation unit can also apply aggregation criteria that include concise information. Furthermore, if the user is excited, the aggregation unit can apply visually appealing aggregation criteria. This allows for information to be aggregated using more appropriate criteria by adjusting the aggregation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the aggregation unit may be performed using AI or not using AI. For example, the aggregation unit can input user emotion data into a generative AI and have the generative AI adjust the aggregation criteria.

[0082] The aggregation unit can improve the accuracy of aggregation by considering the interrelationships of business reports during aggregation. For example, the aggregation unit analyzes the interrelationships of business reports and aggregates related reports together. The aggregation unit can also eliminate redundant information by considering the interrelationships of business reports. Furthermore, the aggregation unit can propose the optimal aggregation method based on the interrelationships of business reports. This improves the accuracy of aggregation by considering the interrelationships of business reports. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or without AI. For example, the aggregation unit can input interrelationship data of business reports into AI and have the AI ​​perform the optimization of aggregation accuracy.

[0083] The aggregation unit can perform aggregation while considering the attribute information of the submitters of business reports. For example, the aggregation unit can aggregate report content based on the submitter's job title. It can also aggregate report content based on the submitter's department. Furthermore, the aggregation unit can aggregate report content based on the submitter's years of experience. This allows for more appropriate aggregation by considering the attribute information of the submitters of business reports. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or without AI. For example, the aggregation unit can input the submitter's attribute information data into AI and have the AI ​​perform the aggregation.

[0084] The aggregation unit can estimate the user's emotions and adjust the order in which the aggregation results are displayed based on the estimated emotions. For example, if the user is relaxed, the aggregation unit may prioritize displaying detailed information. It can also prioritize displaying concise information if the user is stressed. Furthermore, if the user is excited, the aggregation unit may prioritize displaying visually appealing information. This allows for the display of information in a more appropriate order by adjusting the order in which the aggregation results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the aggregation unit may be performed using AI, or not. For example, the aggregation unit can input user emotion data into a generative AI and have the generative AI adjust the display order.

[0085] The aggregation unit can aggregate business reports while considering their geographical distribution. For example, it can aggregate business reports that are geographically close together. It can also aggregate related reports while considering their geographical distribution. Furthermore, it can propose the optimal aggregation method based on the geographical distribution. This allows for the aggregation of highly relevant information by considering the geographical distribution of business reports. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or without AI. For example, the aggregation unit can input geographical distribution data into AI and have the AI ​​perform the aggregation.

[0086] The aggregation unit can improve the accuracy of aggregation by referring to relevant documents in business reports during the aggregation process. For example, the aggregation unit can refer to relevant documents in business reports to improve the accuracy of aggregation. The aggregation unit can also eliminate duplicate information based on the relevant documents. Furthermore, the aggregation unit can propose the optimal aggregation method by referring to relevant documents. This improves the accuracy of aggregation by referring to relevant documents in business reports. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or without AI. For example, the aggregation unit can input the relevant document data into AI and have AI perform the optimization of aggregation accuracy.

[0087] The service provider can estimate the user's emotions and adjust the way suggestions and advice are expressed based on those emotions. For example, if the user is relaxed, the service provider can provide advice using gentle language. If the user is stressed, the service provider can also provide concise and to-the-point advice. Furthermore, if the user is agitated, the service provider can provide visually appealing advice. By adjusting the way suggestions and advice are expressed according to the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the way suggestions and advice are expressed.

[0088] The service provider can adjust the level of detail when providing improvement measures and advice based on the importance of the task. For example, the service provider can provide detailed improvement measures and advice for high-importance tasks. Conversely, it can also provide concise improvement measures and advice for low-importance tasks. Furthermore, the service provider can adjust the content of improvement measures and advice according to the importance of the task. This allows for the provision of advice with an appropriate level of detail by adjusting the level of detail based on the importance of the task. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input task importance data into AI and have the AI ​​perform the adjustment of level of detail.

[0089] The service provider can apply different service provision algorithms depending on the category of work when providing improvement measures and advice. For example, the service provider can apply a service provision algorithm specifically for project management to improvement measures and advice related to project management. It can also apply a service provision algorithm specifically for sales operations to improvement measures and advice related to sales operations. Furthermore, it can apply a service provision algorithm specifically for technology development to improvement measures and advice related to technology development. This allows for the provision of more appropriate advice by applying different service provision algorithms depending on the category of work. Some or all of the above-described processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input business category data into AI and have the AI ​​execute the application of the service provision algorithm.

[0090] The service provider can estimate the user's emotions and adjust the length of suggestions and advice based on the estimated emotions. For example, if the user is in a hurry, the service provider can provide short, concise suggestions and advice. If the user is relaxed, the service provider can also provide longer suggestions and advice that include detailed explanations. Furthermore, if the user is excited, the service provider can provide suggestions and advice with visually stimulating effects. By adjusting the length of suggestions and advice according to the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the length of suggestions and advice.

[0091] The service provider can prioritize tasks based on their submission deadlines when providing improvement measures and advice. For example, the service provider can prioritize tasks with approaching deadlines, and postpone providing improvement measures and advice to tasks with later submission deadlines. Furthermore, the service provider can adjust the order in which improvement measures and advice are provided based on submission deadlines. This allows for the provision of advice in an appropriate order by prioritizing tasks based on their submission deadlines. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input task submission deadline data into an AI and have the AI ​​perform the priority determination.

[0092] The service provider can adjust the order in which improvement measures and advice are provided based on the relevance of the tasks. For example, the service provider can prioritize providing improvement measures and advice to highly relevant tasks. Conversely, the service provider can also postpone providing improvement measures and advice to less relevant tasks. Furthermore, the service provider can adjust the order in which improvement measures and advice are provided based on the relevance of the tasks. This allows advice to be provided in the most relevant order by adjusting the order based on the relevance of the tasks. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input task relevance data into AI and have the AI ​​perform the order adjustment.

[0093] The feedback unit can estimate the user's emotions and adjust the way it expresses the feedback based on the estimated emotions. For example, if the user is relaxed, the feedback unit can provide feedback using gentle language. If the user is stressed, the feedback unit can also provide concise and to-the-point feedback. Furthermore, if the user is excited, the feedback unit can provide visually appealing feedback. In this way, by adjusting the way the feedback is expressed according to the user's emotions, more appropriate feedback can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, or not using AI. For example, the feedback unit can input user emotion data into the generative AI and have the generative AI adjust the way the feedback is expressed.

[0094] The feedback unit can adjust the level of detail based on the importance of the task when providing feedback. For example, the feedback unit can provide detailed feedback for high-importance tasks. It can also provide concise feedback for low-importance tasks. Furthermore, the feedback unit can adjust the content of the feedback according to the importance of the task. This allows for the provision of feedback with an appropriate level of detail by adjusting the level of detail based on the importance of the task. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input task importance data into AI and have the AI ​​perform the adjustment of the level of detail.

[0095] The feedback unit can estimate the user's emotions and adjust the length of the feedback based on the estimated emotions. For example, if the user is in a hurry, the feedback unit can provide short, concise feedback. If the user is relaxed, the feedback unit can also provide longer feedback with detailed explanations. Furthermore, if the user is excited, the feedback unit can provide feedback with visually stimulating effects. By adjusting the length of the feedback according to the user's emotions, it is possible to provide feedback of a more appropriate length. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not using AI. For example, the feedback unit can input user emotion data into the generative AI and have the generative AI adjust the length of the feedback.

[0096] The feedback department can prioritize tasks based on their submission dates when providing feedback. For example, it can prioritize tasks with approaching deadlines, and postpone tasks with later submission dates. Furthermore, the feedback department can adjust the order in which feedback is provided based on submission dates. This allows feedback to be provided in an appropriate order by prioritizing tasks based on their submission dates. Some or all of the above processes in the feedback department may be performed using AI, for example, or not. For example, the feedback department can input task submission date data into an AI and have the AI ​​perform the priority determination.

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

[0098] The reception desk can analyze a user's past work report history and suggest the optimal input method. For example, it can prioritize suggesting input methods that the user has frequently used in the past (voice, text, etc.). The reception desk can also analyze the content of the user's past reports and suggest the most efficient input method. Furthermore, the reception desk can suggest the optimal input method for a specific time period based on the user's past input history. In this way, the optimal input method can be selected by analyzing past work report history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input past work report history into AI and have the AI ​​select the optimal input method.

[0099] The service provider can estimate the user's emotions and adjust the way improvement measures and advice are expressed based on the estimated emotions. For example, if the user is relaxed, it can provide advice using gentle language. If the user is stressed, it can provide concise and to-the-point advice. Furthermore, if the user is excited, it can provide visually appealing advice. In this way, by adjusting the way improvement measures and advice are expressed according to the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the way improvement measures and advice are expressed.

[0100] The feedback unit can estimate the user's emotions and adjust the way feedback is expressed based on the estimated emotions. For example, if the user is relaxed, it can provide feedback using gentle language. If the user is stressed, it can provide concise and to-the-point feedback. Furthermore, if the user is excited, it can provide visually appealing feedback. In this way, by adjusting the way feedback is expressed according to the user's emotions, more appropriate feedback can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, or not using AI. For example, the feedback unit can input user emotion data into the generative AI and have the generative AI adjust the way feedback is expressed.

[0101] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location. For example, if a user is working in a specific region, it will prioritize inputting information related to that region. The reception desk can also suggest relevant work situations and challenges based on the user's current location. Furthermore, if the user is on the move, the reception desk can input information related to their current location in real time. This allows for the priority input of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location information into the AI ​​and have the AI ​​input highly relevant information.

[0102] The generation unit can adjust the level of detail in daily reports and business reports based on the importance of the tasks. For example, it can generate detailed reports for tasks of high importance. The generation unit can also generate concise reports for tasks of low importance. Furthermore, the generation unit can adjust the content of the reports according to the importance of the tasks. This allows for the generation of reports with appropriate levels of detail by adjusting the level of detail based on the importance of the tasks. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input task importance data into AI and have the AI ​​perform the adjustment of the level of detail in the reports.

[0103] The aggregation unit can estimate the user's emotions and adjust the aggregation criteria based on the estimated emotions. For example, if the user is relaxed, an aggregation criterion containing detailed information can be applied. If the user is stressed, an aggregation criterion containing concise information can be applied. Furthermore, if the user is excited, a visually appealing aggregation criterion can be applied. This allows information to be aggregated using more appropriate criteria by adjusting the aggregation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the aggregation unit may be performed using AI or not. For example, the aggregation unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the aggregation criteria.

[0104] The reception desk can filter the input of work status and ongoing challenges based on the user's current projects and areas of interest. For example, it can prioritize inputting information related to the project the user is currently working on. The reception desk can also filter and input relevant challenges based on the user's areas of interest. Furthermore, the reception desk can suggest appropriate input fields based on the user's current work content. This allows the user to input highly relevant information by filtering based on their current projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the user's current projects and areas of interest into an AI and have the AI ​​perform the filtering.

[0105] The service provider can prioritize improvement measures and advice based on the submission deadline of each task. For example, tasks with approaching deadlines will receive priority in improvement measures and advice. Conversely, tasks with later submission deadlines may receive improvement measures and advice later. Furthermore, the service provider can adjust the order in which improvement measures and advice are provided based on the submission deadlines. This allows for the provision of advice in an appropriate order by prioritizing based on the submission deadlines of each task. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input task submission deadline data into an AI and have the AI ​​perform the priority determination.

[0106] The aggregation unit can improve the accuracy of aggregation by considering the interrelationships of business reports during aggregation. For example, it can analyze the interrelationships of business reports and aggregate related reports together. The aggregation unit can also eliminate redundant information by considering the interrelationships of business reports. Furthermore, the aggregation unit can propose the optimal aggregation method based on the interrelationships of business reports. In this way, the accuracy of aggregation is improved by considering the interrelationships of business reports. Some or all of the above processing in the aggregation unit may be performed using AI, for example, or not using AI. For example, the aggregation unit can input interrelationship data of business reports into AI and have the AI ​​perform the optimization of aggregation accuracy.

[0107] The generation unit can estimate the user's emotions and adjust the expression of daily reports and business reports based on the estimated emotions. For example, if the user is relaxed, it can generate a report using soft language. If the user is stressed, it can generate a concise and to-the-point report. Furthermore, if the user is excited, it can generate a visually appealing report. In this way, by adjusting the expression of daily reports and business reports according to the user's emotions, more appropriate reports can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the expression of daily reports and business reports.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The reception desk inputs the status of work and any ongoing challenges. These include progress, problems, achievement goals, technical issues, resource shortages, and schedule delays. Employees can interact with the AI ​​to input this information. Step 2: The generation unit uses a generation AI to analyze the information entered by the reception unit and generate daily reports and business reports. The generation AI uses a text generation AI (e.g., LLM) to generate daily reports and business reports based on the entered information. The generation unit can also use the generation AI to extract important parts of the text and generate daily reports and business reports. Step 3: The aggregation unit consolidates the reports generated by the generation unit and identifies organizational-wide issues. The aggregation unit stores the generated reports in a database and performs analysis to identify organizational-wide issues. Based on the generated reports, the aggregation unit can identify organizational-wide issues and propose solutions. For example, if multiple employees report the same issue, the aggregation unit can identify that issue as an organizational-wide problem and propose solutions.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements described above, including the reception unit, generation unit, aggregation unit, provision unit, and feedback unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit allows employees to input their work status and progress issues using the reception device 38 of the smart device 14. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates daily reports and work reports using generation AI. The aggregation unit is implemented by the specific processing unit 290 of the data processing unit 12 and aggregates the generated report content to identify issues for the entire organization. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides specific improvement measures and advice to each employee. The feedback unit is implemented by the control unit 46A of the smart device 14 and provides feedback to support the skill development and growth of employees. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0115] As shown in Figure 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.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the reception unit, generation unit, aggregation unit, provision unit, and feedback unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit allows employees to input their work status and progress issues using the microphone 238 of the smart glasses 214. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates daily reports and work reports using generation AI. The aggregation unit is implemented by the specific processing unit 290 of the data processing unit 12 and aggregates the generated report content to identify issues for the entire organization. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides specific improvement measures and advice to each employee. The feedback unit is implemented by the control unit 46A of the smart glasses 214 and provides feedback to support the skill development and growth of employees. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the reception unit, generation unit, aggregation unit, provision unit, and feedback unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit allows employees to input their work status and progress issues using the microphone 238 of the headset terminal 314. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates daily reports and work reports using generation AI. The aggregation unit is implemented by the specific processing unit 290 of the data processing unit 12 and aggregates the generated report content to identify issues for the entire organization. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides specific improvement measures and advice to each employee. The feedback unit is implemented by the control unit 46A of the headset terminal 314 and provides feedback to support the skill development and growth of employees. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the reception unit, generation unit, aggregation unit, provision unit, and feedback unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit allows employees to input their work status and progress issues using the microphone 238 of the robot 414. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates daily reports and work reports using generation AI. The aggregation unit is implemented by the specific processing unit 290 of the data processing unit 12 and aggregates the generated report content to identify issues for the entire organization. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides specific improvement measures and advice to each employee. The feedback unit is implemented by the control unit 46A of the robot 414 and provides feedback to support the skill development and growth of employees. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0163] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

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

[0173] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) A reception desk where you input the status of work or any issues in progress, The generation unit analyzes the information entered by the reception unit and generates daily reports and business reports, The system comprises: an aggregation unit that aggregates the report content generated by the generation unit and identifies issues for the entire organization; A system characterized by the following features. (Note 2) Based on the analyzed information, the system includes a department that provides specific improvement measures or advice to each employee. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a feedback department to support the skill development and growth of employees. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of inputting work status or progress issues based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is Analyze past business report history and select the appropriate input method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When entering work status or ongoing issues, the system filters based on the user's current projects or areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the work situation and issues to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering work status and progress issues, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering work status and progress challenges, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is The system estimates user sentiment and adjusts the wording of daily reports and business reports based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is When generating daily reports and business reports, adjust the level of detail in the report based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating daily reports and business reports, different generation algorithms are applied depending on the category of the work. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is The system estimates user sentiment and adjusts the length of daily reports and business reports based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating daily reports and business reports, prioritize reports based on the submission deadline for each task. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating daily reports and business reports, adjust the order of reports based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned aggregation unit is We estimate the user's emotions and adjust the aggregation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned aggregation unit is When aggregating, improve the accuracy of the aggregation by considering the interrelationships between business reports. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned aggregation unit is When consolidating data, the attribute information of the person who submitted the business report will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned aggregation unit is It estimates user sentiment and adjusts the order in which aggregated results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned aggregation unit is When aggregating, the geographical distribution of business reports should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned aggregation unit is When aggregating data, we improve the accuracy of the aggregation by referring to relevant documents in the business report. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way improvement suggestions and advice are presented based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing improvement suggestions or advice, adjust the level of detail based on the importance of the task. The system described in Appendix 2, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing improvement measures and advice, different delivery algorithms are applied depending on the category of the work. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of improvement suggestions and advice based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing improvement suggestions or advice, prioritize them based on the submission deadline for the work. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing improvement measures or advice, adjust the order based on the relevance of the tasks. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned feedback unit is It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 29) The aforementioned feedback unit is When providing feedback, adjust the level of detail based on the importance of the task. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned feedback unit is It estimates the user's emotions and adjusts the length of the feedback based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned feedback unit is When providing feedback, prioritize tasks based on their submission timing. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

[Claim 1] A reception unit that inputs work situations or tasks based on the user's emotions estimated using an emotion identification model that estimates the user's emotions, and if it is estimated that the user is feeling stressed, it postpones the input of low-priority work situations or tasks, and inputs work situations or tasks that include at least one of the following: progress, problems, achievement goals, technical problems, resource shortages, and schedule delays. A generation unit generates a daily report or business report by inputting the extracted keywords or phrases into a text generation AI that extracts keywords or phrases based on the work status or the issues in progress entered by the reception unit, and outputs a daily report or business report containing the entered keywords or phrases when the keywords or phrases are entered into the text generation AI. The aggregation unit analyzes the report content shown in the daily report or business report generated by the generation unit using natural language processing technology and identifies issues across the entire organization by extracting common keywords or phrases. A system characterized by comprising the following features.

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