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 technology]

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, the process of creating business reports and identifying issues across the entire organization was laborious, time-consuming, and inefficient.

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

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

[0007] The system according to the embodiment can efficiently create business reports and identify issues across an organization. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The business reporting system according to the embodiment of the present invention is a system incorporating a conversational AI such as a generative AI to concisely summarize the business situation and progress issues. This system reduces the cost of business reporting and supports the skill development and growth of employees. For example, an employee can converse with an AI to concisely summarize the business situation and progress issues. When an employee provides information such as "Today's work content is XX, and the progress issue is △△" to the AI, the AI ​​analyzes this information and automatically generates a daily report or business report. Next, the AI ​​provides specific improvement measures and advice that are suitable for each employee based on the individual information in the conversation. For example, if an employee reports that "XX work did not go well," the AI ​​provides specific advice such as "Next time, try the △△ method." This supports the skill development and growth of employees. Furthermore, by aggregating the report contents compiled by the AI, issues throughout the organization can be identified. For example, if multiple employees report the same issue, the AI ​​identifies the issue as a problem throughout the organization and proposes improvement measures. This improves the efficiency of the entire organization. This system not only reduces the cost of work reporting and supports the skill development and growth of employees, but also identifies issues throughout the organization and improves efficiency. As a result, the work reporting system reduces the cost of work reporting, supports the skill development and growth of employees, and identifies issues throughout the organization.

[0029] The business report system according to the embodiment includes a reception unit, a generation unit, and an aggregation unit. The reception unit inputs the business status and issues in progress. The business status and issues in progress include, for example, progress status, problems, achievement goals, technical problems, resource shortages, schedule delays, etc., but are not limited to such examples. For example, an employee can have a dialogue with an AI in the reception unit and input the business status and issues in progress. The generation unit uses the generation AI to analyze the information input by the reception unit and generate a daily report or a business report. The generation AI uses, for example, a text generation AI (for example, LLM) to generate a daily report or a business report based on the input information. The generation unit can also use the generation AI to extract important parts of a sentence and generate a daily report or a business report. For example, the generation AI has learned a large amount of text data and has advanced natural language processing capabilities. The generation unit uses the generation AI to automatically generate the contents of the daily report or the business report. The aggregation unit aggregates the report contents generated by the generation unit and identifies issues throughout the organization. The aggregation unit, for example, stores the generated report contents in a database and performs analysis to identify issues throughout the organization. The aggregation unit can identify issues throughout the organization based on the generated report contents and propose improvement measures. For example, when multiple employees report the same issue, the aggregation unit identifies the issue as a problem throughout the organization and proposes improvement measures. In this way, the business report system according to the embodiment can reduce the cost of business reports, support the skill development and growth of employees, and identify issues throughout the organization. Some or all of the above-mentioned processing in the aggregation unit may be performed, for example, using AI, or may be performed without using AI. For example, the aggregation unit can input the generated report contents into AI and have AI execute the identification of issues throughout the organization.

[0030] The reception unit inputs the business status and issues in progress. Examples of the business status and issues in progress include, but are not limited to, progress, problems, goals to be achieved, technical problems, lack of resources, and schedule delays. The reception unit allows, for example, an employee to have a dialogue with the AI ​​and input the business status and issues in progress. Specifically, the employee reports the progress and issues of the business to the AI ​​through voice input or text input. The AI ​​uses natural language processing technology to understand the input information and saves it in the database in an appropriate format. For example, if an employee reports that "Project A is at 50% progress, and database performance is declining as a technical issue," the AI ​​analyzes this and saves it in the database with tags such as "Project A," "Progress 50%," "Technical Issue," and "Decline in Database Performance." In addition, the reception unit also has a function to check the consistency and accuracy of the input information. For example, if there is a contradiction in the content reported by the employee, the AI ​​points out the contradiction and encourages the employee to make corrections. In addition, the reception unit can detect fluctuations in progress and the occurrence of new problems by comparing the content of past reports. This enables the reception department to efficiently and accurately collect reports from employees and provide the data necessary for subsequent processing.

[0031] The generation unit uses the generation AI to analyze the information input by the reception unit and generate a daily report or a work report. The generation AI uses, for example, a text generation AI (for example, LLM) to generate a daily report or a work report based on the input information. The generation unit can also use the generation AI to extract important parts of a sentence and generate a daily report or a work report. Specifically, the generation AI analyzes the input information and extracts important keywords and phrases. For example, based on information such as "progress 50%", "technical problem", and "deterioration in database performance", the generation unit generates a daily report or a work report in an appropriate context. The generation AI has learned a large amount of text data and has advanced natural language processing capabilities, so it can convert the input information into natural sentences. For example, it can automatically generate a report such as "The progress of project A is 50%, and currently there is a technical problem of deteriorating database performance". In addition, the generation unit also has a function to customize the format and style of the generated report. 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 work reports, reducing the burden on employees.

[0032] The aggregation unit aggregates the report contents generated by the generation unit and identifies issues throughout the organization. For example, the aggregation unit stores the generated report contents in a database and performs analysis to identify issues throughout the organization. Specifically, the aggregation unit uses data mining technology to identify common issues and trends based on the generated report contents. For example, if multiple employees report the same technical problem, the problem can be identified as an issue throughout the organization and measures can be taken with priority. The aggregation unit can identify issues throughout the organization based on the generated report contents and propose improvement measures. For example, if multiple employees report the same issue, the aggregation unit can identify the issue as an issue throughout the organization and propose improvement measures. In this way, the business reporting system according to the embodiment can reduce the cost of business reporting, support the development and growth of employees' skills, and identify issues throughout the organization. Part or all of the above-mentioned processing in the aggregation unit may be performed, for example, using AI, or may be performed without using AI. For example, the aggregation unit can input the generated report contents into AI and have the AI ​​execute the identification of issues throughout the organization. Specifically, the AI ​​uses natural language processing technology to analyze the report contents and extract common keywords and phrases. This enables the consolidation department to efficiently and accurately identify issues across the entire organization and propose appropriate improvement measures.

[0033] The provision unit can provide each employee with specific improvement measures and advice based on the analyzed information. For example, if an employee reports that "XX work did not go well," the provision unit provides specific improvement measures and advice. For example, the provision unit provides specific advice such as "Please try △△ method next time." The provision unit can also provide individual feedback to support the employee's skill development and growth. For example, the provision unit provides specific improvement measures and advice based on the employee's work situation and progress challenges. This makes it possible to support the individual's skill development and growth by providing improvement measures and advice suitable for each employee. Some or all of the above-mentioned processing in the provision unit may be performed, for example, using AI or may be performed without using AI. For example, the provision unit can input the employee's work situation and progress challenges into AI and have the AI ​​execute the provision of specific improvement measures and advice.

[0034] The feedback unit can provide feedback to support the employee's skill development and growth. The feedback unit provides specific feedback based on, for example, the employee's work situation and progress challenges. For example, the feedback unit can provide regular evaluations and real-time comments. The feedback unit can also present specific improvements. For example, the feedback unit presents specific improvements based on the employee's work situation and progress challenges. This can support the employee's skill development and growth. A part or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the employee's work situation and progress challenges into AI and cause AI to provide specific feedback.

[0035] The reception unit can analyze the past business report history and select the optimal input method. For example, the reception unit preferentially suggests an input method (such as voice or text) that the user has frequently used in the past. The reception unit can also analyze the contents of the user's past reports and suggest the most efficient input method. Furthermore, the reception unit can also suggest the optimal input method for a specific time period from the user's past input history. In this way, the optimal input method can be selected by analyzing the past business report history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the past business report history into AI and cause the AI ​​to select the optimal input method.

[0036] The reception unit can filter the data based on the user's current project or field of interest when inputting the business status or progress issues. The reception unit, for example, preferentially inputs information related to the project the user is currently working on. The reception unit can also filter and input related issues based on the user's field of interest. Furthermore, the reception unit can also suggest appropriate input items based on the user's current work content. This allows highly relevant information to be input by filtering based on the user's current project or field of interest. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's current project or field of interest to AI and have the AI ​​perform filtering.

[0037] The reception unit can preferentially input highly relevant information in consideration of the user's geographical location information when inputting the business status or the task of progress. For example, when the user is working in a specific area, the reception unit preferentially inputs information related to the area. The reception unit can also suggest related business status and tasks based on the user's current location. Furthermore, when the user is moving, the reception unit can also input information related to the current location in real time. In this way, highly relevant information can be preferentially input by considering the user's geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to AI and cause AI to execute input of highly relevant information.

[0038] The reception unit can analyze the user's social media activity and input related information when inputting the business status or progress issues. The reception unit, for example, automatically inputs the business content shared by the user on social media. The reception unit can also extract and input related issues from the user's social media activity. Furthermore, the reception unit can also input information related to a project mentioned by the user on social media. In this way, the related information can be input by analyzing the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to AI and cause the AI ​​to input related information.

[0039] The generation unit can adjust the level of detail of the report based on the importance of the business when generating a daily report or a business report. For example, the generation unit generates a detailed report for a business with high importance. The generation unit can also generate a concise report for a business with low importance. Furthermore, the generation unit can adjust the content of the report according to the importance of the business. As a result, a report with an appropriate level of detail can be generated by adjusting the level of detail of the report based on the importance of the business. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the importance data of the business to an AI and cause the AI ​​to adjust the level of detail of the report.

[0040] The generation unit can apply different generation algorithms depending on the business category when generating a daily report or a business report. For example, the generation unit applies a generation algorithm dedicated to project management to a report on project management. The generation unit can also apply a generation algorithm dedicated to sales business to a report on sales business. Furthermore, the generation unit can apply a generation algorithm dedicated to technology development to a report on technology development. In this way, by applying different generation algorithms depending on the business category, a more appropriate report can be generated. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input business category data to AI and cause AI to apply the generation algorithm.

[0041] The generation unit can determine the priority of the reports based on the submission time of the tasks when generating the daily reports and task reports. For example, the generation unit generates reports with priority for tasks with upcoming deadlines. The generation unit can also postpone the generation of reports for tasks with distant submission times. Furthermore, the generation unit can adjust the order of report generation based on the submission times. In this way, the reports can be generated in an appropriate order by determining the priority of the reports based on the submission times of the tasks. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the submission times of tasks to AI and have the AI ​​execute the determination of the priority of the reports.

[0042] The generation unit can adjust the order of the reports based on the relevance of the tasks when generating daily reports or business reports. For example, the generation unit generates reports with priority for highly relevant tasks. The generation unit can also generate reports for less relevant tasks at a later date. Furthermore, the generation unit can adjust the order of report generation based on the relevance of the tasks. In this way, by adjusting the order of the reports based on the relevance of the tasks, the reports can be generated in the order of high relevance. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input task relevance data to AI and cause AI to adjust the order of the reports.

[0043] The aggregation unit can improve the accuracy of aggregation by considering the interrelationships of the business reports at the time of aggregation. For example, the aggregation unit analyzes the interrelationships of the business reports and aggregates related reports. The aggregation unit can also eliminate duplicate information by considering the interrelationships of the business reports. Furthermore, the aggregation unit can also propose an optimal aggregation method based on the interrelationships of the business reports. This improves the accuracy of aggregation by considering the interrelationships of the business reports. A part or all of the above-mentioned processing in the aggregation unit may be performed using, for example, AI, or may be performed without using AI. For example, the aggregation unit can input interrelationship data of the business reports to AI and cause AI to improve the accuracy of aggregation.

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

[0045] The aggregating unit can perform aggregation while taking into consideration the geographical distribution of the business reports. For example, the aggregating unit aggregates geographically close business reports together. The aggregating unit can also aggregate related reports while taking into consideration the geographical distribution. Furthermore, the aggregating unit can also propose an optimal aggregation method based on the geographical distribution. This makes it possible to aggregate highly related information by taking into consideration the geographical distribution of the business reports. Some or all of the above-mentioned processing in the aggregating unit may be performed, for example, using AI, or may be performed without using AI. For example, the aggregating unit can input geographical distribution data to AI and have the AI ​​execute the aggregation.

[0046] The aggregation unit can improve the accuracy of aggregation by referring to related literature of the business report at the time of aggregation. The aggregation unit, for example, refers to related literature of the business report to improve the accuracy of aggregation. The aggregation unit can also eliminate duplicate information based on the related literature. Furthermore, the aggregation unit can also suggest an optimal aggregation method by referring to the related literature. Thereby, the accuracy of aggregation is improved by referring to the related literature of the business report. A part or all of the above-mentioned processing in the aggregation unit may be performed, for example, using AI, or may be performed without using AI. For example, the aggregation unit can input related literature data to AI and cause AI to execute the improvement of the accuracy of aggregation.

[0047] The providing unit can adjust the level of detail based on the importance of the business when providing the improvement measures and advice. For example, the providing unit provides detailed improvement measures and advice for a business with high importance. The providing unit can also provide concise improvement measures and advice for a business with low importance. Furthermore, the providing unit can adjust the content of the improvement measures and advice according to the importance of the business. As a result, by adjusting the level of detail based on the importance of the business, advice with an appropriate level of detail can be provided. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the importance data of the business to AI and cause the AI ​​to adjust the level of detail.

[0048] The provision unit can apply different provision algorithms depending on the business category when providing improvement measures and advice. For example, the provision unit applies a provision algorithm dedicated to project management to improvement measures and advice related to project management. The provision unit can also apply a provision algorithm dedicated to sales work to improvement measures and advice related to sales work. Furthermore, the provision unit can apply a provision algorithm dedicated to technology development to improvement measures and advice related to technology development. In this way, by applying different provision algorithms depending on the business category, more appropriate advice can be provided. Part or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input business category data into AI and cause AI to apply the provision algorithm.

[0049] The providing unit can determine the priority order based on the submission time of the work when providing the improvement measures and advice. For example, the providing unit provides improvement measures and advice preferentially for work with a deadline approaching. In addition, the providing unit can provide improvement measures and advice for work with a distant submission time at a later date. Furthermore, the providing unit can adjust the order in which the improvement measures and advice are provided based on the submission time. In this way, by determining the priority order based on the submission time of the work, advice can be provided in an appropriate order. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the submission time data of the work to AI and have the AI ​​execute the determination of the priority order.

[0050] The providing unit can adjust the order based on the relevance of the tasks when providing improvement measures and advice. For example, the providing unit provides improvement measures and advice preferentially for tasks with high relevance. Also, the providing unit can provide improvement measures and advice later for tasks with low relevance. Furthermore, the providing unit can adjust the order of providing improvement measures and advice based on the relevance of the tasks. As a result, advice can be provided in the order of high relevance by adjusting the order based on the relevance of the tasks. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input task relevance data to an AI and cause the AI ​​to adjust the order.

[0051] The feedback unit can adjust the level of detail based on the importance of the task when providing the feedback. For example, the feedback unit provides detailed feedback for a task with high importance. The feedback unit can also provide concise feedback for a task with low importance. Furthermore, the feedback unit can adjust the content of the feedback according to the importance of the task. As a result, feedback with an appropriate level of detail can be provided by adjusting the level of detail based on the importance of the task. A part or all of the above-mentioned processing in the feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input the importance data of the task to an AI and cause the AI ​​to adjust the level of detail.

[0052] The feedback unit can determine the priority order based on the submission time of the task when providing the feedback. For example, the feedback unit provides feedback preferentially for tasks with a deadline approaching. Also, the feedback unit can provide feedback later for tasks with a distant submission time. Furthermore, the feedback unit can adjust the order of providing feedback based on the submission time. In this way, by determining the priority order based on the submission time of the task, feedback can be provided in an appropriate order. A part or all of the above-mentioned processing in the feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input data on the submission time of the task to an AI and have the AI ​​execute the determination of the priority order.

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

[0054] The reception unit can analyze the user's past business report history and suggest the optimal input method. For example, the reception unit preferentially suggests an input method (such as voice or text) that the user has frequently used in the past. The reception unit can also analyze the user's past report content and suggest the most efficient input method. Furthermore, the reception unit can also suggest the optimal input method for a specific time period from the user's past input history. In this way, the optimal input method can be selected by analyzing the past business report history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the past business report history into AI and cause the AI ​​to select the optimal input method.

[0055] The reception unit can preferentially input highly relevant information in consideration of the geographical location information of the user. For example, when the user is working in a specific area, information related to the area is preferentially input. The reception unit can also suggest related work situations and issues based on the user's current location. Furthermore, when the user is moving, the reception unit can also input information related to the current location in real time. In this way, highly relevant information can be preferentially input by considering the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to AI and cause the AI ​​to execute input of highly relevant information.

[0056] The generation unit can adjust the level of detail of the report based on the importance of the work when generating a daily report or a work report. For example, a detailed report is generated for a work of high importance. The generation unit can also generate a concise report for a work of low importance. Furthermore, the generation unit can adjust the content of the report according to the importance of the work. In this way, a report with an appropriate level of detail can be generated by adjusting the level of detail of the report based on the importance of the work. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the importance data of the work into AI and cause AI to adjust the level of detail of the report.

[0057] The reception unit can filter the data based on the user's current project or field of interest when inputting the business status or progress issues. For example, the reception unit preferentially inputs information related to the project the user is currently working on. The reception unit can also filter and input related issues based on the user's field of interest. Furthermore, the reception unit can also suggest appropriate input items based on the user's current business content. This allows highly relevant information to be input by filtering based on the user's current project or field of interest. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's current project or field of interest to AI and have the AI ​​perform filtering.

[0058] The providing unit can determine the priority order based on the submission time of the work when providing the improvement measures and advice. For example, for work with an approaching deadline, the providing unit can provide the improvement measures and advice preferentially. Also, for work with a distant submission time, the providing unit can provide the improvement measures and advice later. Furthermore, the providing unit can adjust the order in which the improvement measures and advice are provided based on the submission time. In this way, by determining the priority order based on the submission time of the work, advice can be provided in an appropriate order. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the submission time data of the work to AI and have the AI ​​execute the determination of the priority order.

[0059] The aggregating unit can improve the accuracy of aggregation by considering the interrelationships of the business reports at the time of aggregation. For example, the aggregating unit analyzes the interrelationships of the business reports and aggregates related reports together. The aggregating unit can also eliminate duplicate information by considering the interrelationships of the business reports. Furthermore, the aggregating unit can also propose an optimal aggregation method based on the interrelationships of the business reports. This improves the accuracy of aggregation by considering the interrelationships of the business reports. A part or all of the above-mentioned processing in the aggregating unit may be performed, for example, using AI or may be performed without using AI. For example, the aggregating unit can input interrelationship data of the business reports to AI and cause AI to execute improvement of the accuracy of aggregation.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The reception department inputs the business status and progress issues. Business status and progress issues include progress, problems, goals to be achieved, technical issues, lack of resources, schedule delays, etc. Employees can enter this information by interacting with the AI. Step 2: The generation unit uses a generation AI to analyze the information input by the reception unit and generate a daily report or a work report. The generation AI uses a text generation AI (e.g., LLM) to generate a daily report or a work report based on the input information. The generation unit can also use the generation AI to extract important parts of a sentence and generate a daily report or a work report. Step 3: The aggregation unit aggregates the report contents generated by the generation unit and identifies issues across the entire organization. The aggregation unit stores the generated report contents in a database and analyzes them to identify issues across the entire organization. The aggregation unit can identify issues across the entire organization based on the generated report contents and propose improvement measures. For example, if multiple employees report the same issue, the aggregation unit identifies the issue as a problem across the entire organization and proposes improvement measures.

[0062] (Example 2) The business reporting system according to the embodiment of the present invention is a system incorporating a conversational AI such as a generative AI to concisely summarize the business situation and progress issues. This system reduces the cost of business reporting and supports the skill development and growth of employees. For example, an employee can converse with an AI to concisely summarize the business situation and progress issues. When an employee provides information such as "Today's work content is XX, and the progress issue is △△" to the AI, the AI ​​analyzes this information and automatically generates a daily report or business report. Next, the AI ​​provides specific improvement measures and advice that are suitable for each employee based on the individual information in the conversation. For example, if an employee reports that "XX work did not go well," the AI ​​provides specific advice such as "Next time, try the △△ method." This supports the skill development and growth of employees. Furthermore, by aggregating the report contents compiled by the AI, issues throughout the organization can be identified. For example, if multiple employees report the same issue, the AI ​​identifies the issue as a problem throughout the organization and proposes improvement measures. This improves the efficiency of the entire organization. This system not only reduces the cost of work reporting and supports the skill development and growth of employees, but also identifies issues throughout the organization and improves efficiency. As a result, the work reporting system reduces the cost of work reporting, supports the skill development and growth of employees, and identifies issues throughout the organization.

[0063] The business report system according to the embodiment includes a reception unit, a generation unit, and an aggregation unit. The reception unit inputs the business status and issues in progress. The business status and issues in progress include, for example, progress status, problems, achievement goals, technical problems, resource shortages, schedule delays, etc., but are not limited to such examples. For example, an employee can have a dialogue with an AI in the reception unit and input the business status and issues in progress. The generation unit uses the generation AI to analyze the information input by the reception unit and generate a daily report or a business report. The generation AI uses, for example, a text generation AI (for example, LLM) to generate a daily report or a business report based on the input information. The generation unit can also use the generation AI to extract important parts of a sentence and generate a daily report or a business report. For example, the generation AI has learned a large amount of text data and has advanced natural language processing capabilities. The generation unit uses the generation AI to automatically generate the contents of the daily report or the business report. The aggregation unit aggregates the report contents generated by the generation unit and identifies issues throughout the organization. The aggregation unit, for example, stores the generated report contents in a database and performs analysis to identify issues throughout the organization. The aggregation unit can identify issues throughout the organization based on the generated report contents and propose improvement measures. For example, when multiple employees report the same issue, the aggregation unit identifies the issue as a problem throughout the organization and proposes improvement measures. In this way, the business report system according to the embodiment can reduce the cost of business reports, support the skill development and growth of employees, and identify issues throughout the organization. Some or all of the above-mentioned processing in the aggregation unit may be performed, for example, using AI, or may be performed without using AI. For example, the aggregation unit can input the generated report contents into AI and have AI execute the identification of issues throughout the organization.

[0064] The reception unit inputs the business status and issues in progress. Examples of the business status and issues in progress include, but are not limited to, progress, problems, goals to be achieved, technical problems, lack of resources, and schedule delays. The reception unit allows, for example, an employee to have a dialogue with the AI ​​and input the business status and issues in progress. Specifically, the employee reports the progress and issues of the business to the AI ​​through voice input or text input. The AI ​​uses natural language processing technology to understand the input information and saves it in the database in an appropriate format. For example, if an employee reports that "Project A is at 50% progress, and database performance is declining as a technical issue," the AI ​​analyzes this and saves it in the database with tags such as "Project A," "Progress 50%," "Technical Issue," and "Decline in Database Performance." In addition, the reception unit also has a function to check the consistency and accuracy of the input information. For example, if there is a contradiction in the content reported by the employee, the AI ​​points out the contradiction and encourages the employee to make corrections. In addition, the reception unit can detect fluctuations in progress and the occurrence of new problems by comparing the content of past reports. This enables the reception department to efficiently and accurately collect reports from employees and provide the data necessary for subsequent processing.

[0065] The generation unit uses the generation AI to analyze the information input by the reception unit and generate a daily report or a work report. The generation AI uses, for example, a text generation AI (for example, LLM) to generate a daily report or a work report based on the input information. The generation unit can also use the generation AI to extract important parts of a sentence and generate a daily report or a work report. Specifically, the generation AI analyzes the input information and extracts important keywords and phrases. For example, based on information such as "progress 50%", "technical problem", and "deterioration in database performance", the generation unit generates a daily report or a work report in an appropriate context. The generation AI has learned a large amount of text data and has advanced natural language processing capabilities, so it can convert the input information into natural sentences. For example, it can automatically generate a report such as "The progress of project A is 50%, and currently there is a technical problem of deteriorating database performance". In addition, the generation unit also has a function to customize the format and style of the generated report. 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 work reports, reducing the burden on employees.

[0066] The aggregation unit aggregates the report contents generated by the generation unit and identifies issues throughout the organization. For example, the aggregation unit stores the generated report contents in a database and performs analysis to identify issues throughout the organization. Specifically, the aggregation unit uses data mining technology to identify common issues and trends based on the generated report contents. For example, if multiple employees report the same technical problem, the problem can be identified as an issue throughout the organization and measures can be taken with priority. The aggregation unit can identify issues throughout the organization based on the generated report contents and propose improvement measures. For example, if multiple employees report the same issue, the aggregation unit can identify the issue as an issue throughout the organization and propose improvement measures. In this way, the business reporting system according to the embodiment can reduce the cost of business reporting, support the development and growth of employees' skills, and identify issues throughout the organization. Part or all of the above-mentioned processing in the aggregation unit may be performed, for example, using AI, or may be performed without using AI. For example, the aggregation unit can input the generated report contents into AI and have the AI ​​execute the identification of issues throughout the organization. Specifically, the AI ​​uses natural language processing technology to analyze the report contents and extract common keywords and phrases. This enables the consolidation department to efficiently and accurately identify issues across the entire organization and propose appropriate improvement measures.

[0067] The provision unit can provide each employee with specific improvement measures and advice based on the analyzed information. For example, if an employee reports that "XX work did not go well," the provision unit provides specific improvement measures and advice. For example, the provision unit provides specific advice such as "Please try △△ method next time." The provision unit can also provide individual feedback to support the employee's skill development and growth. For example, the provision unit provides specific improvement measures and advice based on the employee's work situation and progress challenges. This makes it possible to support the individual's skill development and growth by providing improvement measures and advice suitable for each employee. Some or all of the above-mentioned processing in the provision unit may be performed, for example, using AI or may be performed without using AI. For example, the provision unit can input the employee's work situation and progress challenges into AI and have the AI ​​execute the provision of specific improvement measures and advice.

[0068] The feedback unit can provide feedback to support the employee's skill development and growth. The feedback unit provides specific feedback based on, for example, the employee's work situation and progress challenges. For example, the feedback unit can provide regular evaluations and real-time comments. The feedback unit can also present specific improvements. For example, the feedback unit presents specific improvements based on the employee's work situation and progress challenges. This can support the employee's skill development and growth. A part or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the employee's work situation and progress challenges into AI and cause AI to provide specific feedback.

[0069] The reception unit can estimate the user's emotion and adjust the input timing of the work situation and the task in progress based on the estimated user's emotion. For example, when the user is feeling stressed, the reception unit delays the input timing so that the user can input in a relaxed state. In addition, when the user is concentrating, the reception unit can prompt the user to input immediately and quickly record the work situation and the task. Furthermore, when the user is tired, the reception unit can adjust the input timing and prompt the user to input after a break. In this way, the input timing can be adjusted according to the user's emotion, so that the work situation and the task can be input at a more appropriate timing. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0070] The reception unit can analyze the past business report history and select the optimal input method. For example, the reception unit preferentially suggests an input method (such as voice or text) that the user has frequently used in the past. The reception unit can also analyze the contents of the user's past reports and suggest the most efficient input method. Furthermore, the reception unit can also suggest the optimal input method for a specific time period from the user's past input history. In this way, the optimal input method can be selected by analyzing the past business report history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the past business report history into AI and cause the AI ​​to select the optimal input method.

[0071] The reception unit can filter the data based on the user's current project or field of interest when inputting the business status or progress issues. The reception unit, for example, preferentially inputs information related to the project the user is currently working on. The reception unit can also filter and input related issues based on the user's field of interest. Furthermore, the reception unit can also suggest appropriate input items based on the user's current work content. This allows highly relevant information to be input by filtering based on the user's current project or field of interest. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's current project or field of interest to AI and have the AI ​​perform filtering.

[0072] The reception unit can estimate the user's emotion and determine the priority of the work situation and tasks to be input based on the estimated user's emotion. For example, when the user is feeling stressed, the reception unit postpones the work situation and tasks with low importance. In addition, when the user is relaxed, the reception unit can also input the work situation and tasks with high importance with priority. Furthermore, when the user is in a hurry, the reception unit can also input the most important work situation and tasks with priority. In this way, the priority of the work situation and tasks can be determined according to the user's emotion, so that the input can be made in a more appropriate order. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0073] The reception unit can preferentially input highly relevant information in consideration of the user's geographical location information when inputting the business status or the task of progress. For example, when the user is working in a specific area, the reception unit preferentially inputs information related to the area. The reception unit can also suggest related business status and tasks based on the user's current location. Furthermore, when the user is moving, the reception unit can also input information related to the current location in real time. In this way, highly relevant information can be preferentially input by considering the user's geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to AI and cause AI to execute input of highly relevant information.

[0074] The reception unit can analyze the user's social media activity and input related information when inputting the business status or progress issues. The reception unit, for example, automatically inputs the business content shared by the user on social media. The reception unit can also extract and input related issues from the user's social media activity. Furthermore, the reception unit can also input information related to a project mentioned by the user on social media. In this way, the related information can be input by analyzing the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to AI and cause the AI ​​to input related information.

[0075] The generation unit can estimate the user's emotion and adjust the expression method of the daily report or the business report based on the estimated user's emotion. For example, when the user is relaxed, the generation unit generates a report using a soft expression. In addition, when the user is stressed, the generation unit can also generate a concise report that focuses on the main points. Furthermore, when the user is excited, the generation unit can also generate a visually attractive report. This allows a more appropriate report to be generated by adjusting the expression method of the daily report or the business report according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method of the daily report or the business report.

[0076] The generation unit can adjust the level of detail of the report based on the importance of the business when generating a daily report or a business report. For example, the generation unit generates a detailed report for a business with high importance. The generation unit can also generate a concise report for a business with low importance. Furthermore, the generation unit can adjust the content of the report according to the importance of the business. As a result, a report with an appropriate level of detail can be generated by adjusting the level of detail of the report based on the importance of the business. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the importance data of the business to an AI and cause the AI ​​to adjust the level of detail of the report.

[0077] The generation unit can apply different generation algorithms depending on the business category when generating a daily report or a business report. For example, the generation unit applies a generation algorithm dedicated to project management to a report on project management. The generation unit can also apply a generation algorithm dedicated to sales business to a report on sales business. Furthermore, the generation unit can apply a generation algorithm dedicated to technology development to a report on technology development. In this way, by applying different generation algorithms depending on the business category, a more appropriate report can be generated. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input business category data to AI and cause AI to apply the generation algorithm.

[0078] The generation unit can estimate the user's emotion and adjust the length of the daily report or business report based on the estimated user's emotion. For example, when the user is in a hurry, the generation unit generates a short report that is to the point. In addition, when the user is relaxed, the generation unit can also generate a longer report that includes detailed explanations. Furthermore, when the user is excited, the generation unit can also generate a report with a visually stimulating effect. In this way, by adjusting the length of the daily report or business report according to the user's emotion, a report of a more appropriate length can be generated. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the daily report or business report.

[0079] The generation unit can determine the priority of the reports based on the submission time of the tasks when generating the daily reports and task reports. For example, the generation unit generates reports with priority for tasks with upcoming deadlines. The generation unit can also postpone the generation of reports for tasks with distant submission times. Furthermore, the generation unit can adjust the order of report generation based on the submission times. In this way, the reports can be generated in an appropriate order by determining the priority of the reports based on the submission times of the tasks. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the submission times of tasks to AI and have the AI ​​execute the determination of the priority of the reports.

[0080] The generation unit can adjust the order of the reports based on the relevance of the tasks when generating daily reports or business reports. For example, the generation unit generates reports with priority for highly relevant tasks. The generation unit can also generate reports for less relevant tasks at a later date. Furthermore, the generation unit can adjust the order of report generation based on the relevance of the tasks. In this way, by adjusting the order of the reports based on the relevance of the tasks, the reports can be generated in the order of high relevance. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input task relevance data to AI and cause AI to adjust the order of the reports.

[0081] The aggregation unit can estimate the user's emotion and adjust the aggregation criteria based on the estimated user's emotion. For example, when the user is relaxed, the aggregation unit applies aggregation criteria including detailed information. In addition, when the user is stressed, the aggregation unit can also apply aggregation criteria including concise information. In addition, when the user is excited, the aggregation unit can also apply visually attractive aggregation criteria. In this way, by adjusting the aggregation criteria according to the user's emotion, information can be aggregated with more appropriate criteria. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the aggregation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the aggregation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the aggregation criteria.

[0082] The aggregation unit can improve the accuracy of aggregation by considering the interrelationships of the business reports at the time of aggregation. For example, the aggregation unit analyzes the interrelationships of the business reports and aggregates related reports. The aggregation unit can also eliminate duplicate information by considering the interrelationships of the business reports. Furthermore, the aggregation unit can also propose an optimal aggregation method based on the interrelationships of the business reports. This improves the accuracy of aggregation by considering the interrelationships of the business reports. A part or all of the above-mentioned processing in the aggregation unit may be performed using, for example, AI, or may be performed without using AI. For example, the aggregation unit can input interrelationship data of the business reports to AI and cause AI to improve the accuracy of aggregation.

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

[0084] The aggregation unit can estimate the user's emotion and adjust the order of displaying the aggregation results based on the estimated user's emotion. For example, when the user is relaxed, the aggregation unit can preferentially display detailed information. Furthermore, when the user is stressed, the aggregation unit can also preferentially display concise information. Furthermore, when the user is excited, the aggregation unit can also preferentially display visually attractive information. In this way, by adjusting the order of displaying the aggregation results according to the user's emotion, the information can be displayed in a more appropriate order. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the aggregation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the aggregation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display order.

[0085] The aggregating unit can perform aggregation while taking into consideration the geographical distribution of the business reports. For example, the aggregating unit aggregates geographically close business reports together. The aggregating unit can also aggregate related reports while taking into consideration the geographical distribution. Furthermore, the aggregating unit can also propose an optimal aggregation method based on the geographical distribution. This makes it possible to aggregate highly related information by taking into consideration the geographical distribution of the business reports. Some or all of the above-mentioned processing in the aggregating unit may be performed, for example, using AI, or may be performed without using AI. For example, the aggregating unit can input geographical distribution data to AI and have the AI ​​execute the aggregation.

[0086] The aggregation unit can improve the accuracy of aggregation by referring to related literature of the business report at the time of aggregation. The aggregation unit, for example, refers to related literature of the business report to improve the accuracy of aggregation. The aggregation unit can also eliminate duplicate information based on the related literature. Furthermore, the aggregation unit can also suggest an optimal aggregation method by referring to the related literature. Thereby, the accuracy of aggregation is improved by referring to the related literature of the business report. A part or all of the above-mentioned processing in the aggregation unit may be performed, for example, using AI, or may be performed without using AI. For example, the aggregation unit can input related literature data to AI and cause AI to execute the improvement of the accuracy of aggregation.

[0087] The providing unit can estimate the user's emotion and adjust the expression method of the improvement measures and advice based on the estimated user's emotion. For example, when the user is relaxed, the providing unit provides advice using soft expressions. In addition, when the user is stressed, the providing unit can also provide concise and to the point advice. Furthermore, when the user is excited, the providing unit can also provide visually attractive advice. This makes it possible to provide more appropriate advice by adjusting the expression method of the improvement measures and advice according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method of the improvement measures and advice.

[0088] The providing unit can adjust the level of detail based on the importance of the business when providing the improvement measures and advice. For example, the providing unit provides detailed improvement measures and advice for a business with high importance. The providing unit can also provide concise improvement measures and advice for a business with low importance. Furthermore, the providing unit can adjust the content of the improvement measures and advice according to the importance of the business. As a result, by adjusting the level of detail based on the importance of the business, advice with an appropriate level of detail can be provided. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the importance data of the business to AI and cause the AI ​​to adjust the level of detail.

[0089] The provision unit can apply different provision algorithms depending on the business category when providing improvement measures and advice. For example, the provision unit applies a provision algorithm dedicated to project management to improvement measures and advice related to project management. The provision unit can also apply a provision algorithm dedicated to sales work to improvement measures and advice related to sales work. Furthermore, the provision unit can apply a provision algorithm dedicated to technology development to improvement measures and advice related to technology development. In this way, by applying different provision algorithms depending on the business category, more appropriate advice can be provided. Part or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input business category data into AI and cause AI to apply the provision algorithm.

[0090] The providing unit can estimate the user's emotions and adjust the length of the improvement measures and advice based on the estimated user's emotions. For example, when the user is in a hurry, the providing unit provides short and to the point improvement measures and advice. In addition, when the user is relaxed, the providing unit can also provide longer improvement measures and advice including detailed explanations. Furthermore, when the user is excited, the providing unit can also provide improvement measures and advice with visually stimulating effects. In this way, by adjusting the length of the improvement measures and advice according to the user's emotions, advice of a more appropriate length can be provided. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the improvement measures and advice.

[0091] The providing unit can determine the priority order based on the submission time of the work when providing the improvement measures and advice. For example, the providing unit provides improvement measures and advice preferentially for work with a deadline approaching. In addition, the providing unit can provide improvement measures and advice for work with a distant submission time at a later date. Furthermore, the providing unit can adjust the order in which the improvement measures and advice are provided based on the submission time. In this way, by determining the priority order based on the submission time of the work, advice can be provided in an appropriate order. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the submission time data of the work to AI and have the AI ​​execute the determination of the priority order.

[0092] The providing unit can adjust the order based on the relevance of the tasks when providing improvement measures and advice. For example, the providing unit provides improvement measures and advice preferentially for tasks with high relevance. Also, the providing unit can provide improvement measures and advice later for tasks with low relevance. Furthermore, the providing unit can adjust the order of providing improvement measures and advice based on the relevance of the tasks. As a result, advice can be provided in the order of high relevance by adjusting the order based on the relevance of the tasks. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input task relevance data to an AI and cause the AI ​​to adjust the order.

[0093] The feedback unit can estimate the user's emotion and adjust the way of expressing the feedback based on the estimated user's emotion. For example, when the user is relaxed, the feedback unit provides feedback using a soft expression. In addition, when the user is stressed, the feedback unit can also provide concise and to the point feedback. Furthermore, when the user is excited, the feedback unit can also provide visually attractive feedback. This allows more appropriate feedback to be provided by adjusting the way of expressing the feedback according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the way of expressing the feedback.

[0094] The feedback unit can adjust the level of detail based on the importance of the task when providing the feedback. For example, the feedback unit provides detailed feedback for a task with high importance. The feedback unit can also provide concise feedback for a task with low importance. Furthermore, the feedback unit can adjust the content of the feedback according to the importance of the task. As a result, feedback with an appropriate level of detail can be provided by adjusting the level of detail based on the importance of the task. A part or all of the above-mentioned processing in the feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input the importance data of the task to an AI and cause the AI ​​to adjust the level of detail.

[0095] The feedback unit can estimate the user's emotion and adjust the length of the feedback based on the estimated user's emotion. For example, when the user is in a hurry, the feedback unit provides short, to-the-point feedback. Also, when the user is relaxed, the feedback unit can provide longer feedback including detailed explanations. Furthermore, when the user is excited, the feedback unit can provide feedback with visually stimulating effects. This allows feedback of a more appropriate length to be provided by adjusting the length of the feedback according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the feedback.

[0096] The feedback unit can determine the priority order based on the submission time of the task when providing the feedback. For example, the feedback unit provides feedback preferentially for tasks with a deadline approaching. Also, the feedback unit can provide feedback later for tasks with a distant submission time. Furthermore, the feedback unit can adjust the order of providing feedback based on the submission time. In this way, by determining the priority order based on the submission time of the task, feedback can be provided in an appropriate order. A part or all of the above-mentioned processing in the feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input data on the submission time of the task to an AI and have the AI ​​execute the determination of the priority order.

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

[0098] The reception unit can analyze the user's past business report history and suggest the optimal input method. For example, the reception unit preferentially suggests an input method (such as voice or text) that the user has frequently used in the past. The reception unit can also analyze the user's past report content and suggest the most efficient input method. Furthermore, the reception unit can also suggest the optimal input method for a specific time period from the user's past input history. In this way, the optimal input method can be selected by analyzing the past business report history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the past business report history into AI and cause the AI ​​to select the optimal input method.

[0099] The providing unit can estimate the user's emotion and adjust the expression method of the improvement measures and advice based on the estimated user's emotion. For example, when the user is relaxed, the providing unit can provide advice using a soft expression. Also, when the user is stressed, the providing unit can provide concise and to the point advice. Furthermore, when the user is excited, the providing unit can provide visually attractive advice. This allows the providing unit to provide more appropriate advice by adjusting the expression method of the improvement measures and advice according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method of the improvement measures and advice.

[0100] The feedback unit can estimate the user's emotion and adjust the way of expressing the feedback based on the estimated user's emotion. For example, when the user is relaxed, the feedback unit can provide feedback using a soft expression. When the user is stressed, the feedback unit can provide concise and to the point feedback. When the user is excited, the feedback unit can provide visually attractive feedback. This allows the feedback unit to provide more appropriate feedback by adjusting the way of expressing the feedback according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the way of expressing the feedback.

[0101] The reception unit can preferentially input highly relevant information in consideration of the geographical location information of the user. For example, when the user is working in a specific area, information related to the area is preferentially input. The reception unit can also suggest related work situations and issues based on the user's current location. Furthermore, when the user is moving, the reception unit can also input information related to the current location in real time. In this way, highly relevant information can be preferentially input by considering the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to AI and cause the AI ​​to execute input of highly relevant information.

[0102] The generation unit can adjust the level of detail of the report based on the importance of the work when generating a daily report or a work report. For example, a detailed report is generated for a work of high importance. The generation unit can also generate a concise report for a work of low importance. Furthermore, the generation unit can adjust the content of the report according to the importance of the work. In this way, a report with an appropriate level of detail can be generated by adjusting the level of detail of the report based on the importance of the work. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the importance data of the work into AI and cause AI to adjust the level of detail of the report.

[0103] The aggregation unit can estimate the user's emotion and adjust the aggregation criteria based on the estimated user's emotion. For example, if the user is relaxed, aggregation criteria including detailed information can be applied. Also, if the user is stressed, aggregation criteria including concise information can be applied. Furthermore, if the user is excited, visually attractive aggregation criteria can be applied. In this way, by adjusting the aggregation criteria according to the user's emotion, information can be aggregated with more appropriate criteria. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the aggregation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the aggregation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the aggregation criteria.

[0104] The reception unit can filter the data based on the user's current project or field of interest when inputting the business status or progress issues. For example, the reception unit preferentially inputs information related to the project the user is currently working on. The reception unit can also filter and input related issues based on the user's field of interest. Furthermore, the reception unit can also suggest appropriate input items based on the user's current business content. This allows highly relevant information to be input by filtering based on the user's current project or field of interest. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's current project or field of interest to AI and have the AI ​​perform filtering.

[0105] The providing unit can determine the priority order based on the submission time of the work when providing the improvement measures and advice. For example, for work with an approaching deadline, the providing unit can provide the improvement measures and advice preferentially. Also, for work with a distant submission time, the providing unit can provide the improvement measures and advice later. Furthermore, the providing unit can adjust the order in which the improvement measures and advice are provided based on the submission time. In this way, by determining the priority order based on the submission time of the work, advice can be provided in an appropriate order. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the submission time data of the work to AI and have the AI ​​execute the determination of the priority order.

[0106] The aggregating unit can improve the accuracy of aggregation by considering the interrelationships of the business reports at the time of aggregation. For example, the aggregating unit analyzes the interrelationships of the business reports and aggregates related reports together. The aggregating unit can also eliminate duplicate information by considering the interrelationships of the business reports. Furthermore, the aggregating unit can also propose an optimal aggregation method based on the interrelationships of the business reports. This improves the accuracy of aggregation by considering the interrelationships of the business reports. A part or all of the above-mentioned processing in the aggregating unit may be performed, for example, using AI or may be performed without using AI. For example, the aggregating unit can input interrelationship data of the business reports to AI and cause AI to execute improvement of the accuracy of aggregation.

[0107] The generation unit can estimate the user's emotion and adjust the expression method of the daily report or the business report based on the estimated user's emotion. For example, when the user is relaxed, a report using a soft expression can be generated. Also, when the user is stressed, a concise report that focuses on the main points can be generated. Furthermore, when the user is excited, a visually attractive report can be generated. This allows a more appropriate report to be generated by adjusting the expression method of the daily report or the business report according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method of the daily report or the business report.

[0108] The process flow of the second embodiment will be briefly described below.

[0109] Step 1: The reception department inputs the business status and progress issues. Business status and progress issues include progress, problems, goals to be achieved, technical problems, lack of resources, schedule delays, etc. Employees can enter this information by interacting with the AI. Step 2: The generation unit uses a generation AI to analyze the information input by the reception unit and generate a daily report or a work report. The generation AI uses a text generation AI (e.g., LLM) to generate a daily report or a work report based on the input information. The generation unit can also use the generation AI to extract important parts of a sentence and generate a daily report or a work report. Step 3: The aggregation unit aggregates the report contents generated by the generation unit and identifies issues across the entire organization. The aggregation unit stores the generated report contents in a database and analyzes them to identify issues across the entire organization. The aggregation unit can identify issues across the entire organization based on the generated report contents and propose improvement measures. For example, if multiple employees report the same issue, the aggregation unit identifies the issue as a problem across the entire organization and proposes improvement measures.

[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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0112] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements including the above-mentioned reception unit, generation unit, aggregation unit, provision unit, and feedback unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit allows an employee to input the business situation and progress issues using the reception device 38 of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates a daily report or business report using a generation AI. The aggregation unit is realized by the specific processing unit 290 of the data processing device 12, and aggregates the generated report contents and identifies issues in the entire organization. The provision unit is realized by the specific processing unit 290 of the data processing device 12, and provides specific improvement measures and advice to each employee. The feedback unit is realized 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 above-mentioned example, and various changes are possible.

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

[0115] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0121] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0124] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements including the above-mentioned reception unit, generation unit, aggregation unit, provision unit, and feedback unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit allows an employee to input the work situation and progress issues using the microphone 238 of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates a daily report or a work report using a generation AI. The aggregation unit is realized by the specific processing unit 290 of the data processing device 12, and aggregates the generated report contents and identifies issues in the entire organization. The provision unit is realized by the specific processing unit 290 of the data processing device 12, and provides specific improvement measures and advice to each employee. The feedback unit is realized by the control unit 46A of the smart glasses 214, and provides feedback to support the skill development and growth of employees. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

[0132] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0137] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0140] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

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

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements including the above-mentioned reception unit, generation unit, aggregation unit, provision unit, and feedback unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit allows an employee to input the work situation and progress issues using the microphone 238 of the headset type terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates a daily report or a work report using a generation AI. The aggregation unit is realized by the specific processing unit 290 of the data processing device 12, and aggregates the generated report contents and identifies issues in the entire organization. The provision unit is realized by the specific processing unit 290 of the data processing device 12, and provides specific improvement measures and advice to each employee. The feedback unit is realized by the control unit 46A of the headset type terminal 314, and provides feedback to support the skill development and growth of employees. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

[0148] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0152] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0153] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0154] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0155] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0157] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0158] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[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 control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] Each of the multiple elements including the above-mentioned reception unit, generation unit, aggregation unit, provision unit, and feedback unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit allows an employee to input the work situation and progress issues using the microphone 238 of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates a daily report or a work report using a generation AI. The aggregation unit is realized by the specific processing unit 290 of the data processing device 12, and aggregates the generated report contents and identifies issues in the entire organization. The provision unit is realized by the specific processing unit 290 of the data processing device 12, and provides specific improvement measures and advice to each employee. The feedback unit is realized 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 above-mentioned example, and various changes are possible.

[0163] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0164] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0165] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0166] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0167] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0168] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this 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 the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

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

[0172] In addition, 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 upon request from the data processing device 12.

[0173] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0174] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0175] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0176] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0177] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0178] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0179] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0180] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0181] (Appendix 1) A reception section for inputting business status or issues in progress; a generation unit that analyzes the information input by the reception unit and generates a daily report or an operation report; a consolidation unit that consolidates the report contents generated by the generation unit and identifies issues in the entire organization. A system characterized by: (Appendix 2) Equipping the department with the ability to provide specific improvement measures or advice to each employee based on the analyzed information 2. The system of claim 1, (Appendix 3) Provide a feedback department to support employee skill development and growth 2. The system of claim 1, (Appendix 4) The reception unit is To estimate a user's emotion and adjust input timing of a task on a work status or progress based on the estimated user's emotion. 2. The system of claim 1, (Appendix 5) The reception unit is Analyze past business report history and select the appropriate input method 2. The system of claim 1, (Appendix 6) The reception unit is Filter based on the user's current projects or areas of interest when entering a status or ongoing issue 2. The system of claim 1, (Appendix 7) The reception unit is Infer the user's emotions and determine the priority of the work status and tasks to be entered based on the estimated user emotions 2. The system of claim 1, (Appendix 8) The reception unit is When entering work status or progress issues, the system takes into account the user's geographic location information and prioritizes entering information that is more relevant. 2. The system of claim 1, (Appendix 9) The reception unit is Analyze users' social media activity and input relevant information when entering work status or progress issues 2. The system of claim 1, (Appendix 10) The generation unit is Estimate the user's emotions and adjust the presentation of daily and business reports based on the estimated user emotions 2. The system of claim 1, (Appendix 11) The generation unit is When generating daily or business reports, adjust the level of detail in the report based on the importance of the work 2. The system of claim 1, (Appendix 12) The generation unit is When generating daily reports or business reports, different generation algorithms are applied depending on the business category. 2. The system of claim 1, (Appendix 13) The generation unit is Estimate the user's emotions and adjust the length of daily and business reports based on the estimated user emotions 2. The system of claim 1, (Appendix 14) The generation unit is When generating daily and task reports, prioritize reports based on when tasks were submitted 2. The system of claim 1, (Appendix 15) The generation unit is When generating daily or business reports, adjust the order of reports based on business relevance 2. The system of claim 1, (Appendix 16) The collecting unit includes: Inferring user sentiment and adjusting aggregation criteria based on the estimated user sentiment 2. The system of claim 1, (Appendix 17) The collecting unit includes: When consolidating data, improve the accuracy of the consolidation by considering the interrelationships between business reports. 2. The system of claim 1, (Appendix 18) The collecting unit includes: When aggregating business reports, the attribute information of the person submitting the report is taken into consideration. 2. The system of claim 1, (Appendix 19) The collecting unit includes: Infer user sentiment and adjust the order in which aggregate results are displayed based on the estimated user sentiment. 2. The system of claim 1, (Appendix 20) The collecting unit includes: When consolidating, the geographical distribution of business reports is taken into consideration. 2. The system of claim 1, (Appendix 21) The collecting unit includes: When consolidating data, improve the accuracy of the data by referring to related literature on business reports. 2. The system of claim 1, (Appendix 22) The providing unit is Infer the user's emotions and adjust the way in which improvements and advice are presented based on the inferred user emotions 3. The system of claim 2, (Appendix 23) The providing unit is When providing solutions and advice, adjust the level of detail based on the importance of the business 3. The system of claim 2, (Appendix 24) The providing unit is When providing remedial measures and advice, different delivery algorithms are applied depending on the business category. 3. The system of claim 2, (Appendix 25) The providing unit is Infer user emotions and adjust the length of remedial measures and advice based on the estimated user emotions 3. The system of claim 2, (Appendix 26) The providing unit is Prioritize remediation and advice based on time of submission 3. The system of claim 2, (Appendix 27) The providing unit is Order improvements and advice based on business relevance 3. The system of claim 2, (Appendix 28) The feedback unit is Infer user emotions and adjust feedback presentation based on the estimated user emotions 4. The system of claim 3. (Appendix 29) The feedback unit is When providing feedback, adjust the level of detail based on the importance of the work 4. The system of claim 3. (Appendix 30) The feedback unit is Estimate the user's emotions and adjust the length of the feedback based on the estimated user emotions. 4. The system of claim 3. (Appendix 31) The feedback unit is When providing feedback, prioritize based on when work is submitted 4. The system of claim 3. [Explanation of symbols]

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

Claims

1. A reception section for inputting business status or progress issues; a generation unit that analyzes the information input by the reception unit and generates a daily report or an operation report; a consolidation unit that consolidates the report contents generated by the generation unit and identifies issues in the entire organization. A system characterized by:

2. Equipping the department with the ability to provide specific improvement measures or advice to each employee based on the analyzed information 2. The system of claim 1 .

3. Have a feedback department to support employees in developing and growing their skills 2. The system of claim 1 .

4. The reception unit is To estimate a user's emotion and adjust input timing of a task on a work status or progress based on the estimated user's emotion.

2. The system of claim 1 .

5. The reception unit is Analyze past business report history and select the appropriate input method 2. The system of claim 1 .

6. The reception unit is Filter based on the user's current projects or areas of interest when entering a status or ongoing issue 2. The system of claim 1 .

7. The reception unit is Infer the user's emotions and determine the priority of the work status and tasks to be entered based on the estimated user emotions 2. The system of claim 1 .

8. The reception unit is When entering work status or progress issues, the system takes into account the user's geographic location information and prioritizes entering information that is more relevant.

2. The system of claim 1 .

9. The reception unit is Analyze users' social media activity and input relevant information when entering work status or progress issues 2. The system of claim 1 .

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