System

Abstract

To provide a system for automatically collecting and analyzing histories of projects and business, and efficiently creating a report.SOLUTION: A system comprises: a collection part; an analysis part; and an editing part. The collection part collects data in at least one of a history of e-mail, a history of a chat, a history of files and logs in a company. The analysis part analyzes the data collected by the collection part, and identifies when and who did what. The editing part creates a report according to a request on the basis of the information identified by the analysis part.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, manually compiling project or business histories was time-consuming, labor-intensive, and inefficient.

[0005] The system according to the embodiment aims to automatically collect and analyze project and business histories and efficiently generate reports. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a compilation unit. The collection unit collects at least one of data from in-house emails or chats, and file histories or logs. The analysis unit analyzes the data collected by the collection unit to identify who did what and when. The compilation unit generates a report in response to a request based on the information identified by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can automatically collect and analyze project and business histories and efficiently generate reports. [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 the 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 project history creation system according to the embodiment of the present invention is a system that automatically collects and analyzes data such as internal emails, chats, file history and logs, and compiles the data upon request. This system is effective when a project manager (PM) or person in charge does not have time to summarize what happened and when during their busy days. The project history creation system can record who did what and when from internal emails, chats, file history and logs, and compile and show the data upon request. For example, the project history creation system collects data such as internal emails, chats, file history and logs. Next, the collected data is analyzed to identify who did what and when. For example, it is recorded that in a certain project, a specific person in charge completed a specific task on a specific day. Furthermore, based on the analyzed data, compilation is performed upon request. For example, a report showing the progress of the project and a document summarizing the activity history during a specific period are generated. This allows the history of the project to be easily reviewed and can be used as a reference material for the future. The project history creation system automatically creates the project's official history, reducing the burden on PMs and people in charge and allowing efficient project management. In addition, accurately recording the project history also helps with later verification and improvement. This allows the project history creation system to efficiently record and manage the project history.

[0029] The project history creation system according to the embodiment includes a collection unit, an analysis unit, and a compilation unit. The collection unit collects data such as in-house emails and chats, file history and logs, etc. The collection unit acquires data from, for example, an in-house mail server, chat system, file server, etc. For example, the collection unit can acquire email data from a mail server, a chat log from a chat system, and a file history from a file server. The analysis unit analyzes the data collected by the collection unit to identify who did what and when. The analysis unit analyzes the data using, for example, natural language processing technology. For example, the analysis unit can analyze the contents of emails and chats using morphological analysis, analyze the structure of a sentence using grammatical analysis, and analyze the meaning of a sentence using semantic analysis. The compilation unit generates a report in response to a request based on the information identified by the analysis unit. The compilation unit generates a report in, for example, PDF or Excel format in response to a request. For example, the compilation unit can generate a report showing the progress of a project or a document summarizing an activity history for a specific period of time. As a result, the project official history creation system according to the embodiment can efficiently record and manage the history of the project. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input email data acquired from a mail server to the generation AI, and cause the generation AI to execute analysis of the email data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input data collected by the collection unit to the generation AI, and cause the generation AI to execute analysis of the data. A part or all of the above-mentioned processing in the compilation unit may be performed, for example, using AI, or may be performed without using AI. For example, the compilation unit can input information specified by the analysis unit to the generation AI, and cause the generation AI to execute generation of a report in response to a request. As a result, the project official history creation system according to the embodiment can efficiently record and manage the history of the project.

[0030] The collection department collects data such as in-house emails, chats, and file history and logs. Specifically, data is acquired from in-house email servers, chat systems, file servers, etc. For example, the collection department can acquire email data from email servers, chat logs from chat systems, and file history from file servers. These data are important for recording the progress of projects and the activity history of each member in detail. The collection department can centrally manage these data and link with other systems and departments as necessary. For example, the collected data is stored on a cloud server so that the analysis department and editing department can access it. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. Furthermore, the collection department can automate data collection using AI. For example, email data acquired from email servers can be input into the generation AI, and the generation AI can be made to analyze the email data. This allows the collection department to efficiently and effectively collect data and improve the performance of the entire system.

[0031] The analysis unit analyzes the data collected by the collection unit and identifies who did what and when. The analysis uses natural language processing technology to analyze the data. Specifically, it can analyze the contents of emails and chats using morphological analysis, analyze the structure of sentences using grammatical analysis, and analyze the meaning of sentences using semantic analysis. For example, the analysis unit identifies the sender, recipient, sending date and time, and content of an email, and identifies the speaker, comment content, and comment date and time from the chat log. In addition, it can identify the creator, editor, edit date and time, and change content of a file from the file history. This allows the analysis unit to grasp the progress of the project and the activity history of each member in detail. Furthermore, the analysis unit can automate the analysis of data using AI. For example, the data collected by the collection unit can be input into the generation AI, and the generation AI can be made to analyze the data. The generation AI can use natural language processing technology to quickly and accurately analyze large amounts of data and identify the progress of the project and the activity history of each member. This allows the analysis unit to analyze data efficiently and effectively, improving the performance of the entire system.

[0032] The editing department generates a report according to the request based on the information identified by the analysis department. Specifically, the report is generated in PDF or Excel format according to the request. For example, the editing department can generate a report showing the progress of the project or a document summarizing the activity history for a specific period. This allows the progress of the project and the activity history of each member to be recorded and managed in detail. Furthermore, the editing department can automate the generation of reports using AI. For example, the information identified by the analysis department can be input into the generation AI, and the generation AI can be made to generate a report according to the request. The generation AI can record the progress of the project and the activity history of each member in detail based on the collected data and the analysis results, and quickly and accurately generate a report according to the request. This allows the editing department to generate reports efficiently and effectively, improving the performance of the entire system.

[0033] The collection unit includes an acquisition unit that acquires data from at least one of an in-house mail server, a chat system, and a file server. The collection unit, for example, acquires mail data from an in-house mail server. For example, the collection unit can acquire mail data from the mail server and collect mail sending and receiving history. The collection unit can also acquire chat logs from a chat system. For example, the collection unit can acquire chat logs from the chat system and collect chat contents. The collection unit can also acquire file history from a file server. For example, the collection unit can acquire file history from the file server and collect file change history. This allows the collection unit to acquire data from various in-house data sources, thereby enabling comprehensive data collection. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input mail data acquired from the mail server to the generation AI and cause the generation AI to execute analysis of the mail data.

[0034] The analysis unit can analyze data using natural language processing technology. The analysis unit analyzes data using, for example, natural language processing technology. For example, the analysis unit can analyze the contents of emails and chats using morphological analysis, analyze the structure of a sentence using grammatical analysis, and analyze the meaning of a sentence using semantic analysis. This allows the analysis unit to improve the accuracy of data analysis. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data collected by the collection unit to the generation AI and have the generation AI execute the analysis of the data.

[0035] The compilation unit can generate a report in PDF or Excel format in response to a request. The compilation unit, for example, generates a report in PDF format in response to a request. For example, the compilation unit can generate a report using a PDF generation library and save it in PDF format. The compilation unit can also generate a report in Excel format in response to a request. For example, the compilation unit can generate a report using an Excel generation library and save it in Excel format. As a result, the compilation unit can provide information according to the needs of the user by generating a report in a format according to the request. A part or all of the above-mentioned processing in the compilation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the compilation unit can input information identified by the analysis unit to the generation AI and cause the generation AI to generate a report according to the request.

[0036] The analysis unit can record that a specific person in charge has completed a specific task on a specific day. The analysis unit records, for example, that a specific person in charge has completed a specific task on a specific day. For example, the analysis unit can record that a specific person in charge has completed a specific task on a specific day based on the identification information of the person in charge. This allows the analysis unit to accurately record the task completion status of the person in charge. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the data collected by the collection unit to the generation AI and cause the generation AI to execute recording of the task completion status of the person in charge.

[0037] The compilation unit can generate a report showing the progress of the project or a document summarizing the activity history in a specific period. The compilation unit, for example, generates a report showing the progress of the project. For example, the compilation unit can generate a report showing the progress of the project and record the progress of the project in detail. The compilation unit can also generate a document summarizing the activity history in a specific period. For example, the compilation unit can generate a document summarizing the activity history in a specific period and record the activity history in detail. As a result, the compilation unit can record the progress of the project and the activity history in detail, which is useful for later verification and improvement. A part or all of the above-mentioned processing in the compilation unit may be performed using, for example, AI, or may be performed without using AI. For example, the compilation unit can input the information identified by the analysis unit into the generation AI and cause the generation AI to execute the generation of a report showing the progress of the project or a document summarizing the activity history in a specific period.

[0038] The collection unit can determine the priority of collection based on the importance of data at the time of collection. For example, the collection unit can determine the priority of collection based on the importance of data at the time of collection. For example, the collection unit can preferentially collect data directly related to the progress of the project. In addition, the collection unit can preferentially collect minutes and decisions of important meetings. In addition, the collection unit can preferentially collect data related to tasks with high urgency. As a result, the collection unit can prioritize collecting important data, thereby enabling efficient data management. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can determine the priority of collection using an AI model that evaluates the importance of data.

[0039] The collection unit can apply different collection algorithms depending on the type of data at the time of collection. For example, the collection unit can apply different collection algorithms depending on the type of data at the time of collection. For example, the collection unit can apply a keyword search algorithm to email data to extract important information. In addition, the collection unit can apply an algorithm that analyzes the flow of conversation to chat data to collect highly relevant information. In addition, the collection unit can apply an algorithm that tracks the change history of a file to the file history to collect important changes. Thereby, the collection unit can improve the collection accuracy by applying a collection algorithm depending on the type of data. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can improve the collection accuracy by using an AI model that executes a collection algorithm depending on the type of data.

[0040] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user at the time of collection. For example, the collection unit preferentially collects highly relevant data in consideration of the geographical location information of the user at the time of collection. For example, when the user is in a specific office, the collection unit can preferentially collect data related to the office. In addition, when the user is on a business trip, the collection unit can preferentially collect data related to the business trip destination. In addition, when the user is working remotely, the collection unit can preferentially collect data related to the home or remote work. Thereby, the collection unit can efficiently collect highly relevant data by considering the geographical location information of the user. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to the generation AI and cause the generation AI to perform preferential collection of highly relevant data.

[0041] The collection unit can analyze the social media activity of the user at the time of collection and collect related data. The collection unit, for example, analyzes the social media activity of the user at the time of collection and collects related data. For example, the collection unit can collect information shared by the user on social media and extract data related to the project. The collection unit can also collect data related to events in which the user participated on social media. The collection unit can also collect important information related to the project from the user's interactions on social media. In this way, the collection unit can collect important information related to the project by analyzing the social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the social media activity data of the user to the generation AI and cause the generation AI to execute collection of related data.

[0042] The analysis unit can improve the accuracy of the analysis by considering the interrelationships of data during the analysis. The analysis unit can improve the accuracy of the analysis by considering the interrelationships of data during the analysis. For example, the analysis unit can analyze email and chat data by correlating them with each other to extract important information. The analysis unit can also analyze the history of a file and log data by correlating them with each other to evaluate the impact of changes. The analysis unit can also analyze the progress data of a project and the completion data of a task by correlating them with each other to evaluate the progress status. In this way, the analysis unit can improve the accuracy of the analysis by considering the interrelationships of data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can improve the accuracy of the analysis by using an AI model that evaluates the interrelationships of data.

[0043] The analysis unit can perform the analysis while considering the attribute information of the data submitter. For example, the analysis unit can perform the analysis while considering the attribute information of the data submitter. For example, the analysis unit can analyze the data based on the submitter's job title and the project in charge, and extract important information. The analysis unit can also analyze the data while considering the submitter's past performance and evaluation, and evaluate the reliability. The analysis unit can also analyze the data while considering the submitter's department and team, and extract highly relevant information. In this way, the analysis unit can obtain a more reliable analysis result by considering the submitter's attribute information. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that evaluates the submitter's attribute information.

[0044] The analysis unit can perform the analysis while taking into consideration the geographical distribution of the data. The analysis unit can perform the analysis while taking into consideration the geographical distribution of the data, for example. For example, the analysis unit can perform the analysis while taking into consideration the location where the data is generated, and evaluate the trends for each region. The analysis unit can also analyze the data while taking into consideration geographical factors, and extract characteristics for each region. The analysis unit can also analyze the data while taking into consideration the geographical distribution, and evaluate the performance for each region. In this way, the analysis unit can obtain an analysis result that reflects the characteristics for each region by taking the geographical distribution into consideration. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that evaluates the geographical distribution.

[0045] The analysis unit can improve the accuracy of the analysis by referring to related literature of the data during the analysis. The analysis unit can improve the accuracy of the analysis by referring to related literature of the data during the analysis. For example, the analysis unit can analyze the data by referring to related research papers to improve the accuracy. The analysis unit can also analyze the data by referring to related industry reports to improve the reliability. The analysis unit can also analyze the data by referring to related patent documents to strengthen the technical support. Thereby, the analysis unit can improve the accuracy of the analysis by referring to related literature. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can improve the accuracy of the analysis by using an AI model that evaluates related literature.

[0046] The compilation department can adjust the level of detail of the report based on the importance of the data at the time of compilation. For example, the compilation department adjusts the level of detail of the report based on the importance of the data at the time of compilation. For example, the compilation department can provide a report that describes data related to important projects in detail. The compilation department can also provide a report that describes data related to highly urgent tasks in detail. The compilation department can also provide a report that describes minutes of important meetings and decisions in detail. Thereby, the compilation department can provide important information in detail by adjusting the level of detail of the report based on the importance of the data. Part or all of the above-mentioned processing in the compilation department may be performed using, for example, AI, or may be performed without using AI. For example, the compilation department can adjust the level of detail of the report using an AI model that evaluates the importance of data.

[0047] The compilation unit can apply different compilation algorithms depending on the category of data during compilation. For example, the compilation unit applies different compilation algorithms depending on the category of data during compilation. For example, the compilation unit can apply a keyword search algorithm to email data to extract important information. The compilation unit can also apply an algorithm that analyzes the flow of conversation to chat data to collect highly relevant information. The compilation unit can also apply an algorithm that tracks the change history of a file to the file history to collect important changes. This allows the compilation unit to apply an optimal compilation algorithm depending on the category of data, thereby enabling efficient information organization. Part or all of the above-mentioned processing in the compilation unit may be performed using, for example, AI, or may be performed without using AI. For example, the compilation unit can perform efficient information organization using an AI model that executes a compilation algorithm depending on the category of data.

[0048] The compilation unit can determine the priority of the reports based on the submission time of the data at the time of compilation. The compilation unit, for example, determines the priority of the reports based on the submission time of the data at the time of compilation. For example, the compilation unit can prioritize compilation of recently submitted data to provide the latest information. The compilation unit can also prioritize compilation of data related to highly urgent tasks. The compilation unit can also prioritize compilation of minutes and decisions of important meetings. This allows the compilation unit to determine the priority of the reports based on the submission time of the data, thereby quickly providing the latest information. Part or all of the above-mentioned processing in the compilation unit may be performed, for example, using AI, or may be performed without using AI. For example, the compilation unit can determine the priority of the reports using an AI model that evaluates the submission time of the data.

[0049] The compilation unit can adjust the order of the reports based on the relevance of the data during compilation. The compilation unit, for example, adjusts the order of the reports based on the relevance of the data during compilation. For example, the compilation unit can prioritize compilation of highly relevant data and provide important information first. The compilation unit can also prioritize compilation of data related to the progress of the project. The compilation unit can also prioritize compilation of data related to important tasks. Thereby, the compilation unit can provide important information preferentially by adjusting the order of the reports based on the relevance of the data. A part or all of the above-mentioned processing in the compilation unit may be performed, for example, using AI, or may be performed without using AI. For example, the compilation unit can adjust the order of the reports using an AI model that evaluates the relevance of the data.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The project history creation system can further include a notification section. The notification section can automatically send notifications to relevant parties when the project progresses or important events occur. For example, when a specific task is completed, a notification can be sent to the person in charge or the PM by email or chat. In addition, when an important milestone in the project is reached, notifications can be sent to all relevant parties. Furthermore, if the project progress is delayed or if an important task is incomplete, a reminder notification can be sent. This allows the progress of the project to be grasped in real time and allows for quick response.

[0052] The project history creation system can further include a prediction unit. The prediction unit can predict the future progress and risks of the project based on the collected data. For example, by analyzing past data, the prediction unit can notify the risk when there is a possibility that the progress of the project may be delayed. The prediction unit can also calculate the expected time until a specific task is completed and provide this to the PM. Furthermore, the prediction unit can also suggest reallocation of resources when the resource allocation of the project is inappropriate. This makes it possible to grasp the risks of the project in advance and take appropriate measures.

[0053] The project history creation system may further include a feedback unit. The feedback unit may collect and analyze feedback from stakeholders regarding the progress and results of the project. For example, after the end of each phase of the project, a questionnaire may be sent to stakeholders to collect feedback. The collected feedback may also be analyzed to identify areas for improvement and success factors of the project. Furthermore, the feedback unit may generate improvement suggestions for the next project based on the collected feedback. This may improve the quality of the project and the satisfaction of stakeholders.

[0054] The project history creation system may further include a visualization unit. The visualization unit visually displays collected data, allowing the progress and risks of the project to be understood at a glance. For example, a Gantt chart or a burndown chart may be generated to visually display the progress of the project. A risk matrix may also be generated to visually display the risks of the project. Furthermore, the visualization unit may display the deliverables and task completion status of the project in a dashboard format. This allows the parties involved to quickly grasp the status of the project and make appropriate decisions.

[0055] The project official history creation system can further include a training unit. The training unit can generate a training program for the people involved based on the project data. For example, by analyzing past project data, if a particular skill is lacking, the training unit can propose a training program to improve that skill. The training unit can also adjust the timing of training for the people involved according to the progress of the project. Furthermore, the training unit can evaluate and improve the effectiveness of the training program based on feedback from the people involved. This can improve the skills of the people involved and the success rate of the project.

[0056] The process flow of the first embodiment will be briefly described below.

[0057] Step 1: The collection unit collects data such as in-house emails, chats, and file history and logs. For example, the collection unit acquires data from an in-house email server, chat system, file server, etc. Specifically, the collection unit can acquire email data from an email server, chat logs from a chat system, and file history from a file server. Step 2: The analysis unit analyzes the data collected by the collection unit to identify who did what and when. The analysis unit analyzes the data using, for example, natural language processing technology. Specifically, it can analyze the contents of emails and chats using morphological analysis, analyze the structure of sentences using grammatical analysis, and analyze the meaning of sentences using semantic analysis. Step 3: The editor generates the requested report based on the information identified by the analyzer. For example, the editor generates the report in PDF or Excel format upon request. Specifically, the editor can generate a report showing the progress of the project or a document summarizing the activity history for a specific period of time.

[0058] (Example 2) The project history creation system according to the embodiment of the present invention is a system that automatically collects and analyzes data such as internal emails, chats, file history and logs, and compiles the data upon request. This system is effective when a project manager (PM) or person in charge does not have time to summarize what happened and when during their busy days. The project history creation system can record who did what and when from internal emails, chats, file history and logs, and compile and show the data upon request. For example, the project history creation system collects data such as internal emails, chats, file history and logs. Next, the collected data is analyzed to identify who did what and when. For example, it is recorded that in a certain project, a specific person in charge completed a specific task on a specific day. Furthermore, based on the analyzed data, compilation is performed upon request. For example, a report showing the progress of the project and a document summarizing the activity history during a specific period are generated. This allows the history of the project to be easily reviewed and can be used as a reference material for the future. The project history creation system automatically creates the project's official history, reducing the burden on PMs and people in charge and allowing efficient project management. In addition, accurately recording the project history also helps with later verification and improvement. This allows the project history creation system to efficiently record and manage the project history.

[0059] The project history creation system according to the embodiment includes a collection unit, an analysis unit, and a compilation unit. The collection unit collects data such as in-house emails and chats, file history and logs, etc. The collection unit acquires data from, for example, an in-house mail server, chat system, file server, etc. For example, the collection unit can acquire email data from a mail server, a chat log from a chat system, and a file history from a file server. The analysis unit analyzes the data collected by the collection unit to identify who did what and when. The analysis unit analyzes the data using, for example, natural language processing technology. For example, the analysis unit can analyze the contents of emails and chats using morphological analysis, analyze the structure of a sentence using grammatical analysis, and analyze the meaning of a sentence using semantic analysis. The compilation unit generates a report in response to a request based on the information identified by the analysis unit. The compilation unit generates a report in, for example, PDF or Excel format in response to a request. For example, the compilation unit can generate a report showing the progress of a project or a document summarizing an activity history for a specific period of time. As a result, the project official history creation system according to the embodiment can efficiently record and manage the history of the project. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input email data acquired from a mail server to the generation AI, and cause the generation AI to execute analysis of the email data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input data collected by the collection unit to the generation AI, and cause the generation AI to execute analysis of the data. A part or all of the above-mentioned processing in the compilation unit may be performed, for example, using AI, or may be performed without using AI. For example, the compilation unit can input information specified by the analysis unit to the generation AI, and cause the generation AI to execute generation of a report in response to a request. As a result, the project official history creation system according to the embodiment can efficiently record and manage the history of the project.

[0060] The collection department collects data such as in-house emails, chats, and file history and logs. Specifically, data is acquired from in-house email servers, chat systems, file servers, etc. For example, the collection department can acquire email data from email servers, chat logs from chat systems, and file history from file servers. These data are important for recording the progress of projects and the activity history of each member in detail. The collection department can centrally manage these data and link with other systems and departments as necessary. For example, the collected data is stored on a cloud server so that the analysis department and editing department can access it. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. Furthermore, the collection department can automate data collection using AI. For example, email data acquired from email servers can be input into the generation AI, and the generation AI can be made to analyze the email data. This allows the collection department to efficiently and effectively collect data and improve the performance of the entire system.

[0061] The analysis unit analyzes the data collected by the collection unit and identifies who did what and when. The analysis uses natural language processing technology to analyze the data. Specifically, it can analyze the contents of emails and chats using morphological analysis, analyze the structure of sentences using grammatical analysis, and analyze the meaning of sentences using semantic analysis. For example, the analysis unit identifies the sender, recipient, sending date and time, and content of an email, and identifies the speaker, comment content, and comment date and time from the chat log. In addition, it can identify the creator, editor, edit date and time, and change content of a file from the file history. This allows the analysis unit to grasp the progress of the project and the activity history of each member in detail. Furthermore, the analysis unit can automate the analysis of data using AI. For example, the data collected by the collection unit can be input into the generation AI, and the generation AI can be made to analyze the data. The generation AI can use natural language processing technology to quickly and accurately analyze large amounts of data and identify the progress of the project and the activity history of each member. This allows the analysis unit to analyze data efficiently and effectively, improving the performance of the entire system.

[0062] The editing department generates a report according to the request based on the information identified by the analysis department. Specifically, the report is generated in PDF or Excel format according to the request. For example, the editing department can generate a report showing the progress of the project or a document summarizing the activity history for a specific period. This allows the progress of the project and the activity history of each member to be recorded and managed in detail. Furthermore, the editing department can automate the generation of reports using AI. For example, the information identified by the analysis department can be input into the generation AI, and the generation AI can be made to generate a report according to the request. The generation AI can record the progress of the project and the activity history of each member in detail based on the collected data and the analysis results, and quickly and accurately generate a report according to the request. This allows the editing department to generate reports efficiently and effectively, improving the performance of the entire system.

[0063] The collection unit includes an acquisition unit that acquires data from at least one of an in-house mail server, a chat system, and a file server. The collection unit, for example, acquires mail data from an in-house mail server. For example, the collection unit can acquire mail data from the mail server and collect mail sending and receiving history. The collection unit can also acquire chat logs from a chat system. For example, the collection unit can acquire chat logs from the chat system and collect chat contents. The collection unit can also acquire file history from a file server. For example, the collection unit can acquire file history from the file server and collect file change history. This allows the collection unit to acquire data from various in-house data sources, thereby enabling comprehensive data collection. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input mail data acquired from the mail server to the generation AI and cause the generation AI to execute analysis of the mail data.

[0064] The analysis unit can analyze data using natural language processing technology. The analysis unit analyzes data using, for example, natural language processing technology. For example, the analysis unit can analyze the contents of emails and chats using morphological analysis, analyze the structure of a sentence using grammatical analysis, and analyze the meaning of a sentence using semantic analysis. This allows the analysis unit to improve the accuracy of data analysis. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data collected by the collection unit to the generation AI and have the generation AI execute the analysis of the data.

[0065] The compilation unit can generate a report in PDF or Excel format in response to a request. The compilation unit, for example, generates a report in PDF format in response to a request. For example, the compilation unit can generate a report using a PDF generation library and save it in PDF format. The compilation unit can also generate a report in Excel format in response to a request. For example, the compilation unit can generate a report using an Excel generation library and save it in Excel format. As a result, the compilation unit can provide information according to the needs of the user by generating a report in a format according to the request. A part or all of the above-mentioned processing in the compilation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the compilation unit can input information identified by the analysis unit to the generation AI and cause the generation AI to generate a report according to the request.

[0066] The analysis unit can record that a specific person in charge has completed a specific task on a specific day. The analysis unit records, for example, that a specific person in charge has completed a specific task on a specific day. For example, the analysis unit can record that a specific person in charge has completed a specific task on a specific day based on the identification information of the person in charge. This allows the analysis unit to accurately record the task completion status of the person in charge. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the data collected by the collection unit to the generation AI and cause the generation AI to execute recording of the task completion status of the person in charge.

[0067] The compilation unit can generate a report showing the progress of the project or a document summarizing the activity history in a specific period. The compilation unit, for example, generates a report showing the progress of the project. For example, the compilation unit can generate a report showing the progress of the project and record the progress of the project in detail. The compilation unit can also generate a document summarizing the activity history in a specific period. For example, the compilation unit can generate a document summarizing the activity history in a specific period and record the activity history in detail. As a result, the compilation unit can record the progress of the project and the activity history in detail, which is useful for later verification and improvement. A part or all of the above-mentioned processing in the compilation unit may be performed using, for example, AI, or may be performed without using AI. For example, the compilation unit can input the information identified by the analysis unit into the generation AI and cause the generation AI to execute the generation of a report showing the progress of the project or a document summarizing the activity history in a specific period.

[0068] The collection unit can estimate the user's emotion and adjust the timing of data collection based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and adjusts the timing of data collection based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the burden. Also, when the user is relaxed, the collection unit can increase the frequency of data collection to collect detailed information. Also, when the user is in a hurry, the collection unit can collect only important data preferentially. Thereby, the collection unit can adjust the timing of data collection according to the user's emotion and reduce the burden on the user. 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 collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the timing of data collection.

[0069] The collection unit can determine the priority of collection based on the importance of data at the time of collection. For example, the collection unit can determine the priority of collection based on the importance of data at the time of collection. For example, the collection unit can preferentially collect data directly related to the progress of the project. In addition, the collection unit can preferentially collect minutes and decisions of important meetings. In addition, the collection unit can preferentially collect data related to tasks with high urgency. As a result, the collection unit can prioritize collecting important data, thereby enabling efficient data management. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can determine the priority of collection using an AI model that evaluates the importance of data.

[0070] The collection unit can apply different collection algorithms depending on the type of data at the time of collection. For example, the collection unit can apply different collection algorithms depending on the type of data at the time of collection. For example, the collection unit can apply a keyword search algorithm to email data to extract important information. In addition, the collection unit can apply an algorithm that analyzes the flow of conversation to chat data to collect highly relevant information. In addition, the collection unit can apply an algorithm that tracks the change history of a file to the file history to collect important changes. Thereby, the collection unit can improve the collection accuracy by applying a collection algorithm depending on the type of data. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can improve the collection accuracy by using an AI model that executes a collection algorithm depending on the type of data.

[0071] The collection unit can estimate the user's emotion and filter the data to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and filters the data to be collected based on the estimated user's emotion. For example, when the user is stressed, the collection unit can collect only important data and filter unnecessary data. Also, when the user is relaxed, the collection unit can collect detailed data and provide a wide range of information. Also, when the user is in a hurry, the collection unit can collect only data with high urgency. Thereby, the collection unit can collect only important data by filtering data 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 collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to execute data filtering.

[0072] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user at the time of collection. For example, the collection unit preferentially collects highly relevant data in consideration of the geographical location information of the user at the time of collection. For example, when the user is in a specific office, the collection unit can preferentially collect data related to the office. In addition, when the user is on a business trip, the collection unit can preferentially collect data related to the business trip destination. In addition, when the user is working remotely, the collection unit can preferentially collect data related to the home or remote work. Thereby, the collection unit can efficiently collect highly relevant data by considering the geographical location information of the user. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to the generation AI and cause the generation AI to perform preferential collection of highly relevant data.

[0073] The collection unit can analyze the social media activity of the user at the time of collection and collect related data. The collection unit, for example, analyzes the social media activity of the user at the time of collection and collects related data. For example, the collection unit can collect information shared by the user on social media and extract data related to the project. The collection unit can also collect data related to events in which the user participated on social media. The collection unit can also collect important information related to the project from the user's interactions on social media. In this way, the collection unit can collect important information related to the project by analyzing the social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the social media activity data of the user to the generation AI and cause the generation AI to execute collection of related data.

[0074] The analysis unit can estimate the user's emotion and adjust the analysis method based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the analysis method based on the estimated user's emotion. For example, when the user is stressed, the analysis unit can apply a concise and to the point analysis method. Also, when the user is relaxed, the analysis unit can apply a detailed analysis method to provide a wide range of information. Also, when the user is in a hurry, the analysis unit can apply an analysis method that can quickly obtain results. Thereby, the analysis unit can obtain a more appropriate analysis result by adjusting the analysis method 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 analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the analysis method.

[0075] The analysis unit can improve the accuracy of the analysis by considering the interrelationships of data during the analysis. The analysis unit can improve the accuracy of the analysis by considering the interrelationships of data during the analysis. For example, the analysis unit can analyze email and chat data by correlating them with each other to extract important information. The analysis unit can also analyze the history of a file and log data by correlating them with each other to evaluate the impact of changes. The analysis unit can also analyze the progress data of a project and the completion data of a task by correlating them with each other to evaluate the progress status. In this way, the analysis unit can improve the accuracy of the analysis by considering the interrelationships of data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can improve the accuracy of the analysis by using an AI model that evaluates the interrelationships of data.

[0076] The analysis unit can perform the analysis while considering the attribute information of the data submitter. For example, the analysis unit can perform the analysis while considering the attribute information of the data submitter. For example, the analysis unit can analyze the data based on the submitter's job title and the project in charge, and extract important information. The analysis unit can also analyze the data while considering the submitter's past performance and evaluation, and evaluate the reliability. The analysis unit can also analyze the data while considering the submitter's department and team, and extract highly relevant information. In this way, the analysis unit can obtain a more reliable analysis result by considering the submitter's attribute information. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that evaluates the submitter's attribute information.

[0077] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the display method of the analysis result based on the estimated user's emotion. For example, the analysis unit can provide a simple and highly visible display method when the user is nervous. Furthermore, the analysis unit can provide a display method including detailed information when the user is relaxed. Furthermore, the analysis unit can provide a display method that focuses on the main points when the user is in a hurry. In this way, the analysis unit can provide a more highly visible analysis result by adjusting the display method 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 analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method.

[0078] The analysis unit can perform the analysis while taking into consideration the geographical distribution of the data. The analysis unit can perform the analysis while taking into consideration the geographical distribution of the data, for example. For example, the analysis unit can perform the analysis while taking into consideration the location where the data is generated, and evaluate the trends for each region. The analysis unit can also analyze the data while taking into consideration geographical factors, and extract characteristics for each region. The analysis unit can also analyze the data while taking into consideration the geographical distribution, and evaluate the performance for each region. In this way, the analysis unit can obtain an analysis result that reflects the characteristics for each region by taking the geographical distribution into consideration. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that evaluates the geographical distribution.

[0079] The analysis unit can improve the accuracy of the analysis by referring to related literature of the data during the analysis. The analysis unit can improve the accuracy of the analysis by referring to related literature of the data during the analysis. For example, the analysis unit can analyze the data by referring to related research papers to improve the accuracy. The analysis unit can also analyze the data by referring to related industry reports to improve the reliability. The analysis unit can also analyze the data by referring to related patent documents to strengthen the technical support. Thereby, the analysis unit can improve the accuracy of the analysis by referring to related literature. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can improve the accuracy of the analysis by using an AI model that evaluates related literature.

[0080] The compilation unit can estimate the user's emotion and adjust the report expression method based on the estimated user's emotion. The compilation unit, for example, estimates the user's emotion and adjusts the report expression method based on the estimated user's emotion. For example, the compilation unit can provide a simple and highly visible report when the user is nervous. Also, the compilation unit can provide a report including detailed information when the user is relaxed. Also, the compilation unit can provide a report with the main points when the user is in a hurry. Thereby, the compilation unit can provide a more appropriate report by adjusting the report expression method 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 compilation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the editorial department can input a user's emotional data into the generation AI and have the generation AI adjust the way the report is expressed.

[0081] The compilation department can adjust the level of detail of the report based on the importance of the data at the time of compilation. For example, the compilation department adjusts the level of detail of the report based on the importance of the data at the time of compilation. For example, the compilation department can provide a report that describes data related to important projects in detail. The compilation department can also provide a report that describes data related to highly urgent tasks in detail. The compilation department can also provide a report that describes minutes of important meetings and decisions in detail. Thereby, the compilation department can provide important information in detail by adjusting the level of detail of the report based on the importance of the data. Part or all of the above-mentioned processing in the compilation department may be performed using, for example, AI, or may be performed without using AI. For example, the compilation department can adjust the level of detail of the report using an AI model that evaluates the importance of data.

[0082] The compilation unit can apply different compilation algorithms depending on the category of data during compilation. For example, the compilation unit applies different compilation algorithms depending on the category of data during compilation. For example, the compilation unit can apply a keyword search algorithm to email data to extract important information. The compilation unit can also apply an algorithm that analyzes the flow of conversation to chat data to collect highly relevant information. The compilation unit can also apply an algorithm that tracks the change history of a file to the file history to collect important changes. This allows the compilation unit to apply an optimal compilation algorithm depending on the category of data, thereby enabling efficient information organization. Part or all of the above-mentioned processing in the compilation unit may be performed using, for example, AI, or may be performed without using AI. For example, the compilation unit can perform efficient information organization using an AI model that executes a compilation algorithm depending on the category of data.

[0083] The editing unit can estimate the user's emotion and adjust the length of the report based on the estimated user's emotion. The editing unit, for example, estimates the user's emotion and adjusts the length of the report based on the estimated user's emotion. For example, the editing unit can provide a short report that is to the point when the user is nervous. Also, the editing unit can provide a longer report with detailed explanations when the user is relaxed. Also, the editing unit can provide a short report that can be read quickly when the user is in a hurry. This allows the editing unit to provide more appropriate information by adjusting the length of the 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 editing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the editorial department can input a user's emotional data into the generation AI and have the generation AI adjust the length of the report.

[0084] The compilation unit can determine the priority of the reports based on the submission time of the data at the time of compilation. The compilation unit, for example, determines the priority of the reports based on the submission time of the data at the time of compilation. For example, the compilation unit can prioritize compilation of recently submitted data to provide the latest information. The compilation unit can also prioritize compilation of data related to highly urgent tasks. The compilation unit can also prioritize compilation of minutes and decisions of important meetings. This allows the compilation unit to determine the priority of the reports based on the submission time of the data, thereby quickly providing the latest information. Part or all of the above-mentioned processing in the compilation unit may be performed, for example, using AI, or may be performed without using AI. For example, the compilation unit can determine the priority of the reports using an AI model that evaluates the submission time of the data.

[0085] The compilation unit can adjust the order of the reports based on the relevance of the data during compilation. The compilation unit, for example, adjusts the order of the reports based on the relevance of the data during compilation. For example, the compilation unit can prioritize compilation of highly relevant data and provide important information first. The compilation unit can also prioritize compilation of data related to the progress of the project. The compilation unit can also prioritize compilation of data related to important tasks. Thereby, the compilation unit can provide important information preferentially by adjusting the order of the reports based on the relevance of the data. A part or all of the above-mentioned processing in the compilation unit may be performed, for example, using AI, or may be performed without using AI. For example, the compilation unit can adjust the order of the reports using an AI model that evaluates the relevance of the data.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The project history creation system can further include a notification section. The notification section can automatically send notifications to relevant parties when the project progresses or important events occur. For example, when a specific task is completed, a notification can be sent to the person in charge or the PM by email or chat. In addition, when an important milestone in the project is reached, notifications can be sent to all relevant parties. Furthermore, if the project progress is delayed or if an important task is incomplete, a reminder notification can be sent. This allows the progress of the project to be grasped in real time and allows for quick response.

[0088] The project history creation system can further include a prediction unit. The prediction unit can predict the future progress and risks of the project based on the collected data. For example, by analyzing past data, the prediction unit can notify the risk when there is a possibility that the progress of the project may be delayed. The prediction unit can also calculate the expected time until a specific task is completed and provide this to the PM. Furthermore, the prediction unit can also suggest reallocation of resources when the resource allocation of the project is inappropriate. This makes it possible to grasp the risks of the project in advance and take appropriate measures.

[0089] The project history creation system may further include a feedback unit. The feedback unit may collect and analyze feedback from stakeholders regarding the progress and results of the project. For example, after the end of each phase of the project, a questionnaire may be sent to stakeholders to collect feedback. The collected feedback may also be analyzed to identify areas for improvement and success factors of the project. Furthermore, the feedback unit may generate improvement suggestions for the next project based on the collected feedback. This may improve the quality of the project and the satisfaction of stakeholders.

[0090] The project history creation system may further include a visualization unit. The visualization unit visually displays collected data, allowing the progress and risks of the project to be understood at a glance. For example, a Gantt chart or a burndown chart may be generated to visually display the progress of the project. A risk matrix may also be generated to visually display the risks of the project. Furthermore, the visualization unit may display the deliverables and task completion status of the project in a dashboard format. This allows the parties involved to quickly grasp the status of the project and make appropriate decisions.

[0091] The project official history creation system can further include a training unit. The training unit can generate a training program for the people involved based on the project data. For example, by analyzing past project data, if a particular skill is lacking, the training unit can propose a training program to improve that skill. The training unit can also adjust the timing of training for the people involved according to the progress of the project. Furthermore, the training unit can evaluate and improve the effectiveness of the training program based on feedback from the people involved. This can improve the skills of the people involved and the success rate of the project.

[0092] The analysis unit can estimate the user's emotions and adjust the priority of analysis results based on the estimated user's emotions. For example, if the user is feeling stressed, important analysis results can be displayed preferentially, with detailed information being postponed. Also, if the user is relaxed, detailed analysis results can be provided and a wide range of information can be displayed. Furthermore, if the user is in a hurry, analysis results that focus on the main points and can be quickly understood can be provided. This allows the analysis unit to provide more appropriate information by adjusting the priority of analysis results according to the user's emotions.

[0093] The collection unit can estimate the user's emotions and adjust the range of data collection based on the estimated user's emotions. For example, when the user is stressed, the range of data to be collected can be narrowed and only important data can be collected. When the user is relaxed, the range of data to be collected can be widened and detailed information can be collected. Furthermore, when the user is in a hurry, only data that can be collected quickly can be collected preferentially. This allows the collection unit to adjust the range of data collection according to the user's emotions, thereby enabling efficient data collection.

[0094] The compilation unit can estimate the user's emotions and adjust the report format based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible format can be provided. If the user is relaxed, a format including detailed information can be provided. Furthermore, if the user is in a hurry, a concise format focusing on the main points can be provided. In this way, the compilation unit can provide more appropriate information by adjusting the report format according to the user's emotions.

[0095] The analysis unit can estimate the user's emotions and adjust the depth of the analysis based on the estimated user's emotions. For example, if the user is feeling stressed, a concise analysis that focuses on the main points can be provided. If the user is relaxed, a detailed analysis can be provided to provide a wide range of information. Furthermore, if the user is in a hurry, an analysis that can quickly obtain results can be provided. In this way, the analysis unit can provide more appropriate analysis results by adjusting the depth of the analysis according to the user's emotions.

[0096] The compilation unit can estimate the user's emotions and adjust the contents of the report based on the estimated user's emotions. For example, if the user is nervous, a report including only important information can be provided. If the user is relaxed, a report including detailed information can be provided. Furthermore, if the user is in a hurry, a report that is quick to read and includes the main points can be provided. This allows the compilation unit to provide more appropriate information by adjusting the contents of the report according to the user's emotions.

[0097] The process flow of the second embodiment will be briefly described below.

[0098] Step 1: The collection unit collects data such as in-house emails, chats, and file history and logs. For example, the collection unit acquires data from an in-house email server, chat system, file server, etc. Specifically, the collection unit can acquire email data from an email server, chat logs from a chat system, and file history from a file server. Step 2: The analysis unit analyzes the data collected by the collection unit to identify who did what and when. The analysis unit analyzes the data using, for example, natural language processing technology. Specifically, it can analyze the contents of emails and chats using morphological analysis, analyze the structure of sentences using grammatical analysis, and analyze the meaning of sentences using semantic analysis. Step 3: The editor generates the requested report based on the information identified by the analyzer. For example, the editor generates the report in PDF or Excel format upon request. Specifically, the editor can generate a report showing the progress of the project or a document summarizing the activity history for a specific period of time.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] Each of the multiple elements including the above-mentioned collection unit, analysis unit, and compilation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect data such as in-house emails and chats, file history and logs, etc. by the control unit 46A of the smart device 14. The analysis unit analyzes data collected, for example, by the specific processing unit 290 of the data processing device 12, and identifies who did what and when. The compilation unit generates a report in response to a request, for example, by the specific processing unit 290 of the data processing device 12. A part or all of the multiple elements including the collection unit, analysis unit, and compilation unit may be realized, for example, by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements including the above-mentioned collection unit, analysis unit, and compilation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect data such as in-house emails and chats, file history and logs, etc. by the control unit 46A of the smart glasses 214. The analysis unit analyzes data collected, for example, by the specific processing unit 290 of the data processing device 12, and identifies who did what and when. The compilation unit generates a report in response to a request, for example, by the specific processing unit 290 of the data processing device 12. A part or all of the multiple elements including the collection unit, analysis unit, and compilation unit may be realized, for example, by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements including the above-mentioned collection unit, analysis unit, and compilation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect data such as in-house emails and chats, file history and logs, etc. by the control unit 46A of the headset type terminal 314. The analysis unit analyzes data collected, for example, by the specific processing unit 290 of the data processing device 12, and identifies who did what and when. The compilation unit generates a report in response to a request by, for example, the specific processing unit 290 of the data processing device 12. A part or all of the multiple elements including the collection unit, analysis unit, and compilation unit may be realized, for example, by the control unit 46A of the headset type terminal 314. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the plurality of elements including the above-mentioned collection unit, analysis unit, and compilation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect data such as in-house emails and chats, file history and logs, etc., by the control unit 46A of the robot 414. The analysis unit analyzes data collected, for example, by the specific processing unit 290 of the data processing device 12, and identifies who did what and when. The compilation unit generates a report in response to a request, for example, by the specific processing unit 290 of the data processing device 12. A part or all of the plurality of elements including the collection unit, analysis unit, and compilation unit may be realized, for example, by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0152] 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.

[0153] 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.

[0154] These emotions are distributed in the three 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Appendix 1) A collection unit that collects at least one of data of an internal email or chat, and a file history or log; an analysis unit that analyzes the data collected by the collection unit and identifies who did what and when; a compilation unit that generates a report responsive to the request based on the information identified by the analysis unit. A system characterized by: (Appendix 2) The collecting unit includes: Equipped with an acquisition unit that acquires data from at least one of the company's mail server, chat system, and file server 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze data using natural language processing techniques 2. The system of claim 1, (Appendix 4) The editorial department: Generate reports in PDF or Excel format upon request 2. The system of claim 1, (Appendix 5) The analysis unit is Record that a specific person completed a specific task on a specific date 2. The system of claim 1, (Appendix 6) The editorial department: Generate reports showing the progress of a project or a document outlining the activity over a specific period of time 2. The system of claim 1, (Appendix 7) The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1, (Appendix 8) The collecting unit includes: At the time of collection, prioritize collection based on the importance of the data 2. The system of claim 1, (Appendix 9) The collecting unit includes: At the time of collection, different collection algorithms are applied depending on the type of data 2. The system of claim 1, (Appendix 10) The collecting unit includes: Infer user emotions and filter data to be collected based on the estimated user emotions. 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting data, the data is collected based on the user's geographic location. 2. The system of claim 1, (Appendix 12) The collecting unit includes: At the time of collection, analyze your social media activity and collect relevant data 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust analysis methods based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, the accuracy of the analysis is improved by considering the correlation of data. 2. The system of claim 1, (Appendix 15) The analysis unit is When analyzing, the attribute information of the data submitter is taken into consideration. 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, the geographic distribution of data is taken into account. 2. The system of claim 1, (Appendix 18) The analysis unit is When analyzing, refer to related literature to improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 19) The editorial department: Infer user sentiment and adjust report presentation based on the inferred user sentiment 2. The system of claim 1, (Appendix 20) The editorial department: When compiling, adjust the level of detail in the report based on the importance of the data 2. The system of claim 1, (Appendix 21) The editorial department: During compilation, different compilation algorithms are applied depending on the category of data. 2. The system of claim 1, (Appendix 22) The editorial department: Infer user sentiment and adjust report length based on estimated user sentiment 2. The system of claim 1, (Appendix 23) The editorial department: During compilation, reports will be prioritized based on when data was submitted. 2. The system of claim 1, (Appendix 24) The editorial department: During compilation, adjust the order of reports based on data relevance 2. The system of claim 1, [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects at least one of data of an internal email or chat, and a file history or log; an analysis unit that analyzes the data collected by the collection unit and identifies who did what and when; a compilation unit that generates a report responsive to the request based on the information identified by the analysis unit. A system characterized by:

2. The collecting unit includes: Equipped with an acquisition unit that acquires data from at least one of the company's mail server, chat system, and file server 2. The system of claim 1 .

3. The analysis unit is Analyze data using natural language processing techniques 2. The system of claim 1 .

4. The editorial department: Generate reports in PDF or Excel format upon request 2. The system of claim 1 .

5. The analysis unit is Record that a specific person completed a specific task on a specific date 2. The system of claim 1 .

6. The editorial department: Generate reports showing the progress of a project or a document outlining the activity over a specific period of time 2. The system of claim 1 .

7. The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1 .

8. The collecting unit includes: At the time of collection, prioritize collection based on the importance of the data 2. The system of claim 1 .

9. The collecting unit includes: At the time of collection, different collection algorithms are applied depending on the type of data.

2. The system of claim 1 .

10. The collecting unit includes: Infer user emotions and filter data to be collected based on the estimated user emotions.

2. The system of claim 1 .

Images