System

Abstract

An object of the system according to the embodiment is to automatically extract and manage tasks from a large amount of work request mails and track the progress status.SOLUTION: A system includes a business request mail analysis part, a task extraction part, and an task management part. The job request mail analysis part analyzes the contents of the job request mail. A task extraction part automatically extracts and classifies necessary tasks from the operation request mail analyzed by the operation request mail analysis part. The task management unit automatically manages the task extracted by the task extraction unit, and tracks the progress status.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the process of extracting and managing tasks from a large number of work request emails is time-consuming and there is a risk that tasks will be overlooked.

[0005] The system according to the embodiment aims to automatically extract and manage tasks from a large number of work request emails and track their progress. [Means for solving the problem]

[0006] The system according to the embodiment includes a task request email analysis unit, a task extraction unit, and a task management unit. The task request email analysis unit analyzes the content of task request emails. The task extraction unit automatically extracts and classifies necessary tasks from the task request emails analyzed by the task request email analysis unit. The task management unit automatically manages the tasks extracted by the task extraction unit and tracks their progress. [Effects of the Invention]

[0007] The system according to the embodiment can automatically extract and manage tasks from a large number of work request emails and track their progress. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's 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 read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The business improvement system according to an embodiment of the present invention is a system targeted at the sales support staff for "Mobile Resale Line" in our corporate sales department. This system uses a "Business Improvement Generator" powered by generative AI to lower the programming skill barrier and realize business automation development with minimal development effort. As a result, the business improvement system uses generative AI to automate the sales support work for "Mobile Resale Line", reducing work and management efforts while also reducing the risk of missing or overlooking tasks.

[0029] A business improvement system according to an embodiment includes a business request email analysis unit, a task extraction unit, and a task management unit. The business request email analysis unit analyzes the content of business request emails. For example, the business request email analysis unit analyzes the content of the emails using natural language processing technology and extracts necessary information. The business request email analysis unit can also extract important keywords from the emails using keyword extraction technology. The business request email analysis unit can also analyze the content of the emails based on their context using context analysis technology. For example, the business request email analysis unit analyzes the content of the emails and automatically recognizes tasks such as "requesting the addition of a new line" or "requesting the change of an existing line." The task extraction unit automatically extracts and classifies necessary tasks from the business request emails analyzed by the business request email analysis unit. For example, the task extraction unit extracts tasks from the content of the emails using a machine learning algorithm. The task extraction unit can also classify tasks using rule-based classification technology. The task extraction unit can also classify the extracted tasks into project tasks, daily work tasks, urgent tasks, etc. For example, the task extraction unit analyzes the content of emails to extract and classify tasks. The task management unit automatically manages the tasks extracted by the task extraction unit and tracks their progress. For example, the task management unit periodically checks the progress of tasks and automatically reminds users of incomplete tasks and tasks with approaching deadlines. The task management unit can also automatically prioritize tasks so that important tasks are processed first. The task management unit can also visualize the progress of tasks so that managers can understand the progress at a glance. For example, the task management unit displays the progress of tasks in graphs and charts. As a result, the business improvement system according to the embodiment analyzes the content of work request emails, automatically extracts and classifies tasks, and tracks the progress, thereby improving business efficiency and preventing tasks from being overlooked.

[0030] The work request email analysis unit can improve accuracy by referencing the requester's past request history and learning patterns of request content. For example, when the generation AI analyzes a work request email, the work request email analysis unit retrieves the requester's past request history from a database and learns patterns of request content. For example, it prioritizes extraction of tasks that have been frequently requested in the past. The work request email analysis unit also analyzes trends in request content based on the past request history and automatically groups similar requests. For example, it processes similar requests from the same requester in a batch. The work request email analysis unit also improves the accuracy of predicting request content by referencing the requester's past request history and learning patterns of request content. For example, even if part of the request content is unclear, it can complement it based on past patterns. In this way, by referencing the requester's past request history and learning patterns of request content, analysis accuracy is improved.

[0031] The work request email analysis unit can automatically evaluate the urgency or importance of the request content and set priorities. For example, the work request email analysis unit develops an algorithm to analyze work request emails using a generation AI and evaluate the urgency and importance of the request content. For example, it determines the urgency based on keywords and phrases contained in the request content. The work request email analysis unit also builds a system to evaluate the urgency and importance of the request content and set priorities. For example, it automatically sorts tasks so that requests with high urgency are processed first. The work request email analysis unit also refers to the requester's instructions and past request history to evaluate the urgency and importance of the request content. For example, requests from specific requesters are always treated as high priority. This allows important tasks to be processed first by evaluating the urgency and importance of the request content and setting priorities.

[0032] The work request email analysis unit can also handle requests made via voice input or chatbots. For example, the work request email analysis unit will build a system in which a generation AI analyzes voice input and automatically extracts and classifies tasks in the same way as work request emails. For example, it will use voice recognition technology to convert voice data into text. The work request email analysis unit will also develop a system that automatically processes work requests received through chatbots. For example, it will analyze the content of chatbot conversations and extract necessary tasks. The work request email analysis unit will also build a system that integrates voice input and requests from chatbots with work request emails and centrally manages them. For example, it will store requests in different input formats in the same database. This will enable it to handle requests made via voice input and chatbots, thereby addressing the diversification of request methods.

[0033] The work request email analysis unit can handle work request emails in different languages, making it possible to build a multilingual automatic processing system. For example, the work request email analysis unit develops a system in which a generation AI automatically translates work request emails written in different languages ​​and extracts and classifies tasks. For example, it uses a translation API to convert the email content into Japanese. The work request email analysis unit also builds a multilingual work request email processing system and centrally manages requests in different languages. For example, it supports multiple languages ​​such as English, French, and Chinese. The work request email analysis unit also develops a multilingual algorithm for analyzing work request emails in different languages ​​and extracting tasks. For example, it performs analysis taking into account the grammar and expressions of each language. This allows it to handle work request emails in different languages, making it possible to handle international work requests.

[0034] The task extraction unit can understand the detailed context of the request and break it down into smaller tasks. For example, the generation AI in the task extraction unit analyzes the context of a work request email and develops an algorithm to understand the request in detail. For example, it breaks down tasks taking into account the background information and purpose of the request. The task extraction unit also builds a system that understands the detailed context of the request and breaks it down into smaller tasks. For example, it extracts tasks that are divided into multiple steps individually. The task extraction unit also introduces natural language processing technology that allows the generation AI to analyze the context of the request and break down the tasks into smaller tasks. For example, it identifies the subject and object of the request and breaks down the tasks based on that. This improves the accuracy of the tasks by understanding the detailed context of the request and breaking it down into smaller tasks.

[0035] The task extraction unit can group related tasks based on the relevance of the request content. For example, the task extraction unit constructs a system in which the generation AI analyzes the content of a work request email and automatically groups related tasks. For example, tasks related to the same project are grouped together. The task extraction unit also develops an algorithm for grouping tasks taking into account the relevance of the request content. For example, it evaluates the relevance based on keywords and phrases in the request content. The task extraction unit also introduces natural language processing technology so that the generation AI can analyze the relevance of the request content and group related tasks. For example, it understands the context of the request content and automatically classifies related tasks. This makes task management easier by taking into account the relevance of the request content and grouping related tasks.

[0036] The task extraction unit can classify tasks from sources other than work request emails. For example, the task extraction unit builds a system in which the generation AI extracts and classifies tasks from internal chats and project management tools. For example, it analyzes chat conversation content and project progress information. The task extraction unit also develops algorithms to extract tasks from sources other than work request emails. For example, it integrates data from different sources and manages it centrally. The task extraction unit also introduces natural language processing technology to enable the generation AI to extract and classify tasks from internal chats and project management tools. For example, it understands the context of chats and automatically classifies tasks. This expands the scope of task management by extracting and classifying tasks from sources other than work request emails.

[0037] The task extraction unit can convert the extraction and classification results into visual notes or mind maps to facilitate visual understanding. For example, the task extraction unit can build a system that converts tasks extracted and classified by the generation AI into visual notes. For example, it can show the key points of the tasks using diagrams or icons. The task extraction unit can also convert extracted and classified tasks into mind map format, visually organizing related keywords and concepts. This allows users to understand the overall picture of the task at a glance. The task extraction unit can also develop tools that automatically generate visual notes and mind maps, allowing users to easily visually display task summaries. For example, it can provide a function for visualizing tasks using drag and drop. By converting the extraction and classification results into visual notes or mind maps, tasks can be more easily understood.

[0038] When tracking task progress, the task management unit can predict task completion and send reminders based on the prediction. For example, the task management unit develops an algorithm that allows the generation AI to track task progress and predict completion. For example, it predicts the time until completion based on past task data. The task management unit also builds a system that automatically sends task reminders based on the completion prediction. For example, it automatically sends reminders for tasks whose deadlines are approaching. The task management unit also collects data that the generation AI uses to monitor task progress in real time and make completion predictions. For example, it regularly updates task progress. This allows the generation AI to track task progress, predict completion, and send reminders based on the prediction, thereby improving the task completion rate.

[0039] The task management unit can refer to the communication history between the requester and the person in charge and identify factors that affect the progress of a task. For example, the task management unit builds a system in which a generation AI analyzes the communication history between the requester and the person in charge and identifies factors that affect the progress of a task. For example, it analyzes the content of emails and chats. The task management unit also develops an algorithm that identifies factors that affect the progress of a task based on the communication history. For example, it identifies tasks that require frequent communication. The task management unit also introduces natural language processing technology that allows the generation AI to refer to the communication history between the requester and the person in charge and identify factors that affect the progress of a task. For example, it analyzes the tone and content of the communication. This makes task progress management more accurate by referring to the communication history between the requester and the person in charge and identifying factors that affect the progress of a task.

[0040] The task management unit can link with other project management tools and calendar apps to achieve integrated task management. For example, the task management unit builds a system in which the generation AI links the task management system with other project management tools and calendar apps. For example, it synchronizes data using an API. The task management unit also develops an interface to link with other project management tools and calendar apps to achieve integrated task management. For example, it displays task progress in a unified manner. The task management unit also builds a system in which the generation AI links with other project management tools and calendar apps to update task progress in real time. For example, it ensures that changes to tasks are reflected immediately. This improves the efficiency of task management by linking with other project management tools and calendar apps to achieve integrated task management.

[0041] The task management unit can visualize task progress using dashboards and graphs to enable intuitive understanding. For example, the task management unit builds a system in which the generation AI visualizes task progress using dashboards and graphs. For example, it displays the task progress rate and completion status in graphs. The task management unit also develops a dashboard to visualize task progress so that users can understand it intuitively. For example, it displays task priorities and deadlines using different colors. The task management unit also builds a system in which the generation AI updates task progress in real time and reflects it on dashboards and graphs. For example, it periodically collects task progress data and visualizes it. As a result, task progress can be visualized using dashboards and graphs, making it possible to intuitively understand task progress.

[0042] The task management unit takes task dependencies into account and can simultaneously manage the progress of dependent tasks. For example, the task management unit constructs a system in which the generation AI analyzes task dependencies and simultaneously manages the progress of dependent tasks. For example, it updates the completion status of dependent tasks in real time. The task management unit also develops an algorithm for taking task dependencies into account and simultaneously managing the progress of dependent tasks. For example, it sends reminders when the progress of dependent tasks is behind schedule. The task management unit also introduces natural language processing technology that allows the generation AI to analyze task dependencies and simultaneously manage the progress of dependent tasks. For example, it automatically identifies the relationships between dependent tasks. This allows task dependency to be taken into account and the progress of dependent tasks to be managed simultaneously, resulting in more accurate task progress management.

[0043] The task management unit can evaluate the workload of each person in charge and suggest redistribution of tasks if the workload is high. For example, the task management unit builds a system in which the generation AI tracks the progress of tasks and evaluates the workload of each person in charge. For example, it calculates the workload based on the number of tasks and progress of each person in charge. The task management unit also develops an algorithm that suggests redistribution of tasks when the workload is high. For example, it suggests allocating tasks to another person in charge. The task management unit also builds a system in which the generation AI monitors the workload of each person in charge in real time and suggests redistribution of tasks if the workload is high. For example, it automatically redistributes tasks if the workload is above a certain level. This allows the workload to be balanced by evaluating the workload of each person in charge and suggesting redistribution of tasks if the load is high.

[0044] The task management unit can share task progress with other project members, enabling progress management across the entire team. For example, the task management unit builds a system in which the generation AI shares task progress with other project members. For example, it updates the progress in real time so that everyone can access it. The task management unit also develops an interface to enable progress management across the entire team. For example, it provides a dashboard that centrally displays task progress. The task management unit also builds a system in which the generation AI updates task progress in real time and shares it with other project members. For example, it ensures that changes to tasks are reflected immediately. This allows task progress to be shared with other project members, enabling progress management across the entire team, promoting team cooperation.

[0045] The task management unit can report the task progress status in real time through a voice assistant, enabling hands-free progress confirmation. For example, the task management unit builds a system in which the generation AI reports the task progress status in real time through a voice assistant. For example, the voice assistant reads out the task progress status. The task management unit also develops a voice assistant that enables hands-free progress confirmation. For example, it makes it possible to check the task progress status using voice commands. The task management unit also builds a system in which the generation AI updates the task progress status in real time and reports it through the voice assistant. For example, it makes it possible to instantly reflect changes to the task in the voice assistant. This improves work efficiency by reporting the task progress status in real time through the voice assistant and enabling hands-free progress confirmation.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The work request email analysis unit can automatically evaluate the urgency or importance of the request content and set priorities. For example, an algorithm is developed in which the generation AI analyzes work request emails and evaluates the urgency and importance of the request content. The urgency is determined based on keywords and phrases contained in the request content. A system is also built to evaluate the urgency and importance of the request content and set priorities. Tasks are automatically sorted so that requests with high urgency are given priority. To evaluate the urgency and importance of the request content, the requester's instructions and past request history are referenced. Requests from specific requesters are always treated as high priority. This allows important tasks to be processed first by evaluating the urgency and importance of the request content and setting priorities.

[0048] The work request email analysis unit can also handle requests made via voice input or chatbots. A system will be built in which the generation AI analyzes voice input and automatically extracts and classifies tasks in the same way as work request emails. Voice data will be converted into text using voice recognition technology. A system will be developed that automatically processes work requests received through chatbots. The content of the chatbot's conversation will be analyzed and the necessary tasks will be extracted. A system will be built that integrates voice input and requests from chatbots with work request emails and manages them centrally. Requests in different input formats will be saved in the same database. This will enable requests made via voice input and chatbots to be handled, making it possible to respond to a variety of request methods.

[0049] The work request email analysis unit can handle work request emails in different languages, making it possible to build a multilingual automatic processing system. A generation AI will automatically translate work request emails written in different languages ​​and develop a system that extracts and classifies tasks. A translation API will be used to convert the email content into Japanese. A multilingual work request email processing system will be built and requests in different languages ​​will be managed centrally. It will support multiple languages ​​such as English, French, and Chinese. A multilingual algorithm will be developed to analyze work request emails in different languages ​​and extract tasks. Analysis will be performed taking into account the grammar and expressions of each language. This will allow work request emails in different languages ​​to be handled, making it possible to handle international work requests.

[0050] The task extraction unit can understand the detailed context of the request and break it down into smaller tasks. The generation AI analyzes the context of the work request email and develops an algorithm to understand the request in detail. It breaks down the tasks taking into account the background information and purpose of the request. It builds a system that understands the detailed context of the request and breaks it down into smaller tasks. It individually extracts tasks that are divided into multiple steps. The generation AI analyzes the context of the request and introduces natural language processing technology to break down the tasks. It identifies the subject and object of the request and breaks down the tasks based on that. This improves the accuracy of the tasks by understanding the detailed context of the request and breaking it down into smaller tasks.

[0051] The task extraction unit can group related tasks based on the relevance of the request content. A system will be built in which the generation AI will analyze the content of work request emails and automatically group related tasks. Tasks related to the same project will be grouped together. An algorithm will be developed to group tasks taking into account the relevance of the request content. Relevance will be evaluated based on keywords and phrases in the request content. Natural language processing technology will be introduced so that the generation AI can analyze the relevance of the request content and group related tasks. The context of the request content will be understood and related tasks will be automatically classified. This will make task management easier by taking into account the relevance of the request content and grouping related tasks.

[0052] The task extraction unit can classify tasks from sources other than work request emails. A system will be built in which the generation AI extracts and classifies tasks from internal chats and project management tools. Chat conversation content and project progress information will be analyzed. An algorithm will be developed to extract tasks from sources other than work request emails. Data from different sources will be integrated and managed centrally. Natural language processing technology will be introduced to enable the generation AI to extract and classify tasks from internal chats and project management tools. The context of the chat will be understood and tasks will be automatically classified. This will expand the scope of task management by allowing tasks to be extracted and classified from sources other than work request emails.

[0053] When tracking task progress, the task management unit can predict task completion and send reminders based on the prediction. The generation AI tracks task progress and develops an algorithm to predict completion. It predicts the time until completion based on past task data. It builds a system to automatically send task reminders based on the completion prediction. It automatically sends reminders for tasks with approaching deadlines. The generation AI monitors task progress in real time and collects data to predict completion. It regularly updates task progress. As a result, by tracking task progress, predicting completion, and sending reminders based on the prediction, task completion rates improve.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The task request email analysis unit analyzes the content of the task request email. For example, it uses natural language processing technology to analyze the content of the email and extract the necessary information. It can also use keyword extraction technology to extract important keywords from the email. It can also use context analysis technology to analyze the content of the email based on the context. For example, it can automatically recognize tasks such as "requesting the addition of a new line" or "requesting changes to an existing line." Step 2: The task extraction unit automatically extracts and classifies the necessary tasks from the work request emails analyzed by the work request email analysis unit. For example, it can extract tasks from the email content using a machine learning algorithm. It can also classify tasks using rule-based classification techniques. It can also classify the extracted tasks into project tasks, daily work tasks, urgent tasks, etc. Step 3: The task management section automatically manages the tasks extracted by the task extraction section and tracks their progress. For example, it periodically checks the progress of tasks and automatically reminds users of incomplete tasks or tasks with approaching deadlines. It can also automatically set task priorities so that important tasks are handled first. It can also visualize task progress so that managers can understand it at a glance. For example, it can display task progress in graphs and charts.

[0056] (Example 2) The business improvement system according to an embodiment of the present invention is a system targeted at the sales support staff for "Mobile Resale Line" in our corporate sales department. This system uses a "Business Improvement Generator" powered by generative AI to lower the programming skill barrier and realize business automation development with minimal development effort. As a result, the business improvement system uses generative AI to automate the sales support work for "Mobile Resale Line", reducing work and management efforts while also reducing the risk of missing or overlooking tasks.

[0057] A business improvement system according to an embodiment includes a business request email analysis unit, a task extraction unit, and a task management unit. The business request email analysis unit analyzes the content of business request emails. For example, the business request email analysis unit analyzes the content of the emails using natural language processing technology and extracts necessary information. The business request email analysis unit can also extract important keywords from the emails using keyword extraction technology. The business request email analysis unit can also analyze the content of the emails based on their context using context analysis technology. For example, the business request email analysis unit analyzes the content of the emails and automatically recognizes tasks such as "requesting the addition of a new line" or "requesting the change of an existing line." The task extraction unit automatically extracts and classifies necessary tasks from the business request emails analyzed by the business request email analysis unit. For example, the task extraction unit extracts tasks from the content of the emails using a machine learning algorithm. The task extraction unit can also classify tasks using rule-based classification technology. The task extraction unit can also classify the extracted tasks into project tasks, daily work tasks, urgent tasks, etc. For example, the task extraction unit analyzes the content of emails to extract and classify tasks. The task management unit automatically manages the tasks extracted by the task extraction unit and tracks their progress. For example, the task management unit periodically checks the progress of tasks and automatically reminds users of incomplete tasks and tasks with approaching deadlines. The task management unit can also automatically prioritize tasks so that important tasks are processed first. The task management unit can also visualize the progress of tasks so that managers can understand the progress at a glance. For example, the task management unit displays the progress of tasks in graphs and charts. As a result, the business improvement system according to the embodiment analyzes the content of work request emails, automatically extracts and classifies tasks, and tracks the progress, thereby improving business efficiency and preventing tasks from being overlooked.

[0058] The work request email analysis unit can improve accuracy by referencing the requester's past request history and learning patterns of request content. For example, when the generation AI analyzes a work request email, the work request email analysis unit retrieves the requester's past request history from a database and learns patterns of request content. For example, it prioritizes extraction of tasks that have been frequently requested in the past. The work request email analysis unit also analyzes trends in request content based on the past request history and automatically groups similar requests. For example, it processes similar requests from the same requester in a batch. The work request email analysis unit also improves the accuracy of predicting request content by referencing the requester's past request history and learning patterns of request content. For example, even if part of the request content is unclear, it can complement it based on past patterns. In this way, by referencing the requester's past request history and learning patterns of request content, analysis accuracy is improved.

[0059] The work request email analysis unit can automatically evaluate the urgency or importance of the request content and set priorities. For example, the work request email analysis unit develops an algorithm to analyze work request emails using a generation AI and evaluate the urgency and importance of the request content. For example, it determines the urgency based on keywords and phrases contained in the request content. The work request email analysis unit also builds a system to evaluate the urgency and importance of the request content and set priorities. For example, it automatically sorts tasks so that requests with high urgency are processed first. The work request email analysis unit also refers to the requester's instructions and past request history to evaluate the urgency and importance of the request content. For example, requests from specific requesters are always treated as high priority. This allows important tasks to be processed first by evaluating the urgency and importance of the request content and setting priorities.

[0060] The work request email analysis unit can analyze the emotions of the requester and prioritize emotionally important requests. For example, the work request email analysis unit develops an algorithm that uses a generation AI to analyze work request emails and estimate the requester's emotions. For example, it calculates an emotion score from the email's writing style and expressions. The work request email analysis unit also uses the emotion estimation function to build a system that prioritizes requests that strongly express the requester's emotions. For example, it automatically sets requests with a high emotion score as a high priority. The work request email analysis unit also learns past emotion data to analyze the requester's emotions and identify emotionally important requests. For example, it prioritizes emails from requesters who have made many emotional requests in the past. In this way, the system analyzes the requester's emotions and prioritizes processing emotionally important requests, thereby improving requester satisfaction.

[0061] The work request email analysis unit can also handle requests made via voice input or chatbots. For example, the work request email analysis unit will build a system in which a generation AI analyzes voice input and automatically extracts and classifies tasks in the same way as work request emails. For example, it will use voice recognition technology to convert voice data into text. The work request email analysis unit will also develop a system that automatically processes work requests received through chatbots. For example, it will analyze the content of chatbot conversations and extract necessary tasks. The work request email analysis unit will also build a system that integrates voice input and requests from chatbots with work request emails and centrally manages them. For example, it will store requests in different input formats in the same database. This will enable it to handle requests made via voice input and chatbots, thereby addressing the diversification of request methods.

[0062] The work request email analysis unit can handle work request emails in different languages, making it possible to build a multilingual automatic processing system. For example, the work request email analysis unit develops a system in which a generation AI automatically translates work request emails written in different languages ​​and extracts and classifies tasks. For example, it uses a translation API to convert the email content into Japanese. The work request email analysis unit also builds a multilingual work request email processing system and centrally manages requests in different languages. For example, it supports multiple languages ​​such as English, French, and Chinese. The work request email analysis unit also develops a multilingual algorithm for analyzing work request emails in different languages ​​and extracting tasks. For example, it performs analysis taking into account the grammar and expressions of each language. This allows it to handle work request emails in different languages, making it possible to handle international work requests.

[0063] The work request email analysis unit can analyze the emotions of the requester when they enter their email in real time and make suggestions that will elicit positive emotions. For example, the work request email analysis unit will build a system in which a generation AI analyzes the emotions of the requester in real time when they enter their work request email and makes suggestions that will elicit positive emotions. For example, it will analyze the requester's facial expressions and voice. The work request email analysis unit will also use an emotion estimation function to monitor the emotions of the requester when they enter their email in real time and display messages that will elicit positive emotions. For example, it will present words of encouragement or success stories. The work request email analysis unit will also develop a system that analyzes the emotions of the requester and provides an interface that will elicit positive emotions. For example, it will change the interface design depending on the emotion score. This will allow the system to analyze the emotions of the requester when they enter their email in real time and make suggestions that will elicit positive emotions, thereby improving requester satisfaction.

[0064] The task extraction unit can understand the detailed context of the request and break it down into smaller tasks. For example, the generation AI in the task extraction unit analyzes the context of a work request email and develops an algorithm to understand the request in detail. For example, it breaks down tasks taking into account the background information and purpose of the request. The task extraction unit also builds a system that understands the detailed context of the request and breaks it down into smaller tasks. For example, it extracts tasks that are divided into multiple steps individually. The task extraction unit also introduces natural language processing technology that allows the generation AI to analyze the context of the request and break down the tasks into smaller tasks. For example, it identifies the subject and object of the request and breaks down the tasks based on that. This improves the accuracy of the tasks by understanding the detailed context of the request and breaking it down into smaller tasks.

[0065] The task extraction unit can group related tasks based on the relevance of the request content. For example, the task extraction unit constructs a system in which the generation AI analyzes the content of a work request email and automatically groups related tasks. For example, tasks related to the same project are grouped together. The task extraction unit also develops an algorithm for grouping tasks taking into account the relevance of the request content. For example, it evaluates the relevance based on keywords and phrases in the request content. The task extraction unit also introduces natural language processing technology so that the generation AI can analyze the relevance of the request content and group related tasks. For example, it understands the context of the request content and automatically classifies related tasks. This makes task management easier by taking into account the relevance of the request content and grouping related tasks.

[0066] The task extraction unit can analyze the requester's emotions and prioritize emotionally significant tasks. For example, the task extraction unit develops an algorithm in which a generation AI analyzes the content of a work request email and estimates the requester's emotions. For example, it calculates an emotion score from the email's writing style and expressions. The task extraction unit also uses the emotion estimation function to build a system that prioritizes the extraction and classification of tasks that strongly reflect the requester's emotions. For example, tasks with high emotion scores are automatically set as high priority. The task extraction unit also learns past emotion data to analyze the requester's emotions and identify emotionally significant tasks. For example, it prioritizes emails from requesters who have made many emotional requests in the past. This improves requester satisfaction by analyzing the requester's emotions and prioritizing the extraction and classification of emotionally significant tasks.

[0067] The task extraction unit can classify tasks from sources other than work request emails. For example, the task extraction unit builds a system in which the generation AI extracts and classifies tasks from internal chats and project management tools. For example, it analyzes chat conversation content and project progress information. The task extraction unit also develops algorithms to extract tasks from sources other than work request emails. For example, it integrates data from different sources and manages it centrally. The task extraction unit also introduces natural language processing technology to enable the generation AI to extract and classify tasks from internal chats and project management tools. For example, it understands the context of chats and automatically classifies tasks. This expands the scope of task management by extracting and classifying tasks from sources other than work request emails.

[0068] The task extraction unit can convert the extraction and classification results into visual notes or mind maps to facilitate visual understanding. For example, the task extraction unit can build a system that converts tasks extracted and classified by the generation AI into visual notes. For example, it can show the key points of the tasks using diagrams or icons. The task extraction unit can also convert extracted and classified tasks into mind map format, visually organizing related keywords and concepts. This allows users to understand the overall picture of the task at a glance. The task extraction unit can also develop tools that automatically generate visual notes and mind maps, allowing users to easily visually display task summaries. For example, it can provide a function for visualizing tasks using drag and drop. By converting the extraction and classification results into visual notes or mind maps, tasks can be more easily understood.

[0069] The task extraction unit uses the emotion estimation function to collect users' emotional responses to extracted and classified tasks and improve classification accuracy based on that data. For example, the task extraction unit collects users' emotional responses to tasks extracted and classified by the generation AI in real time and uses that data to improve classification accuracy. For example, it prioritizes classification of tasks with a high number of positive responses. The task extraction unit also uses the emotion estimation function to collect feedback on extracted and classified tasks and readjusts the classification algorithm if there are a high number of negative responses. The task extraction unit also analyzes users' emotional response data and identifies areas for improvement in classification based on the results. For example, it suggests correcting areas with low emotional scores. This allows for more accurate task management by using the emotion estimation function to collect users' emotional responses to extracted and classified tasks and improve classification accuracy based on that feedback.

[0070] When tracking task progress, the task management unit can predict task completion and send reminders based on the prediction. For example, the task management unit develops an algorithm that allows the generation AI to track task progress and predict completion. For example, it predicts the time until completion based on past task data. The task management unit also builds a system that automatically sends task reminders based on the completion prediction. For example, it automatically sends reminders for tasks whose deadlines are approaching. The task management unit also collects data that the generation AI uses to monitor task progress in real time and make completion predictions. For example, it regularly updates task progress. This allows the generation AI to track task progress, predict completion, and send reminders based on the prediction, thereby improving the task completion rate.

[0071] The task management unit can refer to the communication history between the requester and the person in charge and identify factors that affect the progress of a task. For example, the task management unit builds a system in which a generation AI analyzes the communication history between the requester and the person in charge and identifies factors that affect the progress of a task. For example, it analyzes the content of emails and chats. The task management unit also develops an algorithm that identifies factors that affect the progress of a task based on the communication history. For example, it identifies tasks that require frequent communication. The task management unit also introduces natural language processing technology that allows the generation AI to refer to the communication history between the requester and the person in charge and identify factors that affect the progress of a task. For example, it analyzes the tone and content of the communication. This makes task progress management more accurate by referring to the communication history between the requester and the person in charge and identifying factors that affect the progress of a task.

[0072] The task management unit uses the emotion estimation function to analyze the emotions of the person in charge regarding the progress of the task and can provide reminders that emotionally increase motivation. For example, the task management unit develops an algorithm in which the generation AI tracks the progress of the task and estimates the emotions of the person in charge. For example, it analyzes facial expressions and voice when reporting task progress. The task management unit also uses the emotion estimation function to build a system that analyzes the emotions of the person in charge and provides reminders that emotionally increase motivation. For example, it automatically sends positive messages. The task management unit also monitors the emotions of the person in charge in real time and provides reminders that emotionally increase motivation. For example, it adjusts the content of the reminder according to the emotion score. As a result, the emotion estimation function is used to analyze the emotions of the person in charge regarding the progress of the task and provides reminders that emotionally increase motivation, thereby improving the motivation of the person in charge.

[0073] The task management unit can link with other project management tools and calendar apps to achieve integrated task management. For example, the task management unit builds a system in which the generation AI links the task management system with other project management tools and calendar apps. For example, it synchronizes data using an API. The task management unit also develops an interface to link with other project management tools and calendar apps to achieve integrated task management. For example, it displays task progress in a unified manner. The task management unit also builds a system in which the generation AI links with other project management tools and calendar apps to update task progress in real time. For example, it ensures that changes to tasks are reflected immediately. This improves the efficiency of task management by linking with other project management tools and calendar apps to achieve integrated task management.

[0074] The task management unit can visualize task progress using dashboards and graphs to enable intuitive understanding. For example, the task management unit builds a system in which the generation AI visualizes task progress using dashboards and graphs. For example, it displays the task progress rate and completion status in graphs. The task management unit also develops a dashboard to visualize task progress so that users can understand it intuitively. For example, it displays task priorities and deadlines using different colors. The task management unit also builds a system in which the generation AI updates task progress in real time and reflects it on dashboards and graphs. For example, it periodically collects task progress data and visualizes it. As a result, task progress can be visualized using dashboards and graphs, making it possible to intuitively understand task progress.

[0075] The task management unit uses the emotion estimation function to monitor the user's emotional response to task progress in real time and provide optimal reminders. For example, the task management unit develops an algorithm in which the generation AI tracks task progress and estimates the user's emotions. For example, it analyzes facial expressions and voice when reporting task progress. The task management unit also uses the emotion estimation function to build a system that monitors the user's emotions in real time and provides optimal reminders. For example, it adjusts the content of reminders according to the emotion score. The task management unit also uses the generation AI to monitor the user's emotions in real time and provide reminders to emotionally increase motivation. For example, it automatically sends positive messages. As a result, the emotion estimation function is used to monitor the user's emotional response to task progress in real time and provide optimal reminders, thereby improving the user's motivation.

[0076] The task management unit takes task dependencies into account and can simultaneously manage the progress of dependent tasks. For example, the task management unit constructs a system in which the generation AI analyzes task dependencies and simultaneously manages the progress of dependent tasks. For example, it updates the completion status of dependent tasks in real time. The task management unit also develops an algorithm for taking task dependencies into account and simultaneously managing the progress of dependent tasks. For example, it sends reminders when the progress of dependent tasks is behind schedule. The task management unit also introduces natural language processing technology that allows the generation AI to analyze task dependencies and simultaneously manage the progress of dependent tasks. For example, it automatically identifies the relationships between dependent tasks. This allows task dependency to be taken into account and the progress of dependent tasks to be managed simultaneously, resulting in more accurate task progress management.

[0077] The task management unit can evaluate the workload of each person in charge and suggest redistribution of tasks if the workload is high. For example, the task management unit builds a system in which the generation AI tracks the progress of tasks and evaluates the workload of each person in charge. For example, it calculates the workload based on the number of tasks and progress of each person in charge. The task management unit also develops an algorithm that suggests redistribution of tasks when the workload is high. For example, it suggests allocating tasks to another person in charge. The task management unit also builds a system in which the generation AI monitors the workload of each person in charge in real time and suggests redistribution of tasks if the workload is high. For example, it automatically redistributes tasks if the workload is above a certain level. This allows the workload to be balanced by evaluating the workload of each person in charge and suggesting redistribution of tasks if the load is high.

[0078] The task management unit uses the emotion estimation function to analyze the emotions of the person in charge regarding the progress of the task and can make suggestions to emotionally reduce stress. For example, the task management unit develops an algorithm in which the generation AI tracks the progress of the task and estimates the emotions of the person in charge. For example, it analyzes facial expressions and voice when reporting task progress. The task management unit also uses the emotion estimation function to build a system that analyzes the emotions of the person in charge and makes suggestions to emotionally reduce stress. For example, it automatically sends advice on how to relax. The task management unit also monitors the emotions of the person in charge in real time and makes suggestions to emotionally reduce stress. For example, it sends reminders to reduce stress based on the emotion score. As a result, the person in charge's stress is reduced by using the emotion estimation function to analyze the emotions of the person in charge regarding the progress of the task and making suggestions to emotionally reduce stress.

[0079] The task management unit can share task progress with other project members, enabling progress management across the entire team. For example, the task management unit builds a system in which the generation AI shares task progress with other project members. For example, it updates the progress in real time so that everyone can access it. The task management unit also develops an interface to enable progress management across the entire team. For example, it provides a dashboard that centrally displays task progress. The task management unit also builds a system in which the generation AI updates task progress in real time and shares it with other project members. For example, it ensures that changes to tasks are reflected immediately. This allows task progress to be shared with other project members, enabling progress management across the entire team, promoting team cooperation.

[0080] The task management unit can report the task progress status in real time through a voice assistant, enabling hands-free progress confirmation. For example, the task management unit builds a system in which the generation AI reports the task progress status in real time through a voice assistant. For example, the voice assistant reads out the task progress status. The task management unit also develops a voice assistant that enables hands-free progress confirmation. For example, it makes it possible to check the task progress status using voice commands. The task management unit also builds a system in which the generation AI updates the task progress status in real time and reports it through the voice assistant. For example, it makes it possible to instantly reflect changes to the task in the voice assistant. This improves work efficiency by reporting the task progress status in real time through the voice assistant and enabling hands-free progress confirmation.

[0081] The task management unit uses the emotion estimation function to analyze the user's emotional response to task progress and provide progress reports that are easy to empathize with. For example, the task management unit develops an algorithm in which the generation AI tracks task progress and estimates the user's emotions. For example, it analyzes facial expressions and voice when reporting task progress. The task management unit also uses the emotion estimation function to build a system in which the generation AI monitors the user's emotions in real time and provides progress reports that are easy to empathize with. For example, it adjusts the content of the report according to the emotion score. The task management unit also uses the generation AI to monitor the user's emotions in real time and provide progress reports that are easy to empathize with. For example, it automatically sends positive messages. As a result, the emotion estimation function is used to analyze the user's emotional response to task progress and provide progress reports that are easy to empathize with, thereby improving user satisfaction.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The work request email analysis unit can automatically evaluate the urgency or importance of the request content and set priorities. For example, an algorithm is developed in which the generation AI analyzes work request emails and evaluates the urgency and importance of the request content. The urgency is determined based on keywords and phrases contained in the request content. A system is also built to evaluate the urgency and importance of the request content and set priorities. Tasks are automatically sorted so that requests with high urgency are given priority. To evaluate the urgency and importance of the request content, the requester's instructions and past request history are referenced. Requests from specific requesters are always treated as high priority. This allows important tasks to be processed first by evaluating the urgency and importance of the request content and setting priorities.

[0084] The work request email analysis unit can analyze the emotions of the requester and prioritize emotionally important requests. The generation AI analyzes work request emails and develops an algorithm to estimate the requester's emotions. An emotion score is calculated from the email's writing style and expressions. The emotion estimation function is used to build a system that prioritizes requests that strongly express the requester's emotions. Requests with a high emotion score are automatically set as high priority. Past emotion data is learned to analyze the requester's emotions and identify emotionally important requests. Emails from requesters who have made many emotional requests in the past are prioritized. This improves requester satisfaction by analyzing the requester's emotions and prioritizing the processing of emotionally important requests.

[0085] The work request email analysis unit can also handle requests made via voice input or chatbots. A system will be built in which the generation AI analyzes voice input and automatically extracts and classifies tasks in the same way as work request emails. Voice data will be converted into text using voice recognition technology. A system will be developed that automatically processes work requests received through chatbots. The content of the chatbot's conversation will be analyzed and the necessary tasks will be extracted. A system will be built that integrates voice input and requests from chatbots with work request emails and manages them centrally. Requests in different input formats will be saved in the same database. This will enable requests made via voice input and chatbots to be handled, making it possible to respond to a variety of request methods.

[0086] The work request email analysis unit can handle work request emails in different languages, making it possible to build a multilingual automatic processing system. A generation AI will automatically translate work request emails written in different languages ​​and develop a system that extracts and classifies tasks. A translation API will be used to convert the email content into Japanese. A multilingual work request email processing system will be built and requests in different languages ​​will be managed centrally. It will support multiple languages ​​such as English, French, and Chinese. A multilingual algorithm will be developed to analyze work request emails in different languages ​​and extract tasks. Analysis will be performed taking into account the grammar and expressions of each language. This will allow work request emails in different languages ​​to be handled, making it possible to handle international work requests.

[0087] The work request email analysis unit can analyze the emotions of the requester when they enter their email in real time, and make suggestions that will elicit positive emotions. We will build a system in which the generation AI analyzes the emotions of the requester when they enter their work request email in real time, and makes suggestions that will elicit positive emotions. The system analyzes the requester's facial expressions and voice. The emotion estimation function is used to monitor the emotions of the requester when they enter their email in real time, and displays messages to elicit positive emotions. Words of encouragement and success stories are presented. We will develop a system that analyzes the emotions of the requester and provides an interface to elicit positive emotions. The interface design is changed according to the emotion score. This will allow the system to analyze the emotions of the requester when they enter their email in real time, and make suggestions that will elicit positive emotions, thereby improving requester satisfaction.

[0088] The task extraction unit can understand the detailed context of the request and break it down into smaller tasks. The generation AI analyzes the context of the work request email and develops an algorithm to understand the request in detail. It breaks down the tasks taking into account the background information and purpose of the request. It builds a system that understands the detailed context of the request and breaks it down into smaller tasks. It individually extracts tasks that are divided into multiple steps. The generation AI analyzes the context of the request and introduces natural language processing technology to break down the tasks. It identifies the subject and object of the request and breaks down the tasks based on that. This improves the accuracy of the tasks by understanding the detailed context of the request and breaking it down into smaller tasks.

[0089] The task extraction unit can group related tasks based on the relevance of the request content. A system will be built in which the generation AI will analyze the content of work request emails and automatically group related tasks. Tasks related to the same project will be grouped together. An algorithm will be developed to group tasks taking into account the relevance of the request content. Relevance will be evaluated based on keywords and phrases in the request content. Natural language processing technology will be introduced so that the generation AI can analyze the relevance of the request content and group related tasks. The context of the request content will be understood and related tasks will be automatically classified. This will make task management easier by taking into account the relevance of the request content and grouping related tasks.

[0090] The task extraction unit can analyze the requester's emotions and prioritize emotionally important tasks. The generation AI analyzes the content of work request emails and develops an algorithm to estimate the requester's emotions. An emotion score is calculated from the email's writing style and expressions. Using the emotion estimation function, a system is built to prioritize the extraction and classification of tasks that strongly express the requester's emotions. Tasks with high emotion scores are automatically set as high priority. Past emotion data is learned to analyze the requester's emotions and identify emotionally important tasks. Emails from requesters who have made many emotional requests in the past are prioritized. This allows the system to analyze the requester's emotions and prioritize the extraction and classification of emotionally important tasks, thereby improving requester satisfaction.

[0091] The task extraction unit can classify tasks from sources other than work request emails. A system will be built in which the generation AI extracts and classifies tasks from internal chats and project management tools. Chat conversation content and project progress information will be analyzed. An algorithm will be developed to extract tasks from sources other than work request emails. Data from different sources will be integrated and managed centrally. Natural language processing technology will be introduced to enable the generation AI to extract and classify tasks from internal chats and project management tools. The context of the chat will be understood and tasks will be automatically classified. This will expand the scope of task management by allowing tasks to be extracted and classified from sources other than work request emails.

[0092] When tracking task progress, the task management unit can predict task completion and send reminders based on the prediction. The generation AI tracks task progress and develops an algorithm to predict completion. It predicts the time until completion based on past task data. It builds a system to automatically send task reminders based on the completion prediction. It automatically sends reminders for tasks with approaching deadlines. The generation AI monitors task progress in real time and collects data to predict completion. It regularly updates task progress. As a result, by tracking task progress, predicting completion, and sending reminders based on the prediction, task completion rates improve.

[0093] The task management department uses the emotion estimation function to analyze the emotions of the person in charge regarding the progress of the task, and can send reminders that emotionally increase motivation. The generation AI tracks the progress of the task and develops an algorithm to estimate the emotions of the person in charge. It analyzes facial expressions and voice when reporting task progress. It uses the emotion estimation function to analyze the emotions of the person in charge and builds a system that sends reminders that emotionally increase motivation. It automatically sends positive messages. The generation AI monitors the emotions of the person in charge in real time and sends reminders to emotionally increase motivation. It adjusts the content of the reminder according to the emotion score. As a result, the emotion estimation function is used to analyze the emotions of the person in charge regarding the progress of the task, and reminders that emotionally increase motivation are sent, thereby improving the motivation of the person in charge.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The task request email analysis unit analyzes the content of the task request email. For example, it uses natural language processing technology to analyze the content of the email and extract the necessary information. It can also use keyword extraction technology to extract important keywords from the email. It can also use context analysis technology to analyze the content of the email based on the context. For example, it can automatically recognize tasks such as "requesting the addition of a new line" or "requesting changes to an existing line." Step 2: The task extraction unit automatically extracts and classifies the necessary tasks from the work request emails analyzed by the work request email analysis unit. For example, it can extract tasks from the email content using a machine learning algorithm. It can also classify tasks using rule-based classification techniques. It can also classify the extracted tasks into project tasks, daily work tasks, urgent tasks, etc. Step 3: The task management section automatically manages the tasks extracted by the task extraction section and tracks their progress. For example, it periodically checks the progress of tasks and automatically reminds users of incomplete tasks or tasks with approaching deadlines. It can also automatically set task priorities so that important tasks are handled first. It can also visualize task progress so that managers can understand it at a glance. For example, it can display task progress in graphs and charts.

[0096] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0097] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 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.

[0101] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0102] 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.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0106] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0107] 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.

[0108] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 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.

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

[0117] 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.

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

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

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

[0121] 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.

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

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0124] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0126] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0137] 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.

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

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0140] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0142] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0144] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0145] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0146] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0147] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0148] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0149] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0150] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0151] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0152] 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.

[0153] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0154] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0155] 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.

[0156] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0157] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0158] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0159] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0160] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0161] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0162] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0163] 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 business request email analysis unit that analyzes the content of the business request email; a task extraction unit that automatically extracts and classifies necessary tasks from the work request email analyzed by the work request email analysis unit; a task management unit that automatically manages the tasks extracted by the task extraction unit and tracks their progress. A system characterized by:

2. The work request email analysis unit Respond to requests via voice input or chatbots 2. The system of claim 1.

3. The task extraction unit Understand the detailed context of the request and break it down into smaller tasks 2. The system of claim 1.

4. The task management unit When tracking the progress of the task, a completion prediction for the task is made and a reminder is given based on the prediction.

2. The system of claim 1.

5. The work request email analysis unit Analyze the client's emotions and prioritize emotionally important requests 2. The system of claim 1.

Images