system

Abstract

The system according to this embodiment aims to efficiently analyze the content of emails and chats and determine the priority of tasks. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a decision unit, a presentation unit, and a readjustment unit. The collection unit collects the contents of emails and chats. The analysis unit analyzes the contents collected by the collection unit. The decision unit determines priorities based on the analysis results obtained by the analysis unit. The presentation unit presents the priorities determined by the decision unit to a human. The readjustment unit readjusts tasks based on the priorities presented by the presentation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to efficiently analyze the content of mails and chats and determine the priority of tasks.

[0005] The system according to the embodiment aims to efficiently analyze the content of mails and chats and determine the priority of tasks.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a decision unit, a presentation unit, and a readjustment unit. The collection unit collects the contents of emails and chats. The analysis unit analyzes the contents collected by the collection unit. The decision unit determines priorities based on the analysis results obtained by the analysis unit. The presentation unit presents the priorities determined by the decision unit to a human. The readjustment unit readjusts tasks based on the priorities presented by the presentation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently analyze the content of emails and chats and determine the priority of tasks. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The business efficiency system according to an embodiment of the present invention is a system that transfers emails, chats, and communication tools to a server capable of AI analysis, where the AI ​​analyzes the content. The business efficiency system is based on the idea that if the user determines the priority of what needs to be done first, and the user completes the tasks one by one as instructed by the AI, the work will be completed smoothly. For example, the business efficiency system transfers emails, chats, and messages from communication tools to a server capable of AI analysis. Next, the business efficiency system has the AI ​​read these messages and analyze their content. The AI ​​determines the importance and urgency of each message and determines the priority. For example, instructions from a supervisor or urgent requests from a client are set as high priorities. Next, the business efficiency system has the AI ​​create a list of tasks based on the priorities and present it to the user. The user completes the tasks one by one according to this list. This allows the user to leave the planning of the work to the AI ​​and proceed with work efficiently. This mechanism reduces the time spent determining work priorities and improves business efficiency. For example, it eliminates the need to check the contents of each email or chat and wonder which one to tackle first. Furthermore, because AI determines priorities, important tasks are not overlooked and are processed reliably. In addition, the business efficiency system monitors the progress of tasks in real time and readjusts priorities as needed. For example, if a new urgent task arises, the business efficiency system immediately re-evaluates the priorities and notifies the human. This ensures that tasks are always carried out in the optimal order. In this way, by utilizing AI, it is possible to improve business efficiency by having AI take over the task of determining work priorities. This is a particularly useful tool for business people who often struggle to decide where to start with their increasingly complex daily tasks. As a result, the business efficiency system automatically determines the priorities of tasks and improves business efficiency.

[0029] The business efficiency system according to the embodiment comprises a collection unit, an analysis unit, a decision unit, a presentation unit, and a readjustment unit. The collection unit collects the contents of emails and chats. The collection unit obtains messages from, for example, an email server or a chat application. The collection unit can also collect messages using the API of a communication tool. For example, the collection unit obtains emails from an email server using the IMAP protocol. The collection unit can also obtain messages using the API of a chat application. The collection unit can also collect messages using the API of a communication tool. The analysis unit analyzes the contents collected by the collection unit. The analysis unit analyzes the contents of messages using, for example, natural language processing technology. The analysis unit can also determine the importance and urgency of messages using machine learning algorithms. For example, the analysis unit analyzes the contents of messages using natural language processing technology and extracts keywords. The analysis unit can also determine the importance and urgency of messages using machine learning algorithms. The analysis unit can also analyze the contents of messages and determine their importance and urgency. The decision unit determines priorities based on the analysis results obtained by the analysis unit. The decision unit determines the priority of tasks based on factors such as importance and urgency. The decision unit can also use AI to determine priorities. The presentation unit presents the priorities determined by the decision unit to the user. The presentation unit can, for example, generate a task list and display it to the user. The presentation unit can also notify the user of the priorities using a notification function. The presentation unit can also present the priorities to the user. The readjustment unit readjusts tasks based on the priorities presented by the presentation unit. The readjustment unit reviews priorities, for example, when new urgent tasks arise. The readjustment unit can also readjust priorities using AI.For example, the reprioritization unit reviews priorities when new urgent tasks arise. The reprioritization unit can also reprioritize priorities using AI. The reprioritization unit can also reprioritize tasks. As a result, the business efficiency system according to this embodiment improves business efficiency by collecting, analyzing, prioritizing, presenting, and reprioritizing the contents of emails and chats.

[0030] The data collection unit collects email and chat content. For example, it retrieves messages from email servers and chat applications. Specifically, it can retrieve emails from email servers using the IMAP protocol. The IMAP protocol is a standard protocol for efficiently retrieving email header information and body text, allowing the data collection unit to retrieve all emails in a user's inbox in real time. The data collection unit can also retrieve messages using chat application APIs. Furthermore, using these APIs, the data collection unit can simultaneously retrieve message metadata (sender, recipient, date and time sent, etc.). This allows the data collection unit to collect not only the content of emails and chats, but also contextual information about the messages. The data collection unit centrally manages this data and stores it in cloud storage or databases for access by the analytics unit and other departments. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes the content collected by the data collection department. For example, the analysis department uses natural language processing (NLP) technology to analyze the content of messages. Natural language processing technology is a technique for analyzing text data and understanding its meaning and sentiment, allowing the analysis department to analyze the content of messages in detail. Specifically, it uses morphological analysis to divide messages into individual words and identify the part of speech and meaning of each word. It can also use keyword extraction algorithms to extract important keywords from messages. Furthermore, it can use sentiment analysis algorithms to determine the emotional tone (positive, negative, neutral) of messages. Based on these analysis results, the analysis department uses machine learning algorithms to determine the importance and urgency of messages. For example, it can use a model trained on past message data to predict the importance and urgency of new messages. This allows the analysis department to analyze collected data quickly and accurately, contributing to increased operational efficiency. In addition, the analysis department can visualize the analysis results and provide them in a format that is easy for other departments and users to understand. This allows the analysis department to not only analyze data but also provide support for effectively utilizing the results.

[0032] The decision-making unit determines priorities based on the analysis results obtained by the analysis unit. For example, the decision-making unit determines task priorities based on importance and urgency. Specifically, it uses an algorithm that calculates the priority of each task based on the importance and urgency scores provided by the analysis unit. For example, tasks that are both highly important and urgent are given the highest priority, while tasks that are both highly important and urgent are postponed. The decision-making unit can also use AI to determine priorities. The AI ​​can learn the processing history of past tasks and the user's behavior patterns to suggest the optimal priorities. For example, it can learn what tasks a particular user has prioritized in the past and determine the priority of new tasks based on that pattern. This allows the decision-making unit to set priorities flexibly to match the user's work style. Furthermore, it is important for the decision-making unit to make the priority determination process transparent so that users can understand the reasons behind the decisions. This allows users to trust the decision-making unit's judgments and effectively utilize the system.

[0033] The presentation unit presents the priorities determined by the decision unit to the user. For example, the presentation unit generates a task list and displays it to the user. Specifically, it visually displays a task list based on priority through the user interface. The task list is color-coded according to importance and urgency, allowing the user to grasp the priorities at a glance. The presentation unit can also notify the user of priorities using a notification function. For example, it can use the notification function of a smartphone or desktop to notify the user in real time when an important task arises. Furthermore, the presentation unit can use a voice assistant or chatbot to guide the user through task priorities via voice or text. This allows the presentation unit to support the user in efficiently managing tasks and smoothly carrying out their work. In addition, the presentation unit can collect user feedback and use it to improve the presentation method. For example, it can collect information such as how users prefer to display task lists and when they want to receive notifications, and reflect this in the system customization. This allows the presentation unit to respond flexibly to user needs and improve the overall usability of the system.

[0034] The reordering unit readjusts tasks based on the priorities presented by the presentation unit. For example, the reordering unit reviews priorities when a new urgent task arises. Specifically, it uses an algorithm that dynamically updates the existing task list based on new data collected in real time. For example, if a new task with high urgency is added, the reordering unit sets that task as the highest priority and recalculates the priorities of other tasks. The reordering unit can also readjust priorities using AI. The AI ​​can learn past task processing history and user behavior patterns to suggest the optimal readjustment method. For example, if a particular task is taking longer than expected, the AI ​​can suggest lowering the priority of that task and processing other tasks first. This allows the reordering unit to achieve flexible task management to maximize the user's work efficiency. Furthermore, it is important for the reordering unit to continuously improve its readjustment algorithm based on user feedback. This allows the reordering unit to always provide optimal task management based on the latest information and user needs, supporting improved work efficiency.

[0035] The decision unit can determine the importance and urgency of each message using AI. For example, the decision unit can analyze the content of a message using natural language processing technology to determine its importance. The decision unit can also determine the urgency of a message using machine learning algorithms. For example, the decision unit can analyze the content of a message using natural language processing technology to determine its importance. The decision unit can also determine the urgency of a message using machine learning algorithms. The decision unit can also analyze the content of a message to determine its importance and urgency. This improves the accuracy of prioritization by having AI determine the importance and urgency of each message. Importance is evaluated based on, for example, the content of the message and the sender's position. Urgency is evaluated based on, for example, the deadline for response and the scope of impact. Some or all of the above processing in the decision unit may be performed using, for example, a generative AI, or without a generative AI. For example, the decision unit can input the content of a message into a generative AI and have the generative AI perform the determination of importance and urgency.

[0036] The presentation unit can list tasks based on priority and present them to a human. For example, the presentation unit can generate a task list and display it to the user. The presentation unit can also notify the user of the priority using a notification function. For example, the presentation unit can generate a task list and display it to the user. The presentation unit can also notify the user of the priority using a notification function. The presentation unit can also present the priority to the user. This makes task management easier by listing tasks based on priority and presenting them to a human. The listing is done based on, for example, the format of the list and the order of the items. Some or all of the above processing in the presentation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the presentation unit can have a generation AI perform the generation of the task list.

[0037] The readjustment unit can review priorities and notify humans when new urgent tasks arise. For example, the readjustment unit reviews priorities when new urgent tasks arise. The readjustment unit can also notify users of changes in priorities using a notification function. For example, the readjustment unit reviews priorities when new urgent tasks arise. The readjustment unit can also notify users of changes in priorities using a notification function. The readjustment unit can also review priorities and notify humans. This ensures that tasks are always carried out in the optimal order by reviewing priorities and notifying humans when new urgent tasks arise. Urgent tasks are determined based on factors such as deadlines and scope of impact. Some or all of the above-described processes in the readjustment unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the readjustment unit can input the occurrence of an urgent task into the generation AI and have the generation AI perform a review of priorities.

[0038] The analysis unit can read and analyze the content of emails and chats using AI. For example, the analysis unit can analyze message content using natural language processing technology. The analysis unit can also determine the importance and urgency of messages using machine learning algorithms. For example, the analysis unit can analyze message content using natural language processing technology and extract keywords. The analysis unit can also determine the importance and urgency of messages using machine learning algorithms. The analysis unit can also analyze message content and determine its importance and urgency. This improves the accuracy of the analysis by having AI read and analyze the content of emails and chats. Reading is performed based on, for example, natural language processing technology or text analysis algorithms. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input message content into a generative AI and have the generative AI perform content analysis.

[0039] The collection unit can collect messages from email, chat, and communication tools. For example, the collection unit can retrieve messages from email servers or chat applications. It can also collect messages using the APIs of communication tools. For example, the collection unit can retrieve emails from email servers using the IMAP protocol. It can also retrieve messages using the APIs of chat applications. This allows for centralized information management by collecting messages from email, chat, and communication tools. Some or all of the above-described processes in the collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the collection unit can have a generative AI perform the message collection.

[0040] The collection unit can analyze the user's past message collection history and select the optimal collection method. For example, the collection unit may prioritize collection methods that the user has frequently used in the past (email, chat, etc.). The collection unit can also suggest the optimal collection method for a specific time period based on the user's past collection history. For example, the collection unit analyzes the user's past collection history and selects the most efficient collection method. This allows the optimal collection method to be selected by analyzing the user's past message collection history. The optimal collection method is selected based on factors such as collection frequency and collection target. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the collection unit can input past collection history into a generative AI and have the generative AI select the optimal collection method.

[0041] The collection unit can filter messages based on the user's current projects and areas of interest when collecting them. For example, the collection unit can prioritize collecting messages related to the user's current projects. The collection unit can also filter and collect relevant messages based on the user's areas of interest. For example, the collection unit can collect necessary messages according to the progress of the user's current projects. This allows for the priority collection of highly relevant messages by filtering based on the user's current projects and areas of interest. Filtering can be performed based on, for example, keyword filtering or category filtering. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the collection unit can input project and area of ​​interest information into a generative AI and have the generative AI perform the filtering.

[0042] The collection unit can prioritize collecting highly relevant messages based on the user's geographical location information when collecting messages. For example, if the user is in a specific region, the collection unit will prioritize collecting messages related to that region. The collection unit can also prioritize collecting messages related to locations close to the user's current location. For example, the collection unit can filter and collect highly relevant messages based on the user's geographical location information. This allows for efficient collection of region-related information by prioritizing the collection of highly relevant messages based on the user's geographical location information. Geographical location information is obtained, for example, based on GPS data or IP addresses. Some or all of the above processing in the collection unit may be performed using, for example, a generation AI, or without a generation AI. For example, the collection unit can input geographical location information into a generation AI and have the generation AI collect highly relevant messages.

[0043] The collection unit can analyze a user's social media activity and collect relevant messages when collecting messages. For example, the collection unit can prioritize collecting messages related to topics the user has shown interest in on social media. The collection unit can also filter and collect relevant messages based on the user's social media activity history. For example, the collection unit can prioritize collecting messages related to accounts the user follows on social media. This allows for efficient collection of relevant messages by analyzing the user's social media activity. Social media activity is analyzed based on, for example, the content of posts and the number of likes. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the collection unit can input social media activity data into a generative AI and have the generative AI collect relevant messages.

[0044] The analysis unit can adjust the level of detail of its analysis based on the importance of the messages. For example, the analysis unit can perform a detailed analysis on high-importance messages and a concise analysis on low-importance messages. For instance, the analysis unit can adjust the level of detail of its analysis in stages according to the importance of the messages. This allows for efficient analysis by adjusting the level of detail based on the importance of the messages. Importance is evaluated based on factors such as the content of the message and the sender's position. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the importance of the messages into the generative AI and have the generative AI adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the message category during analysis. For example, the analysis unit can apply a business-specific analysis algorithm to business-related messages. It can also apply a private-specific analysis algorithm to private messages. For instance, the analysis unit selects and applies the most suitable analysis algorithm based on the message category. This improves the accuracy of the analysis by applying different analysis algorithms depending on the message category. Categories are classified based on, for example, business categories or technology categories. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input message category information into a generative AI and have the generative AI apply the most suitable analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the message sending date during the analysis process. For example, the analysis unit may prioritize the analysis of recently sent messages. It can also postpone the analysis of older messages. For example, the analysis unit may adjust the analysis priority in stages according to the message sending date. This enables efficient analysis by determining the analysis priority based on the message sending date. The sending date is obtained, for example, based on a timestamp or sending history. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the message sending date into a generative AI and have the generative AI determine the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of messages during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant messages. It can also postpone the analysis of less relevant messages. For example, the analysis unit may adjust the order of analysis in stages according to the relevance of messages. This allows for efficient analysis by adjusting the order of analysis based on the relevance of messages. Relevance is evaluated based on, for example, common keywords or matching topics. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the relevance of messages into a generative AI and have the generative AI adjust the order of analysis.

[0048] The decision-making unit can determine priorities by considering the importance and urgency of messages in combination. For example, the decision-making unit may prioritize messages that are both highly important and highly urgent. It can also postpone messages that are both less important and less urgent. For example, the decision-making unit may determine priorities in stages by considering the balance between importance and urgency. This allows for the determination of the optimal priority by considering the importance and urgency of messages in combination. Importance is evaluated based on, for example, the content of the message or the sender's position. Urgency is evaluated based on, for example, the deadline for response or the scope of impact. Some or all of the above processing in the decision-making unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the decision-making unit can input the importance and urgency of messages into a generation AI and have the generation AI perform the priority determination.

[0049] The decision-making unit can determine priority by considering the attribute information of the message sender. For example, the decision-making unit may prioritize messages from a superior. It can also prioritize messages from a client. For example, the decision-making unit may determine priority by considering the sender's job title and relationship. This allows important messages to be processed preferentially by considering the sender's attribute information. Attribute information is obtained based on, for example, job title and department. Some or all of the above processing in the decision-making unit may be performed using, for example, a generative AI, or without a generative AI. For example, the decision-making unit can input the sender's attribute information into a generative AI and have the generative AI perform the priority determination.

[0050] The decision-making unit can consider the geographical distribution of messages when determining priority. For example, if a user is in a specific region, the decision-making unit will prioritize messages related to that region. It can also prioritize messages if the sender is nearby. For example, the decision-making unit can determine priority in stages based on the geographical distribution of messages. This allows for priority processing of important messages related to a region by considering the geographical distribution of messages. Geographical distribution can be obtained, for example, based on GPS data or IP addresses. Some or all of the above processing in the decision-making unit may be performed using, for example, a generative AI, or without a generative AI. For example, the decision-making unit can input geographical distribution information into a generative AI and have the generative AI perform the priority determination.

[0051] The decision-making unit can improve the accuracy of its prioritization by referring to relevant literature for the message. For example, the decision-making unit can refer to relevant literature for the message and determine the priority. The decision-making unit can also determine the priority based on research papers related to the content of the message. For example, the decision-making unit can analyze relevant literature for the message to improve the accuracy of its priority determination. Thus, the accuracy of priority determination is improved by referring to relevant literature for the message. Relevant literature is referred to, for example, based on database searches or citations. Some or all of the above processing in the decision-making unit may be performed using, for example, a generative AI, or without a generative AI. For example, the decision-making unit can input information on relevant literature into a generative AI and have the generative AI perform the priority determination.

[0052] The display unit can select the optimal display method when displaying the task list by referring to the user's past operation history. For example, the display unit can prioritize display methods previously used by the user. The display unit can also suggest the optimal display method based on the user's past operation history. For example, the display unit analyzes the user's operation history and selects the most efficient display method. This allows the optimal display method to be selected by referring to the user's past operation history. Operation history is obtained based on, for example, click history or operation logs. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit can input operation history information into a generation AI and have the generation AI select the optimal display method.

[0053] The display unit can adjust the level of detail displayed based on the importance of the task when displaying the task list. For example, the display unit can display detailed information for tasks of high importance, and concise information for tasks of low importance. For example, the display unit can adjust the level of detail in stages according to the importance of the task. This allows for efficient task management by adjusting the level of detail based on the importance of the task. Importance is evaluated based on, for example, the content and deadline of the task. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit can input the importance of the task into the generation AI and have the generation AI perform the adjustment of the level of detail in the display.

[0054] The display unit can select the optimal display method when displaying the task list, taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. The display unit can also provide a display method optimized for a larger screen if the user is using a tablet. For example, if the user is using a smartwatch, the display unit provides a concise and highly visible display method. This allows the display unit to provide the optimal display method by considering the user's device information. Device information is obtained based on, for example, the device type and OS version. Some or all of the above processing in the display unit may be performed using, for example, a generative AI, or without a generative AI. For example, the display unit can input device information into a generative AI and have the generative AI select the optimal display method.

[0055] The display unit can adjust the display order of tasks based on their relevance when displaying a task list. For example, the display unit can prioritize the display of highly relevant tasks. It can also display less relevant tasks later. For example, the display unit can adjust the display order in stages according to the relevance of the tasks. This allows for efficient task management by adjusting the display order based on the relevance of tasks. Relevance is evaluated based on, for example, common keywords or matching topics. Some or all of the above processing in the display unit may be performed using, for example, a generative AI, or without a generative AI. For example, the display unit can input the relevance of tasks into a generative AI and have the generative AI perform the adjustment of the display order.

[0056] The readjustment unit can analyze the frequency of new urgent tasks occurring during readjustment to determine the frequency of readjustment. For example, if new urgent tasks occur frequently, the readjustment unit can increase the frequency of readjustment. Conversely, if there are few new urgent tasks, the readjustment unit can decrease the frequency of readjustment. For example, the readjustment unit can adjust the frequency of readjustment in stages according to the frequency of new urgent tasks occurring. This allows the optimal readjustment frequency to be determined by analyzing the frequency of new urgent tasks. Urgent tasks are determined based on factors such as response deadlines and scope of impact. Some or all of the above-described processes in the readjustment unit may be performed using, for example, a generation AI, or without a generation AI. For example, the readjustment unit can input the frequency of urgent tasks into a generation AI and have the generation AI determine the readjustment frequency.

[0057] The readjustment unit can monitor the progress of tasks in real time during readjustment and make readjustments as needed. For example, if a task is behind schedule, the readjustment unit will readjust and revise its priorities. The readjustment unit can also readjust and revise the priorities of other tasks if a task is progressing ahead of schedule. For example, the readjustment unit monitors the progress of tasks in real time and makes readjustments as needed. This allows for readjustments as needed by monitoring the progress of tasks in real time. The progress is obtained based on, for example, the completion rate of tasks or the amount of work time. Some or all of the above processing in the readjustment unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the readjustment unit can input task progress data into a generation AI and have the generation AI perform the readjustment.

[0058] The readjustment unit can perform readjustment while considering the geographical distribution of tasks. For example, if a user is in a specific region, the readjustment unit will prioritize readjusting tasks related to that region. The readjustment unit can also determine the priority of readjustment based on the geographical distribution of tasks. For example, the readjustment unit can adjust the order of readjustment in stages, taking into account the geographical distribution of tasks. This allows for efficient readjustment of region-related tasks by considering the geographical distribution of tasks. Geographical distribution is obtained, for example, based on GPS data or IP addresses. Some or all of the above processing in the readjustment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the readjustment unit can input geographical distribution information into a generative AI and have the generative AI perform the readjustment.

[0059] The readjustment unit can improve the accuracy of readjustment by referring to relevant literature for the task during the readjustment process. For example, the readjustment unit can refer to relevant literature for the task and determine the readjustment priority. The readjustment unit can also determine the readjustment priority based on research papers related to the content of the task. For example, the readjustment unit can analyze relevant literature for the task and improve the readjustment accuracy. Thus, the accuracy of readjustment is improved by referring to relevant literature for the task. Relevant literature is referred to, for example, based on database searches or citations. Some or all of the above processing in the readjustment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the readjustment unit can input information on relevant literature into a generative AI and have the generative AI perform the readjustment.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The business efficiency system can also analyze the user's past work history and determine priorities by referring to past responses to similar tasks. For example, it can analyze how similar tasks were handled in the past and suggest the optimal approach. It can also identify the user's strengths and weaknesses in specific tasks based on past work history and prioritize assigning tasks they excel at. Furthermore, it can evaluate the efficiency of work during specific time periods based on past work history and assign tasks to the most optimal time slots. In this way, by utilizing the user's past work history, more efficient work execution becomes possible.

[0062] The business efficiency system can also monitor the user's current health status and adjust task priorities based on that status. For example, if a user is tired, it can prioritize assigning lower-priority tasks. Conversely, if a user is healthy and energetic, it can prioritize assigning higher-priority tasks. Furthermore, if a user is ill or injured, it can assign less demanding tasks. By adjusting task priorities based on the user's health status, the system reduces the user's burden and enables more efficient work performance.

[0063] The business efficiency system can also monitor users' schedules in real time and adjust task priorities based on those schedules. For example, if a user has a meeting or an important event coming up, it can assign lower-priority tasks to the time slots before and after. It can also assign higher-priority tasks to time slots when the user is free. Furthermore, it can optimize task assignments based on the user's schedule. This allows for more efficient work execution by adjusting task priorities based on the user's schedule.

[0064] The business efficiency system can also analyze the user's past task completion times and provide predicted completion times. For example, it can predict how long a particular task will take to complete based on past data and present this prediction to the user. It can also adjust task priorities based on the predicted completion time. Furthermore, it can optimize the user's schedule based on the predicted completion time. This allows for more efficient work execution by leveraging the user's past task completion times.

[0065] The business efficiency system can also analyze the progress of the user's current projects and adjust the overall project priorities. For example, if a particular project is behind schedule, tasks related to that project can be prioritized. Conversely, if a project is progressing smoothly, tasks related to other projects can be prioritized. Furthermore, it is possible to optimize task assignments based on project progress. This allows for more efficient work execution by adjusting task priorities based on project progress.

[0066] The business efficiency system can also analyze users' past task completion rates and predict task difficulty. For example, it can predict the difficulty level of a particular task based on past data and present it to the user. It can also adjust task priorities based on the predicted difficulty level. Furthermore, it can optimize the user's schedule based on the predicted difficulty level. This enables more efficient work execution by leveraging users' past task completion rates.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The collection unit collects email and chat content. For example, the collection unit can retrieve messages from email servers and chat applications, and collect messages using the communication tool's API. Specifically, the collection unit retrieves emails using the IMAP protocol and retrieves messages using the chat application's API. Step 2: The analysis unit analyzes the content collected by the collection unit. For example, the analysis unit uses natural language processing technology to analyze the message content and machine learning algorithms to determine the importance and urgency of the message. Specifically, the analysis unit extracts keywords and determines the importance and urgency of the message. Step 3: The decision unit determines priorities based on the analysis results obtained by the analysis unit. For example, the decision unit can determine task priorities based on importance and urgency, or it can use AI to determine priorities. Step 4: The presentation unit presents the priorities determined by the decision unit to the user. For example, the presentation unit generates a task list and displays it to the user. It can also notify the user of the priorities using a notification function. Step 5: The rescheduling unit readjusts tasks based on the priorities presented by the presentation unit. For example, if a new urgent task arises, the unit can review the priorities and readjust them using AI.

[0069] (Example of form 2) The business efficiency system according to an embodiment of the present invention is a system that transfers emails, chats, and communication tools to a server capable of AI analysis, where the AI ​​analyzes the content. The business efficiency system is based on the idea that if the user determines the priority of what needs to be done first, and the user completes the tasks one by one as instructed by the AI, the work will be completed smoothly. For example, the business efficiency system transfers emails, chats, and messages from communication tools to a server capable of AI analysis. Next, the business efficiency system has the AI ​​read these messages and analyze their content. The AI ​​determines the importance and urgency of each message and determines the priority. For example, instructions from a supervisor or urgent requests from a client are set as high priorities. Next, the business efficiency system has the AI ​​create a list of tasks based on the priorities and present it to the user. The user completes the tasks one by one according to this list. This allows the user to leave the planning of the work to the AI ​​and proceed with work efficiently. This mechanism reduces the time spent determining work priorities and improves business efficiency. For example, it eliminates the need to check the contents of each email or chat and wonder which one to tackle first. Furthermore, because AI determines priorities, important tasks are not overlooked and are processed reliably. In addition, the business efficiency system monitors the progress of tasks in real time and readjusts priorities as needed. For example, if a new urgent task arises, the business efficiency system immediately re-evaluates the priorities and notifies the human. This ensures that tasks are always carried out in the optimal order. In this way, by utilizing AI, it is possible to improve business efficiency by having AI take over the task of determining work priorities. This is a particularly useful tool for business people who often struggle to decide where to start with their increasingly complex daily tasks. As a result, the business efficiency system automatically determines the priorities of tasks and improves business efficiency.

[0070] The business efficiency system according to the embodiment comprises a collection unit, an analysis unit, a decision unit, a presentation unit, and a readjustment unit. The collection unit collects the contents of emails and chats. The collection unit obtains messages from, for example, an email server or a chat application. The collection unit can also collect messages using the API of a communication tool. For example, the collection unit obtains emails from an email server using the IMAP protocol. The collection unit can also obtain messages using the API of a chat application. The collection unit can also collect messages using the API of a communication tool. The analysis unit analyzes the contents collected by the collection unit. The analysis unit analyzes the contents of messages using, for example, natural language processing technology. The analysis unit can also determine the importance and urgency of messages using machine learning algorithms. For example, the analysis unit analyzes the contents of messages using natural language processing technology and extracts keywords. The analysis unit can also determine the importance and urgency of messages using machine learning algorithms. The analysis unit can also analyze the contents of messages and determine their importance and urgency. The decision unit determines priorities based on the analysis results obtained by the analysis unit. The decision unit determines the priority of tasks based on factors such as importance and urgency. The decision unit can also use AI to determine priorities. The presentation unit presents the priorities determined by the decision unit to the user. The presentation unit can, for example, generate a task list and display it to the user. The presentation unit can also notify the user of the priorities using a notification function. The presentation unit can also present the priorities to the user. The readjustment unit readjusts tasks based on the priorities presented by the presentation unit. The readjustment unit reviews priorities, for example, when new urgent tasks arise. The readjustment unit can also readjust priorities using AI.For example, the reprioritization unit reviews priorities when new urgent tasks arise. The reprioritization unit can also reprioritize priorities using AI. The reprioritization unit can also reprioritize tasks. As a result, the business efficiency system according to this embodiment improves business efficiency by collecting, analyzing, prioritizing, presenting, and reprioritizing the contents of emails and chats.

[0071] The data collection unit collects email and chat content. For example, it retrieves messages from email servers and chat applications. Specifically, it can retrieve emails from email servers using the IMAP protocol. The IMAP protocol is a standard protocol for efficiently retrieving email header information and body text, allowing the data collection unit to retrieve all emails in a user's inbox in real time. The data collection unit can also retrieve messages using chat application APIs. Furthermore, using these APIs, the data collection unit can simultaneously retrieve message metadata (sender, recipient, date and time sent, etc.). This allows the data collection unit to collect not only the content of emails and chats, but also contextual information about the messages. The data collection unit centrally manages this data and stores it in cloud storage or databases for access by the analytics unit and other departments. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0072] The analysis department analyzes the content collected by the data collection department. For example, the analysis department uses natural language processing (NLP) technology to analyze the content of messages. Natural language processing technology is a technique for analyzing text data and understanding its meaning and sentiment, allowing the analysis department to analyze the content of messages in detail. Specifically, it uses morphological analysis to divide messages into individual words and identify the part of speech and meaning of each word. It can also use keyword extraction algorithms to extract important keywords from messages. Furthermore, it can use sentiment analysis algorithms to determine the emotional tone (positive, negative, neutral) of messages. Based on these analysis results, the analysis department uses machine learning algorithms to determine the importance and urgency of messages. For example, it can use a model trained on past message data to predict the importance and urgency of new messages. This allows the analysis department to analyze collected data quickly and accurately, contributing to increased operational efficiency. In addition, the analysis department can visualize the analysis results and provide them in a format that is easy for other departments and users to understand. This allows the analysis department to not only analyze data but also provide support for effectively utilizing the results.

[0073] The decision-making unit determines priorities based on the analysis results obtained by the analysis unit. For example, the decision-making unit determines task priorities based on importance and urgency. Specifically, it uses an algorithm that calculates the priority of each task based on the importance and urgency scores provided by the analysis unit. For example, tasks that are both highly important and urgent are given the highest priority, while tasks that are both highly important and urgent are postponed. The decision-making unit can also use AI to determine priorities. The AI ​​can learn the processing history of past tasks and the user's behavior patterns to suggest the optimal priorities. For example, it can learn what tasks a particular user has prioritized in the past and determine the priority of new tasks based on that pattern. This allows the decision-making unit to set priorities flexibly to match the user's work style. Furthermore, it is important for the decision-making unit to make the priority determination process transparent so that users can understand the reasons behind the decisions. This allows users to trust the decision-making unit's judgments and effectively utilize the system.

[0074] The presentation unit presents the priorities determined by the decision unit to the user. For example, the presentation unit generates a task list and displays it to the user. Specifically, it visually displays a task list based on priority through the user interface. The task list is color-coded according to importance and urgency, allowing the user to grasp the priorities at a glance. The presentation unit can also notify the user of priorities using a notification function. For example, it can use the notification function of a smartphone or desktop to notify the user in real time when an important task arises. Furthermore, the presentation unit can use a voice assistant or chatbot to guide the user through task priorities via voice or text. This allows the presentation unit to support the user in efficiently managing tasks and smoothly carrying out their work. In addition, the presentation unit can collect user feedback and use it to improve the presentation method. For example, it can collect information such as how users prefer to display task lists and when they want to receive notifications, and reflect this in the system customization. This allows the presentation unit to respond flexibly to user needs and improve the overall usability of the system.

[0075] The reordering unit readjusts tasks based on the priorities presented by the presentation unit. For example, the reordering unit reviews priorities when a new urgent task arises. Specifically, it uses an algorithm that dynamically updates the existing task list based on new data collected in real time. For example, if a new task with high urgency is added, the reordering unit sets that task as the highest priority and recalculates the priorities of other tasks. The reordering unit can also readjust priorities using AI. The AI ​​can learn past task processing history and user behavior patterns to suggest the optimal readjustment method. For example, if a particular task is taking longer than expected, the AI ​​can suggest lowering the priority of that task and processing other tasks first. This allows the reordering unit to achieve flexible task management to maximize the user's work efficiency. Furthermore, it is important for the reordering unit to continuously improve its readjustment algorithm based on user feedback. This allows the reordering unit to always provide optimal task management based on the latest information and user needs, supporting improved work efficiency.

[0076] The decision unit can determine the importance and urgency of each message using AI. For example, the decision unit can analyze the content of a message using natural language processing technology to determine its importance. The decision unit can also determine the urgency of a message using machine learning algorithms. For example, the decision unit can analyze the content of a message using natural language processing technology to determine its importance. The decision unit can also determine the urgency of a message using machine learning algorithms. The decision unit can also analyze the content of a message to determine its importance and urgency. This improves the accuracy of prioritization by having AI determine the importance and urgency of each message. Importance is evaluated based on, for example, the content of the message and the sender's position. Urgency is evaluated based on, for example, the deadline for response and the scope of impact. Some or all of the above processing in the decision unit may be performed using, for example, a generative AI, or without a generative AI. For example, the decision unit can input the content of a message into a generative AI and have the generative AI perform the determination of importance and urgency.

[0077] The presentation unit can list tasks based on priority and present them to a human. For example, the presentation unit can generate a task list and display it to the user. The presentation unit can also notify the user of the priority using a notification function. For example, the presentation unit can generate a task list and display it to the user. The presentation unit can also notify the user of the priority using a notification function. The presentation unit can also present the priority to the user. This makes task management easier by listing tasks based on priority and presenting them to a human. The listing is done based on, for example, the format of the list and the order of the items. Some or all of the above processing in the presentation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the presentation unit can have a generation AI perform the generation of the task list.

[0078] The readjustment unit can review priorities and notify humans when new urgent tasks arise. For example, the readjustment unit reviews priorities when new urgent tasks arise. The readjustment unit can also notify users of changes in priorities using a notification function. For example, the readjustment unit reviews priorities when new urgent tasks arise. The readjustment unit can also notify users of changes in priorities using a notification function. The readjustment unit can also review priorities and notify humans. This ensures that tasks are always carried out in the optimal order by reviewing priorities and notifying humans when new urgent tasks arise. Urgent tasks are determined based on factors such as deadlines and scope of impact. Some or all of the above-described processes in the readjustment unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the readjustment unit can input the occurrence of an urgent task into the generation AI and have the generation AI perform a review of priorities.

[0079] The analysis unit can read and analyze the content of emails and chats using AI. For example, the analysis unit can analyze message content using natural language processing technology. The analysis unit can also determine the importance and urgency of messages using machine learning algorithms. For example, the analysis unit can analyze message content using natural language processing technology and extract keywords. The analysis unit can also determine the importance and urgency of messages using machine learning algorithms. The analysis unit can also analyze message content and determine its importance and urgency. This improves the accuracy of the analysis by having AI read and analyze the content of emails and chats. Reading is performed based on, for example, natural language processing technology or text analysis algorithms. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input message content into a generative AI and have the generative AI perform content analysis.

[0080] The collection unit can collect messages from email, chat, and communication tools. For example, the collection unit can retrieve messages from email servers or chat applications. It can also collect messages using the APIs of communication tools. For example, the collection unit can retrieve emails from email servers using the IMAP protocol. It can also retrieve messages using the APIs of chat applications. This allows for centralized information management by collecting messages from email, chat, and communication tools. Some or all of the above-described processes in the collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the collection unit can have a generative AI perform the message collection.

[0081] The collection unit can estimate the user's emotions and adjust the timing of message collection based on the estimated emotions. For example, if the user is stressed, the collection unit can delay collection to collect messages when the user is relaxed. Conversely, if the user is focused, the collection unit can advance collection to deliver important messages urgently. For example, if the user is tired, the collection unit can adjust collection timing to collect messages after the user has rested. This reduces the user's burden by adjusting the timing of message collection based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the collection unit can input user emotion data into a generative AI and have the generative AI adjust the collection timing.

[0082] The collection unit can analyze the user's past message collection history and select the optimal collection method. For example, the collection unit may prioritize collection methods that the user has frequently used in the past (email, chat, etc.). The collection unit can also suggest the optimal collection method for a specific time period based on the user's past collection history. For example, the collection unit analyzes the user's past collection history and selects the most efficient collection method. This allows the optimal collection method to be selected by analyzing the user's past message collection history. The optimal collection method is selected based on factors such as collection frequency and collection target. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the collection unit can input past collection history into a generative AI and have the generative AI select the optimal collection method.

[0083] The collection unit can filter messages based on the user's current projects and areas of interest when collecting them. For example, the collection unit can prioritize collecting messages related to the user's current projects. The collection unit can also filter and collect relevant messages based on the user's areas of interest. For example, the collection unit can collect necessary messages according to the progress of the user's current projects. This allows for the priority collection of highly relevant messages by filtering based on the user's current projects and areas of interest. Filtering can be performed based on, for example, keyword filtering or category filtering. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the collection unit can input project and area of ​​interest information into a generative AI and have the generative AI perform the filtering.

[0084] The data collection unit can estimate the user's emotions and determine the priority of messages to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone less important messages. Conversely, if the user is relaxed, the data collection unit can prioritize collecting more important messages. For example, if the user is in a hurry, the data collection unit will prioritize collecting more urgent messages. This allows for the priority collection of important messages by determining the priority of messages based on the user's emotions. Prioritization is determined based on factors such as importance and urgency. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of messages.

[0085] The collection unit can prioritize collecting highly relevant messages based on the user's geographical location information when collecting messages. For example, if the user is in a specific region, the collection unit will prioritize collecting messages related to that region. The collection unit can also prioritize collecting messages related to locations close to the user's current location. For example, the collection unit can filter and collect highly relevant messages based on the user's geographical location information. This allows for efficient collection of region-related information by prioritizing the collection of highly relevant messages based on the user's geographical location information. Geographical location information is obtained, for example, based on GPS data or IP addresses. Some or all of the above processing in the collection unit may be performed using, for example, a generation AI, or without a generation AI. For example, the collection unit can input geographical location information into a generation AI and have the generation AI collect highly relevant messages.

[0086] The collection unit can analyze a user's social media activity and collect relevant messages when collecting messages. For example, the collection unit can prioritize collecting messages related to topics the user has shown interest in on social media. The collection unit can also filter and collect relevant messages based on the user's social media activity history. For example, the collection unit can prioritize collecting messages related to accounts the user follows on social media. This allows for efficient collection of relevant messages by analyzing the user's social media activity. Social media activity is analyzed based on, for example, the content of posts and the number of likes. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the collection unit can input social media activity data into a generative AI and have the generative AI collect relevant messages.

[0087] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. In this way, by adjusting the presentation of the analysis based on the user's emotions, the analysis results can be provided that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0088] The analysis unit can adjust the level of detail of its analysis based on the importance of the messages. For example, the analysis unit can perform a detailed analysis on high-importance messages and a concise analysis on low-importance messages. For instance, the analysis unit can adjust the level of detail of its analysis in stages according to the importance of the messages. This allows for efficient analysis by adjusting the level of detail based on the importance of the messages. Importance is evaluated based on factors such as the content of the message and the sender's position. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the importance of the messages into the generative AI and have the generative AI adjust the level of detail of the analysis.

[0089] The analysis unit can apply different analysis algorithms depending on the message category during analysis. For example, the analysis unit can apply a business-specific analysis algorithm to business-related messages. It can also apply a private-specific analysis algorithm to private messages. For instance, the analysis unit selects and applies the most suitable analysis algorithm based on the message category. This improves the accuracy of the analysis by applying different analysis algorithms depending on the message category. Categories are classified based on, for example, business categories or technology categories. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input message category information into a generative AI and have the generative AI apply the most suitable analysis algorithm.

[0090] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. Conversely, if the user is relaxed, the analysis unit can provide a longer analysis with detailed explanations. For example, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. By adjusting the length of the analysis based on the user's emotions, the system can provide the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0091] The analysis unit can determine the priority of analysis based on the message sending date during the analysis process. For example, the analysis unit may prioritize the analysis of recently sent messages. It can also postpone the analysis of older messages. For example, the analysis unit may adjust the analysis priority in stages according to the message sending date. This enables efficient analysis by determining the analysis priority based on the message sending date. The sending date is obtained, for example, based on a timestamp or sending history. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the message sending date into a generative AI and have the generative AI determine the analysis priority.

[0092] The analysis unit can adjust the order of analysis based on the relevance of messages during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant messages. It can also postpone the analysis of less relevant messages. For example, the analysis unit may adjust the order of analysis in stages according to the relevance of messages. This allows for efficient analysis by adjusting the order of analysis based on the relevance of messages. Relevance is evaluated based on, for example, common keywords or matching topics. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the relevance of messages into a generative AI and have the generative AI adjust the order of analysis.

[0093] The decision-making unit can estimate the user's emotions and adjust the priority determination method based on the estimated user emotions. For example, if the user is stressed, the decision-making unit will prioritize high-importance tasks. Conversely, if the user is relaxed, the decision-making unit can also prioritize high-urgency tasks. For example, if the user is in a hurry, the decision-making unit will prioritize tasks that can be completed quickly. This allows for the determination of optimal priorities for the user by adjusting the priority determination method based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the decision-making unit may be performed using, for example, a generative AI, or not. For example, the decision-making unit can input user emotion data into a generative AI and have the generative AI adjust the priority determination method.

[0094] The decision-making unit can determine priorities by considering the importance and urgency of messages in combination. For example, the decision-making unit may prioritize messages that are both highly important and highly urgent. It can also postpone messages that are both less important and less urgent. For example, the decision-making unit may determine priorities in stages by considering the balance between importance and urgency. This allows for the determination of the optimal priority by considering the importance and urgency of messages in combination. Importance is evaluated based on, for example, the content of the message or the sender's position. Urgency is evaluated based on, for example, the deadline for response or the scope of impact. Some or all of the above processing in the decision-making unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the decision-making unit can input the importance and urgency of messages into a generation AI and have the generation AI perform the priority determination.

[0095] The decision-making unit can determine priority by considering the attribute information of the message sender. For example, the decision-making unit may prioritize messages from a superior. It can also prioritize messages from a client. For example, the decision-making unit may determine priority by considering the sender's job title and relationship. This allows important messages to be processed preferentially by considering the sender's attribute information. Attribute information is obtained based on, for example, job title and department. Some or all of the above processing in the decision-making unit may be performed using, for example, a generative AI, or without a generative AI. For example, the decision-making unit can input the sender's attribute information into a generative AI and have the generative AI perform the priority determination.

[0096] The decision unit can estimate the user's emotions and adjust the display method of priorities based on the estimated emotions. For example, if the user is nervous, the decision unit can provide a simple and highly visible display method. It can also provide a display method that includes detailed information if the user is relaxed. For example, if the user is in a hurry, the decision unit can provide a concise display method. This allows for a user-friendly display by adjusting the display method of priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the decision unit may be performed using, for example, a generative AI, or not. For example, the decision unit can input user emotion data into a generative AI and have the generative AI adjust the display method.

[0097] The decision-making unit can consider the geographical distribution of messages when determining priority. For example, if a user is in a specific region, the decision-making unit will prioritize messages related to that region. It can also prioritize messages if the sender is nearby. For example, the decision-making unit can determine priority in stages based on the geographical distribution of messages. This allows for priority processing of important messages related to a region by considering the geographical distribution of messages. Geographical distribution can be obtained, for example, based on GPS data or IP addresses. Some or all of the above processing in the decision-making unit may be performed using, for example, a generative AI, or without a generative AI. For example, the decision-making unit can input geographical distribution information into a generative AI and have the generative AI perform the priority determination.

[0098] The decision-making unit can improve the accuracy of its prioritization by referring to relevant literature for the message. For example, the decision-making unit can refer to relevant literature for the message and determine the priority. The decision-making unit can also determine the priority based on research papers related to the content of the message. For example, the decision-making unit can analyze relevant literature for the message to improve the accuracy of its priority determination. Thus, the accuracy of priority determination is improved by referring to relevant literature for the message. Relevant literature is referred to, for example, based on database searches or citations. Some or all of the above processing in the decision-making unit may be performed using, for example, a generative AI, or without a generative AI. For example, the decision-making unit can input information on relevant literature into a generative AI and have the generative AI perform the priority determination.

[0099] The presentation unit can estimate the user's emotions and adjust how the task list is displayed based on the estimated emotions. For example, if the user is stressed, the presentation unit can provide a simple and highly visible task list. If the user is relaxed, the presentation unit can also provide a task list with more detailed information. For example, if the user is in a hurry, the presentation unit can provide a concise task list. By adjusting how the task list is displayed based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using a generative AI, or not using a generative AI. For example, the presentation unit can input user emotion data into a generative AI and have the generative AI adjust how the task list is displayed.

[0100] The display unit can select the optimal display method when displaying the task list by referring to the user's past operation history. For example, the display unit can prioritize display methods previously used by the user. The display unit can also suggest the optimal display method based on the user's past operation history. For example, the display unit analyzes the user's operation history and selects the most efficient display method. This allows the optimal display method to be selected by referring to the user's past operation history. Operation history is obtained based on, for example, click history or operation logs. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit can input operation history information into a generation AI and have the generation AI select the optimal display method.

[0101] The display unit can adjust the level of detail displayed based on the importance of the task when displaying the task list. For example, the display unit can display detailed information for tasks of high importance, and concise information for tasks of low importance. For example, the display unit can adjust the level of detail in stages according to the importance of the task. This allows for efficient task management by adjusting the level of detail based on the importance of the task. Importance is evaluated based on, for example, the content and deadline of the task. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit can input the importance of the task into the generation AI and have the generation AI perform the adjustment of the level of detail in the display.

[0102] The presentation unit can estimate the user's emotions and adjust the operation procedure of the task list based on the estimated user emotions. For example, if the user is tense, the presentation unit can provide simple and intuitive operation procedures. It can also provide detailed operation procedures if the user is relaxed. For example, if the user is in a hurry, the presentation unit can provide procedures that allow for quick operation. This allows for user-friendly operation procedures by adjusting the operation procedure of the task list based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the presentation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the presentation unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the operation procedure.

[0103] The display unit can select the optimal display method when displaying the task list, taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. The display unit can also provide a display method optimized for a larger screen if the user is using a tablet. For example, if the user is using a smartwatch, the display unit provides a concise and highly visible display method. This allows the display unit to provide the optimal display method by considering the user's device information. Device information is obtained based on, for example, the device type and OS version. Some or all of the above processing in the display unit may be performed using, for example, a generative AI, or without a generative AI. For example, the display unit can input device information into a generative AI and have the generative AI select the optimal display method.

[0104] The display unit can adjust the display order of tasks based on their relevance when displaying a task list. For example, the display unit can prioritize the display of highly relevant tasks. It can also display less relevant tasks later. For example, the display unit can adjust the display order in stages according to the relevance of the tasks. This allows for efficient task management by adjusting the display order based on the relevance of tasks. Relevance is evaluated based on, for example, common keywords or matching topics. Some or all of the above processing in the display unit may be performed using, for example, a generative AI, or without a generative AI. For example, the display unit can input the relevance of tasks into a generative AI and have the generative AI perform the adjustment of the display order.

[0105] The readjustment unit can estimate the user's emotions and adjust the task readjustment method based on the estimated user emotions. For example, if the user is stressed, the readjustment unit will prioritize readjusting high-priority tasks. Similarly, if the user is relaxed, the readjustment unit can prioritize readjusting high-urgency tasks. For example, if the user is in a hurry, the readjustment unit will prioritize readjusting tasks that can be completed quickly. This allows for optimal task readjustment for the user by adjusting the task readjustment method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the readjustment unit may be performed using, for example, a generative AI, or not. For example, the readjustment unit can input user emotion data into a generative AI and have the generative AI adjust the readjustment method.

[0106] The readjustment unit can analyze the frequency of new urgent tasks occurring during readjustment to determine the frequency of readjustment. For example, if new urgent tasks occur frequently, the readjustment unit can increase the frequency of readjustment. Conversely, if there are few new urgent tasks, the readjustment unit can decrease the frequency of readjustment. For example, the readjustment unit can adjust the frequency of readjustment in stages according to the frequency of new urgent tasks occurring. This allows the optimal readjustment frequency to be determined by analyzing the frequency of new urgent tasks. Urgent tasks are determined based on factors such as response deadlines and scope of impact. Some or all of the above-described processes in the readjustment unit may be performed using, for example, a generation AI, or without a generation AI. For example, the readjustment unit can input the frequency of urgent tasks into a generation AI and have the generation AI determine the readjustment frequency.

[0107] The readjustment unit can monitor the progress of tasks in real time during readjustment and make readjustments as needed. For example, if a task is behind schedule, the readjustment unit will readjust and revise its priorities. The readjustment unit can also readjust and revise the priorities of other tasks if a task is progressing ahead of schedule. For example, the readjustment unit monitors the progress of tasks in real time and makes readjustments as needed. This allows for readjustments as needed by monitoring the progress of tasks in real time. The progress is obtained based on, for example, the completion rate of tasks or the amount of work time. Some or all of the above processing in the readjustment unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the readjustment unit can input task progress data into a generation AI and have the generation AI perform the readjustment.

[0108] The readjustment unit can estimate the user's emotions and determine readjustment priorities based on the estimated emotions. For example, if the user is stressed, the readjustment unit will prioritize readjusting high-priority tasks. Similarly, if the user is relaxed, the readjustment unit can prioritize readjusting high-urgency tasks. For example, if the user is in a hurry, the readjustment unit will prioritize readjusting tasks that can be completed quickly. This allows for optimal task readjustment for the user by determining readjustment priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the readjustment unit may be performed using, for example, a generative AI, or not. For example, the readjustment unit can input user emotion data into a generative AI and have the generative AI determine readjustment priorities.

[0109] The readjustment unit can perform readjustment while considering the geographical distribution of tasks. For example, if a user is in a specific region, the readjustment unit will prioritize readjusting tasks related to that region. The readjustment unit can also determine the priority of readjustment based on the geographical distribution of tasks. For example, the readjustment unit can adjust the order of readjustment in stages, taking into account the geographical distribution of tasks. This allows for efficient readjustment of region-related tasks by considering the geographical distribution of tasks. Geographical distribution is obtained, for example, based on GPS data or IP addresses. Some or all of the above processing in the readjustment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the readjustment unit can input geographical distribution information into a generative AI and have the generative AI perform the readjustment.

[0110] The readjustment unit can improve the accuracy of readjustment by referring to relevant literature for the task during the readjustment process. For example, the readjustment unit can refer to relevant literature for the task and determine the readjustment priority. The readjustment unit can also determine the readjustment priority based on research papers related to the content of the task. For example, the readjustment unit can analyze relevant literature for the task and improve the readjustment accuracy. Thus, the accuracy of readjustment is improved by referring to relevant literature for the task. Relevant literature is referred to, for example, based on database searches or citations. Some or all of the above processing in the readjustment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the readjustment unit can input information on relevant literature into a generative AI and have the generative AI perform the readjustment.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The business efficiency system can also analyze the user's past work history and determine priorities by referring to past responses to similar tasks. For example, it can analyze how similar tasks were handled in the past and suggest the optimal approach. It can also identify the user's strengths and weaknesses in specific tasks based on past work history and prioritize assigning tasks they excel at. Furthermore, it can evaluate the efficiency of work during specific time periods based on past work history and assign tasks to the most optimal time slots. In this way, by utilizing the user's past work history, more efficient work execution becomes possible.

[0113] The business efficiency system can also estimate the user's emotions and adjust task assignments based on those emotions. For example, if a user is stressed, it can prioritize assigning lower-priority tasks. Conversely, if a user is relaxed, it can prioritize assigning higher-priority tasks. Furthermore, if a user is focused, it can assign complex tasks. By adjusting task assignments based on the user's emotions, the system can reduce the user's burden and enable more efficient work performance.

[0114] The business efficiency system can also monitor the user's current health status and adjust task priorities based on that status. For example, if a user is tired, it can prioritize assigning lower-priority tasks. Conversely, if a user is healthy and energetic, it can prioritize assigning higher-priority tasks. Furthermore, if a user is ill or injured, it can assign less demanding tasks. By adjusting task priorities based on the user's health status, the system reduces the user's burden and enables more efficient work performance.

[0115] The business efficiency system can also monitor users' schedules in real time and adjust task priorities based on those schedules. For example, if a user has a meeting or an important event coming up, it can assign lower-priority tasks to the time slots before and after. It can also assign higher-priority tasks to time slots when the user is free. Furthermore, it can optimize task assignments based on the user's schedule. This allows for more efficient work execution by adjusting task priorities based on the user's schedule.

[0116] The business efficiency system can also estimate the user's emotions and adjust the way tasks are notified based on those emotions. For example, if the user is stressed, it can provide a simple and highly visible notification. If the user is relaxed, it can provide a notification that includes more detailed information. Furthermore, if the user is in a hurry, it can provide a concise notification that gets straight to the point. By adjusting the way tasks are notified based on the user's emotions, it becomes possible to provide notifications that are easy for the user to understand.

[0117] The business efficiency system can also analyze the user's past task completion times and provide predicted completion times. For example, it can predict how long a particular task will take to complete based on past data and present this prediction to the user. It can also adjust task priorities based on the predicted completion time. Furthermore, it can optimize the user's schedule based on the predicted completion time. This allows for more efficient work execution by leveraging the user's past task completion times.

[0118] The business efficiency system can also estimate the user's emotions and provide real-time feedback on task progress based on those emotions. For example, if the user is stressed, the progress will be displayed simply. If the user is relaxed, a more detailed progress report can be displayed. Furthermore, if the user is focused, the progress can be visually highlighted. This allows for progress management that is easier for users to understand by providing feedback on progress based on their emotions.

[0119] The business efficiency system can also analyze the progress of the user's current projects and adjust the overall project priorities. For example, if a particular project is behind schedule, tasks related to that project can be prioritized. Conversely, if a project is progressing smoothly, tasks related to other projects can be prioritized. Furthermore, it is possible to optimize task assignments based on project progress. This allows for more efficient work execution by adjusting task priorities based on project progress.

[0120] The business efficiency system can also estimate the user's emotions and adjust task reminders based on those emotions. For example, if the user is stressed, it can provide a simple, highly visible reminder. If the user is relaxed, it can provide a reminder with more detailed information. Furthermore, if the user is in a hurry, it can provide a concise, to-the-point reminder. By adjusting task reminders based on the user's emotions, it becomes possible to create reminders that are easier for the user to understand.

[0121] The business efficiency system can also analyze users' past task completion rates and predict task difficulty. For example, it can predict the difficulty level of a particular task based on past data and present it to the user. It can also adjust task priorities based on the predicted difficulty level. Furthermore, it can optimize the user's schedule based on the predicted difficulty level. This enables more efficient work execution by leveraging users' past task completion rates.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The collection unit collects email and chat content. For example, the collection unit can retrieve messages from email servers and chat applications, and collect messages using the communication tool's API. Specifically, the collection unit retrieves emails using the IMAP protocol and retrieves messages using the chat application's API. Step 2: The analysis unit analyzes the content collected by the collection unit. For example, the analysis unit uses natural language processing technology to analyze the message content and machine learning algorithms to determine the importance and urgency of the message. Specifically, the analysis unit extracts keywords and determines the importance and urgency of the message. Step 3: The decision unit determines priorities based on the analysis results obtained by the analysis unit. For example, the decision unit can determine task priorities based on importance and urgency, or it can use AI to determine priorities. Step 4: The presentation unit presents the priorities determined by the decision unit to the user. For example, the presentation unit generates a task list and displays it to the user. It can also notify the user of the priorities using a notification function. Step 5: The rescheduling unit readjusts tasks based on the priorities presented by the presentation unit. For example, if a new urgent task arises, the unit can review the priorities and readjust them using AI.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0126] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements described above, including the collection unit, analysis unit, decision unit, presentation unit, and readjustment unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects the contents of emails and chats. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected contents. The decision unit is implemented by the specific processing unit 290 of the data processing device 12 and determines priorities based on the analysis results. The presentation unit is implemented by the control unit 46A of the smart device 14 and presents the determined priorities to the user. The readjustment unit is implemented by the specific processing unit 290 of the data processing device 12 and readjusts the priorities as needed. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the collection unit, analysis unit, decision unit, presentation unit, and readjustment unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects the contents of emails and chats. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected contents. The decision unit is implemented by the identification processing unit 290 of the data processing unit 12 and determines priorities based on the analysis results. The presentation unit is implemented by the control unit 46A of the smart glasses 214 and presents the determined priorities to the user. The readjustment unit is implemented by the identification processing unit 290 of the data processing unit 12 and readjusts the priorities as needed. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the collection unit, analysis unit, decision unit, presentation unit, and readjustment unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects the contents of emails and chats. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected contents. The decision unit is implemented by the specific processing unit 290 of the data processing unit 12 and determines priorities based on the analysis results. The presentation unit is implemented by the control unit 46A of the headset terminal 314 and presents the determined priorities to the user. The readjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and readjusts the priorities as needed. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0168] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0169] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0170] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0171] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0172] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0173] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0174] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0175] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] Each of the multiple elements described above, including the collection unit, analysis unit, decision unit, presentation unit, and readjustment unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects the contents of emails and chats. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected contents. The decision unit is implemented by the specific processing unit 290 of the data processing unit 12 and determines priorities based on the analysis results. The presentation unit is implemented by the control unit 46A of the robot 414 and presents the determined priorities to a human. The readjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and readjusts the priorities as needed. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0177] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0179] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0180] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0181] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0185] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0186] 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.

[0187] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0188] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0189] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0190] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0192] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0193] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0194] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0195] (Note 1) The collection department collects the contents of emails and chats, An analysis unit analyzes the contents collected by the collection unit, A decision unit that determines priorities based on the analysis results obtained by the aforementioned analysis unit, A presentation unit that presents the priority order determined by the aforementioned decision unit to a human, The system includes a readjustment unit that readjusts tasks based on the priority levels presented by the presentation unit. A system characterized by the following features. (Note 2) The aforementioned determination unit, AI determines the importance and urgency of each message. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned display unit is, List tasks based on priority and present them to humans. The system described in Appendix 1, characterized by the features described herein. (Note 4) The readjustment unit is, When new urgent tasks arise, prioritize them and notify humans. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is AI reads and analyzes the content of emails and chats. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Collect messages from email, chat, and other communication tools. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of message collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past message collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting messages, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of messages to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting messages, the system prioritizes collecting highly relevant messages based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting messages, the system analyzes users' social media activity and collects relevant messages. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the messages. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analysis algorithms are applied depending on the message category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the messages were sent. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the messages. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned determination unit, It estimates the user's emotions and adjusts the prioritization method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned determination unit, When determining priorities, the importance and urgency of the message are considered in combination. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned determination unit, When determining priority, the sender's attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned determination unit, It estimates the user's emotions and adjusts the display priority based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned determination unit, When determining priority, the geographical distribution of messages is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned determination unit, When prioritizing, referencing relevant literature for each message improves the accuracy of the decision. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is, It estimates the user's emotions and adjusts how the task list is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is, When displaying the task list, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is, When displaying the task list, adjust the level of detail based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is, It estimates the user's emotions and adjusts the steps in the task list based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is, When displaying the task list, the system selects the optimal display method considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned display unit is, When displaying the task list, the display order will be adjusted based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 31) The readjustment unit is, It estimates the user's emotions and adjusts how tasks are readjusted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The readjustment unit is, During readjustment, the frequency of new urgent tasks is analyzed to determine the frequency of readjustment. The system described in Appendix 1, characterized by the features described herein. (Note 33) The readjustment unit is, During readjustment, the task progress is monitored in real time, and readjustments are made as needed. The system described in Appendix 1, characterized by the features described herein. (Note 34) The readjustment unit is, It estimates the user's emotions and determines the priority of readjustments based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The readjustment unit is, When readjusting, take into account the geographical distribution of tasks. The system described in Appendix 1, characterized by the features described herein. (Note 36) The readjustment unit is, During readjustment, refer to relevant literature for the task to improve the accuracy of the readjustment. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects the contents of emails and chats, An analysis unit analyzes the contents collected by the collection unit, A decision unit that determines priorities based on the analysis results obtained by the aforementioned analysis unit, A presentation unit that presents the priority order determined by the aforementioned decision unit to a human, The system includes a readjustment unit that readjusts tasks based on the priority levels presented by the presentation unit. A system characterized by the following features.

2. The aforementioned determination unit, AI determines the importance and urgency of each message. The system according to feature 1.

3. The aforementioned display unit is, List tasks based on priority and present them to humans. The system according to feature 1.

4. The readjustment unit is, When new urgent tasks arise, prioritize them and notify humans. The system according to feature 1.

5. The aforementioned analysis unit is AI reads and analyzes the content of emails and chats. The system according to feature 1.

6. The aforementioned collection unit is Collect messages from email, chat, and other communication tools. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of message collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past message collection history and select the optimal collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting messages, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and determines the priority of messages to collect based on those estimated emotions. The system according to feature 1.

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