system

Abstract

The system according to this embodiment aims to reduce the burden of handover work and to enable a smooth transition of operations. [Solution] The system according to the embodiment comprises a document search unit, an email search unit, a summarization unit, and a notification unit. The document search unit searches for documents related to the business to be handed over. The email search unit searches for past emails related to the business to be handed over. The summarization unit summarizes the documents found by the document search unit and the emails found by the email search unit. The notification unit notifies the relevant parties of the information summarized by the summarization unit.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0007] The system according to this embodiment can reduce the burden of handover work and enable a smooth transition of operations. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple 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 handover support system according to an embodiment of the present invention is a system that reduces the burden of handover work during changes within an organization. This handover support system frees users from manual information gathering and verification work, and provides appropriate summaries using AI. By integrating functions such as document search, notification to relevant parties, and email search, it saves time and effort. By using this system, communication within the organization is strengthened, and a smooth transition of operations becomes possible. For example, the handover support system uses AI to automatically search for documents related to the tasks to be handed over. For example, it can quickly find relevant documents such as project progress reports and meeting minutes. Next, the handover support system uses AI to search past emails and extract information necessary for the handover. For example, it can find emails related to a specific project or emails containing important notices. Furthermore, the handover support system uses AI to summarize the documents and emails found by the document search unit and the email search unit. This allows users to grasp a large amount of information at once and proceed with the handover work efficiently. Finally, the handover support system prevents handover omissions by notifying relevant parties of the summarized information. For example, it can notify the new person in charge of a project of past progress and important notices. This system strengthens communication within the organization and enables a smooth transition of operations. Users can perform efficient and accurate handovers, reducing stress during organizational changes and transfers. Thus, the handover support system enables users to perform efficient and accurate handovers, reducing stress during organizational changes and transfers.

[0029] The handover support system according to this embodiment comprises a document search unit, an email search unit, a summarization unit, and a notification unit. The document search unit searches for documents related to the tasks to be handed over. The document search unit can quickly find relevant documents, such as project progress reports and meeting minutes. The document search unit can search for documents based on relevant keywords, for example, using AI. The document search unit can also filter documents based on conditions specified by the user. For example, the document search unit can prioritize searching for documents created within a specific period or documents created by a specific person. The email search unit searches for past emails. The email search unit can find emails related to a specific project or emails containing important notices, for example. The email search unit can analyze the content of emails and extract information necessary for the handover, for example, using AI. The email search unit can also filter emails based on conditions specified by the user. For example, the email search unit can prioritize searching for emails sent and received within a specific period or emails from a specific sender. The summarization unit summarizes the documents and emails found by the document search unit and the email search unit. The summarization unit can efficiently summarize vast amounts of information, for example, using AI. The summarization unit can perform summaries based on factors such as the length of the text and the importance of the information being summarized. The summarization unit can also adjust the level of detail of the summary based on conditions specified by the user. For example, the summarization unit can summarize highly important information in detail and less important information concisely. The notification unit notifies relevant parties of the information summarized by the summarization unit. The notification unit can notify relevant parties of the information using methods such as email notifications, push notifications, and SMS notifications. The notification unit can notify relevant parties at the appropriate time, for example, using AI. The notification unit can also adjust the priority of notifications based on conditions specified by the user. For example, the notification unit can prioritize notifying highly important information and postpone notifying less important information. As a result, the handover support system according to this embodiment reduces the burden of handover work and enables efficient and accurate handover.

[0030] The document search unit searches for documents related to the tasks to be handed over. It can quickly find relevant documents, such as project progress reports and meeting minutes. Specifically, the document search unit accesses internal company databases and cloud storage to search for relevant documents. Using AI, the document search unit utilizes natural language processing technology to analyze keywords and phrases within documents and identify highly relevant materials. For example, it can search based on project names, names of responsible persons, or technical terms related to specific tasks. The document search unit can also filter documents based on user-specified criteria. For example, it can prioritize searches for documents created within a specific period or documents created by specific persons. This allows users to quickly obtain necessary information, improving the efficiency of the handover process. Furthermore, the document search unit provides an interface to display search results visually and clearly. For example, it has a function to organize search results by category and sort them in order of relevance. It also provides a preview function for search results, allowing users to easily check the contents of the documents. This enables users to quickly identify necessary documents and proceed smoothly with the handover process.

[0031] The email search unit searches past emails. For example, it can find emails related to a specific project or emails containing important notices. Specifically, the email search unit accesses the company's email server and searches past email data. By using AI, the email search unit can analyze the content of emails and extract information necessary for handover. For example, it uses natural language processing technology to analyze the content of email bodies, subjects, and attachments to identify highly relevant emails. The email search unit can also filter emails based on conditions specified by the user. For example, it can prioritize searching for emails sent and received within a specific period or emails from specific senders. This allows users to quickly obtain the necessary emails, improving the efficiency of the handover process. Furthermore, the email search unit provides an interface to display search results in a visually easy-to-understand manner for the user. For example, it has a function to organize search results chronologically and sort them by relevance. It also provides a preview function for search results, allowing users to easily check the content of emails. This allows users to quickly identify the necessary emails and proceed with the handover process smoothly.

[0032] The summarization unit summarizes documents and emails retrieved by the document search unit and email search unit. The summarization unit can efficiently summarize vast amounts of information, for example, using AI. Specifically, it uses natural language processing technology to analyze the content of documents and emails and extract important information. For example, it can perform summarization based on the length of the text and the importance of the information being summarized. The summarization unit can also adjust the level of detail of the summary based on user-specified conditions. For example, it can summarize highly important information in detail and less important information concisely. This allows users to quickly grasp the necessary information and improves the efficiency of handover work. Furthermore, the summarization unit provides an interface to display the summarization results to the user in a visually easy-to-understand manner. For example, it can display the summarization results in bullet points or graphs, making it easy for users to understand the information. The summarization unit can also integrate the summarization results with other systems and applications. For example, it can automatically transfer the summarization results to project management tools or task management apps, further streamlining handover work. In this way, the summarization unit can efficiently summarize vast amounts of information and provide it in an easy-to-understand format for users.

[0033] The notification unit notifies relevant parties of the information summarized by the summarization unit. The notification unit can notify relevant parties using methods such as email notifications, push notifications, and SMS notifications. Specifically, the notification unit sends summarized information to relevant parties based on the notification method specified by the user. By using AI, the notification unit can notify relevant parties at the appropriate time. For example, it can notify at the optimal time considering the schedules and progress of the relevant parties' work. The notification unit can also adjust the priority of notifications based on conditions specified by the user. For example, it can prioritize notifications for high-priority information and postpone notifications for lower-priority information. This allows relevant parties to receive necessary information quickly, improving the efficiency of the handover process. Furthermore, the notification unit provides an interface to display notification content in a visually easy-to-understand manner for the user. For example, it can display notification content in a dashboard format, allowing relevant parties to easily check the information. The notification unit also manages the notification history, making it easy to refer to past notification content. This allows the notification unit to provide relevant parties with timely and appropriate information, enabling a smooth handover process.

[0034] The document search unit can automatically search for documents related to the tasks to be handed over using AI. For example, the document search unit can use AI to automatically search for documents related to the tasks to be handed over. For example, the document search unit can quickly find relevant documents such as project progress reports and meeting minutes. The document search unit can also filter documents based on conditions specified by the user. For example, the document search unit can prioritize searching for documents created within a specific period or documents created by a specific person. This automates the document search process, allowing for the efficient discovery of necessary documents. Some or all of the above processes in the document search unit may be performed using AI, or not. For example, the document search unit can use AI to search for documents based on relevant keywords. Alternatively, the document search unit can also search for documents by having the user manually enter keywords without using AI.

[0035] The email search unit can use AI to search past emails and extract information necessary for handover. For example, the email search unit can use AI to search past emails and extract information necessary for handover. For example, the email search unit can find emails related to a specific project or emails containing important notices. The email search unit can also filter emails based on conditions specified by the user. For example, the email search unit can prioritize searching emails sent and received within a specific period or emails from a specific sender. This allows for the efficient extraction of necessary information from past emails. Some or all of the above processing in the email search unit may be performed using AI, or not. For example, the email search unit can use AI to analyze the content of emails and extract information necessary for handover. Alternatively, the email search unit can also allow the user to manually search emails and extract necessary information without using AI.

[0036] The summarization unit can use AI to summarize documents and emails retrieved by the document search unit and the email search unit. For example, the summarization unit can use AI to summarize documents and emails retrieved by the document search unit and the email search unit. For example, the summarization unit can efficiently summarize vast amounts of information. For example, the summarization unit can perform summaries based on the length of the text and the importance of the information being summarized. The summarization unit can also adjust the level of detail of the summary based on conditions specified by the user. For example, the summarization unit can summarize highly important information in detail and less important information concisely. This allows for the efficient summarization of vast amounts of information and supports handover work. Some or all of the above-described processes in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can use AI to analyze the content of documents and emails and generate summaries. Alternatively, the summarization unit can also allow users to manually summarize documents and emails without using AI.

[0037] The notification unit can notify relevant parties of summarized information. For example, the notification unit notifies relevant parties of information summarized by the summarization unit. The notification unit can notify relevant parties of information by methods such as email notifications, push notifications, and SMS notifications. The notification unit can use AI to notify relevant parties at the appropriate time. The notification unit can also adjust the priority of notifications based on conditions specified by the user. For example, the notification unit can prioritize notifying high-priority information and postpone less important information. This prevents information from being missed during handover and provides relevant parties with the necessary information quickly. Some or all of the above processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can use AI to notify relevant parties at the appropriate time. Alternatively, the notification unit can also send notifications manually by the user without using AI.

[0038] The document search unit can analyze the user's past search history and select the optimal search method when a document search is performed. For example, the document search unit can analyze the user's past search history and select the optimal search method when a document search is performed. For example, the document search unit can suggest the optimal search method based on keywords that the user has frequently searched in the past. The document search unit can also prioritize searching for specific document types based on the user's past search history. Furthermore, the document search unit can analyze the user's past search patterns and suggest an efficient search method. This enables efficient document searching by providing the optimal search method based on the user's past search history. Some or all of the above processing in the document search unit may be performed using AI, for example, or without AI. For example, the document search unit can use AI to analyze the user's past search history and select the optimal search method. Alternatively, the document search unit can also allow the user to manually check their past search history and select the optimal search method without using AI.

[0039] The document search unit can filter documents based on the user's current projects and areas of interest during a document search. For example, the document search unit can filter documents based on the user's current projects and areas of interest during a document search. For example, the document search unit can prioritize searching for documents related to projects the user is currently involved in. The document search unit can also filter highly relevant documents based on the user's areas of interest. Furthermore, the document search unit can narrow down the necessary documents according to the user's current work content. This allows the system to provide highly relevant documents based on the user's current projects and areas of interest. Some or all of the above processing in the document search unit may be performed using AI, for example, or without AI. For example, the document search unit can use AI to filter documents based on the user's current projects and areas of interest. Alternatively, the document search unit can filter documents based on the user's manual specification of projects and areas of interest without using AI.

[0040] The document search unit can prioritize searching for highly relevant documents based on the user's geographical location information during a document search. For example, the document search unit can prioritize searching for highly relevant documents based on the user's geographical location information during a document search. For example, if the user is in a specific region, the document search unit can prioritize searching for documents related to that region. The document search unit can also filter highly relevant documents based on the user's current location. Furthermore, the document search unit can narrow down the necessary documents by considering the user's geographical location information. This allows the system to provide highly relevant documents based on the user's geographical location information. Some or all of the above processing in the document search unit may be performed using AI, for example, or without AI. For example, the document search unit can use AI to search for documents based on the user's geographical location information. Alternatively, the document search unit can also search for documents based on information that the user manually inputs using geographical location information, without using AI.

[0041] The data search unit can analyze a user's social media activity and search for relevant data when a data search is performed. For example, the data search unit can analyze a user's social media activity and search for relevant data when a data search is performed. For example, the data search unit can search for relevant data based on information shared by the user on social media. The data search unit can also prioritize searching for data related to topics of interest based on the user's social media activity. Furthermore, the data search unit can analyze a user's social media activity history and narrow down the necessary data. This allows the system to provide highly relevant data based on the user's social media activity. Some or all of the above processing in the data search unit may be performed using AI, for example, or without AI. For example, the data search unit can use AI to analyze a user's social media activity and search for relevant data. Alternatively, the data search unit can also search for data based on information manually entered by the user regarding their social media activity, without using AI.

[0042] The email search unit can analyze the user's past email sending and receiving history and select the optimal search method when performing an email search. For example, the email search unit can analyze the user's past email sending and receiving history and select the optimal search method. For example, the email search unit can suggest the optimal search method based on emails that the user has frequently sent and received in the past. The email search unit can also prioritize searching for specific email types based on the user's past email sending and receiving history. Furthermore, the email search unit can analyze the user's past email sending and receiving patterns and suggest an efficient search method. This enables efficient email searching by providing the optimal search method based on the user's past email sending and receiving history. Some or all of the above processing in the email search unit may be performed using AI, for example, or without AI. For example, the email search unit can use AI to analyze the user's past email sending and receiving history and select the optimal search method. Alternatively, the email search unit can also allow the user to manually check their past email sending and receiving history and select the optimal search method without using AI.

[0043] The email search unit can filter emails based on specific projects or areas of interest during an email search. For example, the email search unit can filter emails based on specific projects or areas of interest during an email search. For example, the email search unit can prioritize searching for emails related to projects the user is currently involved in. The email search unit can also filter highly relevant emails based on the user's areas of interest. Furthermore, the email search unit can narrow down the necessary emails according to the user's current work. This allows the system to provide highly relevant emails based on the user's current projects and areas of interest. Some or all of the above processing in the email search unit may be performed using AI, for example, or not using AI. For example, the email search unit can use AI to filter emails based on the user's current projects or areas of interest. Alternatively, the email search unit can filter emails by allowing the user to manually specify projects or areas of interest without using AI.

[0044] The email search unit can prioritize searching for emails that are highly relevant based on the user's geographical location information during an email search. For example, the email search unit can prioritize searching for emails that are highly relevant based on the user's geographical location information during an email search. For example, if the user is in a specific region, the email search unit can prioritize searching for emails related to that region. The email search unit can also filter highly relevant emails based on the user's current location. Furthermore, the email search unit can narrow down the necessary emails by considering the user's geographical location information. This allows the email search unit to provide highly relevant emails based on the user's geographical location information. Some or all of the above processing in the email search unit may be performed using AI, for example, or without AI. For example, the email search unit can use AI to search for emails based on the user's geographical location information. Alternatively, the email search unit can also search for emails based on information that the user manually inputs using geographical location information, without using AI.

[0045] The email search unit can analyze a user's social media activity and search for relevant emails during an email search. For example, the email search unit can analyze a user's social media activity and search for relevant emails. For example, the email search unit can search for relevant emails based on information shared by the user on social media. Furthermore, the email search unit can prioritize searching for emails related to topics of interest based on the user's social media activity. In addition, the email search unit can analyze a user's social media activity history and narrow down the necessary emails. This allows the system to provide highly relevant emails based on the user's social media activity. Some or all of the above processing in the email search unit may be performed using AI, or not. For example, the email search unit can use AI to analyze a user's social media activity and search for relevant emails. Alternatively, the email search unit can search for emails based on information manually entered by the user, without using AI.

[0046] The summarization unit can adjust the level of detail in the summary based on the importance of the documents and emails during summary generation. For example, the summarization unit can adjust the level of detail in the summary based on the importance of the documents and emails during summary generation. For example, the summarization unit can provide a detailed summary for documents and emails of high importance. It can also provide a concise summary for documents and emails of low importance. Furthermore, the summarization unit can dynamically adjust the level of detail in the summary according to the importance of the documents and emails. This allows for the provision of a summary with an appropriate level of detail according to the importance of the documents and emails. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can use AI to evaluate the importance of documents and emails and adjust the level of detail in the summary based on that evaluation. Alternatively, the summarization unit can use AI to allow a user to manually evaluate the importance of documents and emails and adjust the level of detail in the summary based on that evaluation.

[0047] The summarization unit can apply different summarization algorithms depending on the category of the document and email when generating summaries. For example, the summarization unit can apply a summarization algorithm that focuses on the technical points to technical documents. It can also apply a summarization algorithm that focuses on the business points to business documents. Furthermore, it can apply a summarization algorithm that focuses on the communication points to emails. This allows for the provision of optimal summaries tailored to the categories of documents and emails. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can use AI to classify the categories of documents and emails and apply different summarization algorithms based on that classification. Alternatively, the summarization unit can also use AI to allow users to manually classify the categories of documents and emails and apply different summarization algorithms based on that classification.

[0048] The summarization unit can determine the priority of summaries based on the submission dates of documents and emails when generating summaries. For example, the summarization unit can prioritize summarizing recently submitted documents and emails. It can also provide a concise summary for older documents and emails. Furthermore, the summarization unit can dynamically adjust the priority of summaries according to the submission dates. This allows for the provision of summaries with appropriate priority based on the submission dates of documents and emails. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can use AI to evaluate the submission dates of documents and emails and determine the priority of summaries based on that evaluation. Alternatively, the summarization unit can also use AI to allow users to manually evaluate the submission dates of documents and emails and determine the priority of summaries based on that evaluation.

[0049] The summarization unit can adjust the order of summaries based on the relevance of the documents and emails during summary generation. For example, the summarization unit can prioritize summarizing highly relevant documents and emails. It can also provide a concise summary for less relevant documents and emails. Furthermore, the summarization unit can dynamically adjust the order of summaries according to the relevance of the documents and emails. This allows for the provision of summaries in an appropriate order according to the relevance of the documents and emails. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can use AI to evaluate the relevance of documents and emails and adjust the order of summaries based on that evaluation. Alternatively, the summarization unit can also allow a user to manually evaluate the relevance of documents and emails and adjust the order of summaries based on that evaluation, without using AI.

[0050] The notification unit can analyze the past notification history of stakeholders and select the optimal notification method at the time of notification. For example, the notification unit can analyze the past notification history of stakeholders and select the optimal notification method at the time of notification. The notification unit can, for example, suggest the optimal notification method based on the notification methods that stakeholders have preferred to receive in the past. The notification unit can also prioritize the selection of a specific notification method based on the stakeholders' past notification history. Furthermore, the notification unit can analyze the stakeholders' past notification patterns and suggest an efficient notification method. This enables efficient notification by providing the optimal notification method based on the stakeholders' past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can use AI to analyze the past notification history of stakeholders and select the optimal notification method. Alternatively, the notification unit can also allow users to manually check past notification history and select the optimal notification method without using AI.

[0051] The notification unit can filter notifications based on the stakeholders' current projects and areas of interest. For example, the notification unit can filter notifications based on the stakeholders' current projects and areas of interest. For example, the notification unit can prioritize notifications related to projects that stakeholders are currently involved in. The notification unit can also filter notifications based on the stakeholders' areas of interest to ensure they are relevant. Furthermore, the notification unit can narrow down the necessary notifications according to the stakeholders' current work. This allows the notification unit to provide notifications that are relevant to the stakeholders' current projects and areas of interest. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can use AI to filter notifications based on the stakeholders' current projects and areas of interest. Alternatively, the notification unit can filter notifications based on the user's manual specification of projects and areas of interest, without using AI.

[0052] The notification unit can prioritize notifying relevant information based on the geographical location of the relevant parties when a notification is sent. For example, the notification unit can prioritize notifying relevant information based on the geographical location of the relevant parties when a notification is sent. For example, if a relevant party is in a specific region, the notification unit can prioritize notifying information related to that region. The notification unit can also filter relevant information based on the relevant party's current location. Furthermore, the notification unit can narrow down the necessary information by considering the geographical location of the relevant parties. This allows the notification unit to provide relevant information based on the geographical location of the relevant parties. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can use AI to notify information based on the geographical location of the relevant parties. Alternatively, the notification unit can also send notifications based on information that the user manually inputs using geographical location information, without using AI.

[0053] The notification unit can analyze the social media activity of stakeholders and notify relevant information at the time of notification. For example, the notification unit can analyze the social media activity of stakeholders and notify relevant information at the time of notification. For example, the notification unit can notify relevant information based on information shared by stakeholders on social media. The notification unit can also prioritize notifying information related to topics of interest from the stakeholders' social media activity. Furthermore, the notification unit can analyze the social media activity history of stakeholders and narrow down the necessary information. This allows for the provision of highly relevant information based on the stakeholders' social media activity. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can use AI to analyze the social media activity of stakeholders and notify relevant information. Alternatively, the notification unit can also notify users based on their manual input of social media activity without using AI.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The handover support system can also be equipped with a voice recognition unit. The voice recognition unit allows users to give instructions by voice and perform document searches and email searches based on those instructions. For example, if a user says, "Search for the progress report for Project X," the voice recognition unit can analyze the instruction and send it to the document search unit. The voice recognition unit can also be used by users to request summaries by voice. For example, if a user says, "Summarize the latest meeting minutes," the unit can send that instruction to the summarization unit. Furthermore, the voice recognition unit can also be used by users to request notifications by voice. For example, if a user says, "Notify the new person in charge of the progress," the unit can send that instruction to the notification unit. This allows users to easily operate the system by voice, further improving the efficiency of the handover process.

[0056] The handover support system can also include a translation unit. This unit can automatically translate documents and emails created in different languages. For example, if the document search unit searches for a document written in English, the translation unit can translate that document into Japanese. Similarly, if the email search unit searches for an email written in a foreign language, the translation unit can translate that email into the user's native language. Furthermore, the translation unit can also translate information summarized by the summarization unit into different languages. For example, it can translate information summarized in English by the summarization unit into Japanese. This facilitates smooth handovers between users who speak different languages.

[0057] The handover support system can also be equipped with an image analysis unit. The image analysis unit can analyze images contained in documents and emails and extract relevant information. For example, if the document search unit searches for a project progress report, the image analysis unit can analyze the graphs and charts contained in the report and extract important data. Similarly, if the email search unit searches for images attached to emails, the image analysis unit can analyze those images and extract relevant information. Furthermore, the image analysis unit can also take the content of images into consideration when the summarization unit summarizes the information. For example, when the summarization unit summarizes meeting minutes, it can reflect the content of charts and tables contained in the minutes in the summary. This allows for the effective use of information from documents and emails that include images.

[0058] The handover support system can also include a schedule management unit. This unit manages the user's schedule and optimizes the timing of handover tasks. For example, it can analyze the user's calendar and suggest suitable times for handover work. It can also track the progress of handover tasks and send reminders as needed. Furthermore, it can coordinate the schedules of stakeholders and set dates and times for handover meetings. This ensures that handover tasks proceed systematically and are completed efficiently.

[0059] The handover support system can also include a feedback collection unit. This unit can collect feedback from users after the handover process is complete, helping to improve the system. For example, the feedback collection unit can conduct surveys regarding user satisfaction with the handover process and areas for improvement. It can also analyze the user feedback and generate suggestions for improving the system's functionality and usability. Furthermore, based on user feedback, the feedback collection unit can implement improvements to optimize the handover process. This allows the handover support system to continuously improve according to user needs, supporting more effective handover operations.

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

[0061] Step 1: The document search unit searches for documents related to the tasks to be handed over. The document search unit can quickly find relevant documents such as project progress reports and meeting minutes. The document search unit can use AI to search for documents based on relevant keywords and can also filter documents based on conditions specified by the user. For example, it can prioritize searching for documents created within a specific period or documents created by a specific person. Step 2: The email search unit searches past emails. The email search unit can find emails related to a specific project or emails containing important notices. The email search unit can use AI to analyze the content of emails and extract the information necessary for the handover. It can also filter emails based on conditions specified by the user. For example, it can prioritize searching for emails sent and received within a specific period or emails from specific senders. Step 3: The summarization unit summarizes the documents and emails retrieved by the document search unit and the email search unit. The summarization unit can efficiently summarize vast amounts of information using AI. The summarization unit performs summarization based on the length of the text and the importance of the information being summarized, and can also adjust the level of detail of the summary based on conditions specified by the user. For example, it can summarize highly important information in detail and less important information concisely. Step 4: The notification unit notifies stakeholders of the information summarized by the summarization unit. The notification unit can notify stakeholders via methods such as email, push notifications, and SMS notifications. The notification unit can use AI to notify stakeholders at the appropriate time and can also adjust the notification priority based on user-specified conditions. For example, it can prioritize notifications for high-priority information and postpone notifications for lower-priority information.

[0062] (Example of form 2) The handover support system according to an embodiment of the present invention is a system that reduces the burden of handover work during changes within an organization. This handover support system frees users from manual information gathering and verification work, and provides appropriate summaries using AI. By integrating functions such as document search, notification to relevant parties, and email search, it saves time and effort. By using this system, communication within the organization is strengthened, and a smooth transition of operations becomes possible. For example, the handover support system uses AI to automatically search for documents related to the tasks to be handed over. For example, it can quickly find relevant documents such as project progress reports and meeting minutes. Next, the handover support system uses AI to search past emails and extract information necessary for the handover. For example, it can find emails related to a specific project or emails containing important notices. Furthermore, the handover support system uses AI to summarize the documents and emails found by the document search unit and the email search unit. This allows users to grasp a large amount of information at once and proceed with the handover work efficiently. Finally, the handover support system prevents handover omissions by notifying relevant parties of the summarized information. For example, it can notify the new person in charge of a project of past progress and important notices. This system strengthens communication within the organization and enables a smooth transition of operations. Users can perform efficient and accurate handovers, reducing stress during organizational changes and transfers. Thus, the handover support system enables users to perform efficient and accurate handovers, reducing stress during organizational changes and transfers.

[0063] The handover support system according to this embodiment comprises a document search unit, an email search unit, a summarization unit, and a notification unit. The document search unit searches for documents related to the tasks to be handed over. The document search unit can quickly find relevant documents, such as project progress reports and meeting minutes. The document search unit can search for documents based on relevant keywords, for example, using AI. The document search unit can also filter documents based on conditions specified by the user. For example, the document search unit can prioritize searching for documents created within a specific period or documents created by a specific person. The email search unit searches for past emails. The email search unit can find emails related to a specific project or emails containing important notices, for example. The email search unit can analyze the content of emails and extract information necessary for the handover, for example, using AI. The email search unit can also filter emails based on conditions specified by the user. For example, the email search unit can prioritize searching for emails sent and received within a specific period or emails from a specific sender. The summarization unit summarizes the documents and emails found by the document search unit and the email search unit. The summarization unit can efficiently summarize vast amounts of information, for example, using AI. The summarization unit can perform summaries based on factors such as the length of the text and the importance of the information being summarized. The summarization unit can also adjust the level of detail of the summary based on conditions specified by the user. For example, the summarization unit can summarize highly important information in detail and less important information concisely. The notification unit notifies relevant parties of the information summarized by the summarization unit. The notification unit can notify relevant parties of the information using methods such as email notifications, push notifications, and SMS notifications. The notification unit can notify relevant parties at the appropriate time, for example, using AI. The notification unit can also adjust the priority of notifications based on conditions specified by the user. For example, the notification unit can prioritize notifying highly important information and postpone notifying less important information. As a result, the handover support system according to this embodiment reduces the burden of handover work and enables efficient and accurate handover.

[0064] The document search unit searches for documents related to the tasks to be handed over. It can quickly find relevant documents, such as project progress reports and meeting minutes. Specifically, the document search unit accesses internal company databases and cloud storage to search for relevant documents. Using AI, the document search unit utilizes natural language processing technology to analyze keywords and phrases within documents and identify highly relevant materials. For example, it can search based on project names, names of responsible persons, or technical terms related to specific tasks. The document search unit can also filter documents based on user-specified criteria. For example, it can prioritize searches for documents created within a specific period or documents created by specific persons. This allows users to quickly obtain necessary information, improving the efficiency of the handover process. Furthermore, the document search unit provides an interface to display search results visually and clearly. For example, it has a function to organize search results by category and sort them in order of relevance. It also provides a preview function for search results, allowing users to easily check the contents of the documents. This enables users to quickly identify necessary documents and proceed smoothly with the handover process.

[0065] The email search unit searches past emails. For example, it can find emails related to a specific project or emails containing important notices. Specifically, the email search unit accesses the company's email server and searches past email data. By using AI, the email search unit can analyze the content of emails and extract information necessary for handover. For example, it uses natural language processing technology to analyze the content of email bodies, subjects, and attachments to identify highly relevant emails. The email search unit can also filter emails based on conditions specified by the user. For example, it can prioritize searching for emails sent and received within a specific period or emails from specific senders. This allows users to quickly obtain the necessary emails, improving the efficiency of the handover process. Furthermore, the email search unit provides an interface to display search results in a visually easy-to-understand manner for the user. For example, it has a function to organize search results chronologically and sort them by relevance. It also provides a preview function for search results, allowing users to easily check the content of emails. This allows users to quickly identify the necessary emails and proceed with the handover process smoothly.

[0066] The summarization unit summarizes documents and emails retrieved by the document search unit and email search unit. The summarization unit can efficiently summarize vast amounts of information, for example, using AI. Specifically, it uses natural language processing technology to analyze the content of documents and emails and extract important information. For example, it can perform summarization based on the length of the text and the importance of the information being summarized. The summarization unit can also adjust the level of detail of the summary based on user-specified conditions. For example, it can summarize highly important information in detail and less important information concisely. This allows users to quickly grasp the necessary information and improves the efficiency of handover work. Furthermore, the summarization unit provides an interface to display the summarization results to the user in a visually easy-to-understand manner. For example, it can display the summarization results in bullet points or graphs, making it easy for users to understand the information. The summarization unit can also integrate the summarization results with other systems and applications. For example, it can automatically transfer the summarization results to project management tools or task management apps, further streamlining handover work. In this way, the summarization unit can efficiently summarize vast amounts of information and provide it in an easy-to-understand format for users.

[0067] The notification unit notifies relevant parties of the information summarized by the summarization unit. The notification unit can notify relevant parties using methods such as email notifications, push notifications, and SMS notifications. Specifically, the notification unit sends summarized information to relevant parties based on the notification method specified by the user. By using AI, the notification unit can notify relevant parties at the appropriate time. For example, it can notify at the optimal time considering the schedules and progress of the relevant parties' work. The notification unit can also adjust the priority of notifications based on conditions specified by the user. For example, it can prioritize notifications for high-priority information and postpone notifications for lower-priority information. This allows relevant parties to receive necessary information quickly, improving the efficiency of the handover process. Furthermore, the notification unit provides an interface to display notification content in a visually easy-to-understand manner for the user. For example, it can display notification content in a dashboard format, allowing relevant parties to easily check the information. The notification unit also manages the notification history, making it easy to refer to past notification content. This allows the notification unit to provide relevant parties with timely and appropriate information, enabling a smooth handover process.

[0068] The document search unit can automatically search for documents related to the tasks to be handed over using AI. For example, the document search unit can use AI to automatically search for documents related to the tasks to be handed over. For example, the document search unit can quickly find relevant documents such as project progress reports and meeting minutes. The document search unit can also filter documents based on conditions specified by the user. For example, the document search unit can prioritize searching for documents created within a specific period or documents created by a specific person. This automates the document search process, allowing for the efficient discovery of necessary documents. Some or all of the above processes in the document search unit may be performed using AI, or not. For example, the document search unit can use AI to search for documents based on relevant keywords. Alternatively, the document search unit can also search for documents by having the user manually enter keywords without using AI.

[0069] The email search unit can use AI to search past emails and extract information necessary for handover. For example, the email search unit can use AI to search past emails and extract information necessary for handover. For example, the email search unit can find emails related to a specific project or emails containing important notices. The email search unit can also filter emails based on conditions specified by the user. For example, the email search unit can prioritize searching emails sent and received within a specific period or emails from a specific sender. This allows for the efficient extraction of necessary information from past emails. Some or all of the above processing in the email search unit may be performed using AI, or not. For example, the email search unit can use AI to analyze the content of emails and extract information necessary for handover. Alternatively, the email search unit can also allow the user to manually search emails and extract necessary information without using AI.

[0070] The summarization unit can use AI to summarize documents and emails retrieved by the document search unit and the email search unit. For example, the summarization unit can use AI to summarize documents and emails retrieved by the document search unit and the email search unit. For example, the summarization unit can efficiently summarize vast amounts of information. For example, the summarization unit can perform summaries based on the length of the text and the importance of the information being summarized. The summarization unit can also adjust the level of detail of the summary based on conditions specified by the user. For example, the summarization unit can summarize highly important information in detail and less important information concisely. This allows for the efficient summarization of vast amounts of information and supports handover work. Some or all of the above-described processes in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can use AI to analyze the content of documents and emails and generate summaries. Alternatively, the summarization unit can also allow users to manually summarize documents and emails without using AI.

[0071] The notification unit can notify relevant parties of summarized information. For example, the notification unit notifies relevant parties of information summarized by the summarization unit. The notification unit can notify relevant parties of information by methods such as email notifications, push notifications, and SMS notifications. The notification unit can use AI to notify relevant parties at the appropriate time. The notification unit can also adjust the priority of notifications based on conditions specified by the user. For example, the notification unit can prioritize notifying high-priority information and postpone less important information. This prevents information from being missed during handover and provides relevant parties with the necessary information quickly. Some or all of the above processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can use AI to notify relevant parties at the appropriate time. Alternatively, the notification unit can also send notifications manually by the user without using AI.

[0072] The data retrieval unit can estimate the user's emotions and adjust the priority of data retrieval based on the estimated emotions. For example, if the user is stressed, the data retrieval unit can prioritize searching for highly important data. If the user is relaxed, the data retrieval unit can also search a wide range of relevant data. Furthermore, if the user is in a hurry, the data retrieval unit can prioritize searching for the data that can be accessed most quickly. This allows the data retrieval unit to adjust the priority of data retrieval according to the user's emotions and provide more appropriate data. 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 data retrieval unit may be performed using AI, for example, or without AI. For example, the data retrieval unit can use AI to estimate the user's emotions and adjust the priority of data retrieval based on the estimated emotions. Furthermore, the document search function can also allow users to manually input their emotions without using AI, and adjust the priority of document searches based on those emotions.

[0073] The document search unit can analyze the user's past search history and select the optimal search method when a document search is performed. For example, the document search unit can analyze the user's past search history and select the optimal search method when a document search is performed. For example, the document search unit can suggest the optimal search method based on keywords that the user has frequently searched in the past. The document search unit can also prioritize searching for specific document types based on the user's past search history. Furthermore, the document search unit can analyze the user's past search patterns and suggest an efficient search method. This enables efficient document searching by providing the optimal search method based on the user's past search history. Some or all of the above processing in the document search unit may be performed using AI, for example, or without AI. For example, the document search unit can use AI to analyze the user's past search history and select the optimal search method. Alternatively, the document search unit can also allow the user to manually check their past search history and select the optimal search method without using AI.

[0074] The document search unit can filter documents based on the user's current projects and areas of interest during a document search. For example, the document search unit can filter documents based on the user's current projects and areas of interest during a document search. For example, the document search unit can prioritize searching for documents related to projects the user is currently involved in. The document search unit can also filter highly relevant documents based on the user's areas of interest. Furthermore, the document search unit can narrow down the necessary documents according to the user's current work content. This allows the system to provide highly relevant documents based on the user's current projects and areas of interest. Some or all of the above processing in the document search unit may be performed using AI, for example, or without AI. For example, the document search unit can use AI to filter documents based on the user's current projects and areas of interest. Alternatively, the document search unit can filter documents based on the user's manual specification of projects and areas of interest without using AI.

[0075] The data search unit can estimate the user's emotions and adjust the display order of search results based on the estimated emotions. For example, if the user is stressed, the data search unit can display the most important data first. If the user is relaxed, the data search unit can display a wide range of relevant data. Furthermore, if the user is in a hurry, the data search unit can display data that can be accessed quickly at the top. This allows the display order of search results to be adjusted according to the user's emotions, providing more appropriate data. 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 data search unit may be performed using AI, or not using AI. For example, the data search unit can use AI to estimate the user's emotions and adjust the display order of search results based on the estimated emotions. Furthermore, the document search function can also allow users to manually input their emotions without using AI, and adjust the display order of search results based on those emotions.

[0076] The document search unit can prioritize searching for highly relevant documents based on the user's geographical location information during a document search. For example, the document search unit can prioritize searching for highly relevant documents based on the user's geographical location information during a document search. For example, if the user is in a specific region, the document search unit can prioritize searching for documents related to that region. The document search unit can also filter highly relevant documents based on the user's current location. Furthermore, the document search unit can narrow down the necessary documents by considering the user's geographical location information. This allows the system to provide highly relevant documents based on the user's geographical location information. Some or all of the above processing in the document search unit may be performed using AI, for example, or without AI. For example, the document search unit can use AI to search for documents based on the user's geographical location information. Alternatively, the document search unit can also search for documents based on information that the user manually inputs using geographical location information, without using AI.

[0077] The data search unit can analyze a user's social media activity and search for relevant data when a data search is performed. For example, the data search unit can analyze a user's social media activity and search for relevant data when a data search is performed. For example, the data search unit can search for relevant data based on information shared by the user on social media. The data search unit can also prioritize searching for data related to topics of interest based on the user's social media activity. Furthermore, the data search unit can analyze a user's social media activity history and narrow down the necessary data. This allows the system to provide highly relevant data based on the user's social media activity. Some or all of the above processing in the data search unit may be performed using AI, for example, or without AI. For example, the data search unit can use AI to analyze a user's social media activity and search for relevant data. Alternatively, the data search unit can also search for data based on information manually entered by the user regarding their social media activity, without using AI.

[0078] The email search unit can estimate the user's emotions and adjust the priority of email searches based on the estimated emotions. For example, if the user is stressed, the email search unit can prioritize searching for high-priority emails. If the user is relaxed, the email search unit can also search a wide range of relevant emails. Furthermore, if the user is in a hurry, the email search unit can prioritize searching for emails that can be accessed most quickly. This allows the email search priority to be adjusted according to the user's emotions, providing more appropriate emails. 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 email search unit may be performed using AI, or not. For example, the email search unit can use AI to estimate the user's emotions and adjust the priority of email searches based on the estimated emotions. Furthermore, the email search function can also adjust the email search priority based on the user's manually entered emotions, without using AI.

[0079] The email search unit can analyze the user's past email sending and receiving history and select the optimal search method when performing an email search. For example, the email search unit can analyze the user's past email sending and receiving history and select the optimal search method. For example, the email search unit can suggest the optimal search method based on emails that the user has frequently sent and received in the past. The email search unit can also prioritize searching for specific email types based on the user's past email sending and receiving history. Furthermore, the email search unit can analyze the user's past email sending and receiving patterns and suggest an efficient search method. This enables efficient email searching by providing the optimal search method based on the user's past email sending and receiving history. Some or all of the above processing in the email search unit may be performed using AI, for example, or without AI. For example, the email search unit can use AI to analyze the user's past email sending and receiving history and select the optimal search method. Alternatively, the email search unit can also allow the user to manually check their past email sending and receiving history and select the optimal search method without using AI.

[0080] The email search unit can filter emails based on specific projects or areas of interest during an email search. For example, the email search unit can filter emails based on specific projects or areas of interest during an email search. For example, the email search unit can prioritize searching for emails related to projects the user is currently involved in. The email search unit can also filter highly relevant emails based on the user's areas of interest. Furthermore, the email search unit can narrow down the necessary emails according to the user's current work. This allows the system to provide highly relevant emails based on the user's current projects and areas of interest. Some or all of the above processing in the email search unit may be performed using AI, for example, or not using AI. For example, the email search unit can use AI to filter emails based on the user's current projects or areas of interest. Alternatively, the email search unit can filter emails by allowing the user to manually specify projects or areas of interest without using AI.

[0081] The email search unit can estimate the user's emotions and adjust the display order of search results based on the estimated emotions. For example, if the user is stressed, the email search unit can display the most important emails first. If the user is relaxed, the email search unit can display a wide range of relevant emails. Furthermore, if the user is in a hurry, the email search unit can display emails that can be accessed quickly at the top. This allows the display order of search results to be adjusted according to the user's emotions, providing more appropriate emails. 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 email search unit may be performed using AI, for example, or without AI. For example, the email search unit can use AI to estimate the user's emotions and adjust the display order of search results based on the estimated emotions. Furthermore, the email search function can also allow users to manually input their emotions without using AI, and adjust the display order of search results based on those emotions.

[0082] The email search unit can prioritize searching for emails that are highly relevant based on the user's geographical location information during an email search. For example, the email search unit can prioritize searching for emails that are highly relevant based on the user's geographical location information during an email search. For example, if the user is in a specific region, the email search unit can prioritize searching for emails related to that region. The email search unit can also filter highly relevant emails based on the user's current location. Furthermore, the email search unit can narrow down the necessary emails by considering the user's geographical location information. This allows the email search unit to provide highly relevant emails based on the user's geographical location information. Some or all of the above processing in the email search unit may be performed using AI, for example, or without AI. For example, the email search unit can use AI to search for emails based on the user's geographical location information. Alternatively, the email search unit can also search for emails based on information that the user manually inputs using geographical location information, without using AI.

[0083] The email search unit can analyze a user's social media activity and search for relevant emails during an email search. For example, the email search unit can analyze a user's social media activity and search for relevant emails. For example, the email search unit can search for relevant emails based on information shared by the user on social media. Furthermore, the email search unit can prioritize searching for emails related to topics of interest based on the user's social media activity. In addition, the email search unit can analyze a user's social media activity history and narrow down the necessary emails. This allows the system to provide highly relevant emails based on the user's social media activity. Some or all of the above processing in the email search unit may be performed using AI, or not. For example, the email search unit can use AI to analyze a user's social media activity and search for relevant emails. Alternatively, the email search unit can search for emails based on information manually entered by the user, without using AI.

[0084] The summarization unit can estimate the user's emotions and adjust the way the summary is presented based on the estimated emotions. For example, if the user is stressed, the summarization unit can provide a concise and to-the-point summary. If the user is relaxed, the summarization unit can provide a summary that includes detailed information. Furthermore, if the user is in a hurry, the summarization unit can provide a summary that can be quickly understood. This allows the summarization unit to adjust the way the summary is presented according to the user's emotions and provide a more appropriate summary. 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 summarization unit may be performed using AI, for example, or not using AI. For example, the summarization unit can use AI to estimate the user's emotions and adjust the way the summary is presented based on the estimated emotions. Furthermore, the summarization function allows users to manually input emotions without using AI, and the way the summary is presented can be adjusted based on those emotions.

[0085] The summarization unit can adjust the level of detail in the summary based on the importance of the documents and emails during summary generation. For example, the summarization unit can adjust the level of detail in the summary based on the importance of the documents and emails during summary generation. For example, the summarization unit can provide a detailed summary for documents and emails of high importance. It can also provide a concise summary for documents and emails of low importance. Furthermore, the summarization unit can dynamically adjust the level of detail in the summary according to the importance of the documents and emails. This allows for the provision of a summary with an appropriate level of detail according to the importance of the documents and emails. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can use AI to evaluate the importance of documents and emails and adjust the level of detail in the summary based on that evaluation. Alternatively, the summarization unit can use AI to allow a user to manually evaluate the importance of documents and emails and adjust the level of detail in the summary based on that evaluation.

[0086] The summarization unit can apply different summarization algorithms depending on the category of the document and email when generating summaries. For example, the summarization unit can apply a summarization algorithm that focuses on the technical points to technical documents. It can also apply a summarization algorithm that focuses on the business points to business documents. Furthermore, it can apply a summarization algorithm that focuses on the communication points to emails. This allows for the provision of optimal summaries tailored to the categories of documents and emails. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can use AI to classify the categories of documents and emails and apply different summarization algorithms based on that classification. Alternatively, the summarization unit can also use AI to allow users to manually classify the categories of documents and emails and apply different summarization algorithms based on that classification.

[0087] The summarization unit can estimate the user's emotions and adjust the length of the summary based on the estimated emotions. For example, if the user is stressed, the summarization unit can provide a short, concise summary. If the user is relaxed, the summarization unit can provide a longer summary with more detailed information. Furthermore, if the user is in a hurry, the summarization unit can provide a short summary that can be quickly understood. This allows for adjusting the length of the summary according to the user's emotions to provide a more appropriate summary. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the summarization unit may be performed using AI or not. For example, the summarization unit can use AI to estimate the user's emotions and adjust the length of the summary based on the estimated emotions. Furthermore, the summarization function allows users to manually input emotions without using AI, and the length of the summary can be adjusted based on those emotions.

[0088] The summarization unit can determine the priority of summaries based on the submission dates of documents and emails when generating summaries. For example, the summarization unit can prioritize summarizing recently submitted documents and emails. It can also provide a concise summary for older documents and emails. Furthermore, the summarization unit can dynamically adjust the priority of summaries according to the submission dates. This allows for the provision of summaries with appropriate priority based on the submission dates of documents and emails. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can use AI to evaluate the submission dates of documents and emails and determine the priority of summaries based on that evaluation. Alternatively, the summarization unit can also use AI to allow users to manually evaluate the submission dates of documents and emails and determine the priority of summaries based on that evaluation.

[0089] The summarization unit can adjust the order of summaries based on the relevance of the documents and emails during summary generation. For example, the summarization unit can prioritize summarizing highly relevant documents and emails. It can also provide a concise summary for less relevant documents and emails. Furthermore, the summarization unit can dynamically adjust the order of summaries according to the relevance of the documents and emails. This allows for the provision of summaries in an appropriate order according to the relevance of the documents and emails. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can use AI to evaluate the relevance of documents and emails and adjust the order of summaries based on that evaluation. Alternatively, the summarization unit can also allow a user to manually evaluate the relevance of documents and emails and adjust the order of summaries based on that evaluation, without using AI.

[0090] The notification unit can estimate the user's emotions and adjust the way notifications are presented based on the estimated emotions. For example, the notification unit can provide a concise and to-the-point notification if the user is stressed. It can also provide a notification with detailed information if the user is relaxed. Furthermore, it can provide a notification that can be quickly understood if the user is in a hurry. This allows for the presentation of notifications to be adjusted according to the user's emotions, providing more appropriate notifications. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can use AI to estimate the user's emotions and adjust the way notifications are presented based on the estimated emotions. Furthermore, the notification system can also allow users to manually input emotions without using AI, and adjust the way notifications are expressed based on those emotions.

[0091] The notification unit can analyze the past notification history of stakeholders and select the optimal notification method at the time of notification. For example, the notification unit can analyze the past notification history of stakeholders and select the optimal notification method at the time of notification. The notification unit can, for example, suggest the optimal notification method based on the notification methods that stakeholders have preferred to receive in the past. The notification unit can also prioritize the selection of a specific notification method based on the stakeholders' past notification history. Furthermore, the notification unit can analyze the stakeholders' past notification patterns and suggest an efficient notification method. This enables efficient notification by providing the optimal notification method based on the stakeholders' past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can use AI to analyze the past notification history of stakeholders and select the optimal notification method. Alternatively, the notification unit can also allow users to manually check past notification history and select the optimal notification method without using AI.

[0092] The notification unit can filter notifications based on the stakeholders' current projects and areas of interest. For example, the notification unit can filter notifications based on the stakeholders' current projects and areas of interest. For example, the notification unit can prioritize notifications related to projects that stakeholders are currently involved in. The notification unit can also filter notifications based on the stakeholders' areas of interest to ensure they are relevant. Furthermore, the notification unit can narrow down the necessary notifications according to the stakeholders' current work. This allows the notification unit to provide notifications that are relevant to the stakeholders' current projects and areas of interest. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can use AI to filter notifications based on the stakeholders' current projects and areas of interest. Alternatively, the notification unit can filter notifications based on the user's manual specification of projects and areas of interest, without using AI.

[0093] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit can prioritize high-priority notifications. If the user is relaxed, the notification unit can also send a wide range of relevant notifications. Furthermore, if the user is in a hurry, the notification unit can prioritize notifications that require immediate attention. This allows for adjusting notification priorities according to the user's emotions, providing more appropriate notifications. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can use AI to estimate the user's emotions and determine the priority of notifications based on the estimated emotions. Furthermore, the notification system can also prioritize notifications based on the user's manually entered emotions, without the use of AI.

[0094] The notification unit can prioritize notifying relevant information based on the geographical location of the relevant parties when a notification is sent. For example, the notification unit can prioritize notifying relevant information based on the geographical location of the relevant parties when a notification is sent. For example, if a relevant party is in a specific region, the notification unit can prioritize notifying information related to that region. The notification unit can also filter relevant information based on the relevant party's current location. Furthermore, the notification unit can narrow down the necessary information by considering the geographical location of the relevant parties. This allows the notification unit to provide relevant information based on the geographical location of the relevant parties. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can use AI to notify information based on the geographical location of the relevant parties. Alternatively, the notification unit can also send notifications based on information that the user manually inputs using geographical location information, without using AI.

[0095] The notification unit can analyze the social media activity of stakeholders and notify relevant information at the time of notification. For example, the notification unit can analyze the social media activity of stakeholders and notify relevant information at the time of notification. For example, the notification unit can notify relevant information based on information shared by stakeholders on social media. The notification unit can also prioritize notifying information related to topics of interest from the stakeholders' social media activity. Furthermore, the notification unit can analyze the social media activity history of stakeholders and narrow down the necessary information. This allows for the provision of highly relevant information based on the stakeholders' social media activity. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can use AI to analyze the social media activity of stakeholders and notify relevant information. Alternatively, the notification unit can also notify users based on their manual input of social media activity without using AI.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The handover support system can also be equipped with a voice recognition unit. The voice recognition unit allows users to give instructions by voice and perform document searches and email searches based on those instructions. For example, if a user says, "Search for the progress report for Project X," the voice recognition unit can analyze the instruction and send it to the document search unit. The voice recognition unit can also be used by users to request summaries by voice. For example, if a user says, "Summarize the latest meeting minutes," the unit can send that instruction to the summarization unit. Furthermore, the voice recognition unit can also be used by users to request notifications by voice. For example, if a user says, "Notify the new person in charge of the progress," the unit can send that instruction to the notification unit. This allows users to easily operate the system by voice, further improving the efficiency of the handover process.

[0098] The handover support system can also include a translation unit. This unit can automatically translate documents and emails created in different languages. For example, if the document search unit searches for a document written in English, the translation unit can translate that document into Japanese. Similarly, if the email search unit searches for an email written in a foreign language, the translation unit can translate that email into the user's native language. Furthermore, the translation unit can also translate information summarized by the summarization unit into different languages. For example, it can translate information summarized in English by the summarization unit into Japanese. This facilitates smooth handovers between users who speak different languages.

[0099] The handover support system can also be equipped with an image analysis unit. The image analysis unit can analyze images contained in documents and emails and extract relevant information. For example, if the document search unit searches for a project progress report, the image analysis unit can analyze the graphs and charts contained in the report and extract important data. Similarly, if the email search unit searches for images attached to emails, the image analysis unit can analyze those images and extract relevant information. Furthermore, the image analysis unit can also take the content of images into consideration when the summarization unit summarizes the information. For example, when the summarization unit summarizes meeting minutes, it can reflect the content of charts and tables contained in the minutes in the summary. This allows for the effective use of information from documents and emails that include images.

[0100] The handover support system can also include a schedule management unit. This unit manages the user's schedule and optimizes the timing of handover tasks. For example, it can analyze the user's calendar and suggest suitable times for handover work. It can also track the progress of handover tasks and send reminders as needed. Furthermore, it can coordinate the schedules of stakeholders and set dates and times for handover meetings. This ensures that handover tasks proceed systematically and are completed efficiently.

[0101] The handover support system can also include a feedback collection unit. This unit can collect feedback from users after the handover process is complete, helping to improve the system. For example, the feedback collection unit can conduct surveys regarding user satisfaction with the handover process and areas for improvement. It can also analyze the user feedback and generate suggestions for improving the system's functionality and usability. Furthermore, based on user feedback, the feedback collection unit can implement improvements to optimize the handover process. This allows the handover support system to continuously improve according to user needs, supporting more effective handover operations.

[0102] The handover support system also includes an emotion estimation unit that can adjust the progress of the handover process based on the user's emotions. For example, if the user is feeling stressed, the emotion estimation unit can break down the handover process into smaller, more manageable steps to reduce the burden. Conversely, if the user is relaxed, the emotion estimation unit can expedite the handover process. Furthermore, if the user is in a hurry, the emotion estimation unit can prioritize the most important tasks. This allows the system to adjust the handover process according to the user's emotions, resulting in an efficient and effective handover.

[0103] The handover support system also includes an emotion estimation unit that can adjust the content of notifications based on the user's emotions. For example, if the user is feeling stressed, the emotion estimation unit can provide a concise and to-the-point notification. If the user is relaxed, it can provide a notification with more detailed information. Furthermore, if the user is in a hurry, it can provide a notification that can be quickly understood. This allows the system to adjust the content of notifications according to the user's emotions and provide more appropriate information.

[0104] The handover support system also includes an emotion estimation unit that can adjust the format of the summary based on the user's emotions. For example, if the user is feeling stressed, the emotion estimation unit can provide a concise and to-the-point summary. If the user is relaxed, it can provide a summary with more detailed information. Furthermore, if the user is in a hurry, it can provide a summary that can be quickly understood. This allows the system to adjust the format of the summary according to the user's emotions and provide more relevant information.

[0105] The handover support system also includes an emotion estimation unit that can filter the results of document searches based on the user's emotions. For example, if the user is feeling stressed, the emotion estimation unit can prioritize displaying the most important documents. Conversely, if the user is relaxed, it can display a wide range of highly relevant documents. Furthermore, if the user is in a hurry, the emotion estimation unit can prioritize displaying documents that can be accessed quickly. This allows the system to filter document search results according to the user's emotions, providing more appropriate information.

[0106] The handover support system also includes an emotion estimation unit that can filter email search results based on the user's emotions. For example, if the user is feeling stressed, the emotion estimation unit can prioritize displaying the most important emails. Conversely, if the user is relaxed, it can display a wide range of relevant emails. Furthermore, if the user is in a hurry, it can display emails that can be accessed quickly at the top of the list. This allows the system to filter email search results according to the user's emotions, providing more relevant information.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The document search unit searches for documents related to the tasks to be handed over. The document search unit can quickly find relevant documents such as project progress reports and meeting minutes. The document search unit can use AI to search for documents based on relevant keywords and can also filter documents based on conditions specified by the user. For example, it can prioritize searching for documents created within a specific period or documents created by a specific person. Step 2: The email search unit searches past emails. The email search unit can find emails related to a specific project or emails containing important notices. The email search unit can use AI to analyze the content of emails and extract the information necessary for the handover. It can also filter emails based on conditions specified by the user. For example, it can prioritize searching for emails sent and received within a specific period or emails from specific senders. Step 3: The summarization unit summarizes the documents and emails retrieved by the document search unit and the email search unit. The summarization unit can efficiently summarize vast amounts of information using AI. The summarization unit performs summarization based on the length of the text and the importance of the information being summarized, and can also adjust the level of detail of the summary based on conditions specified by the user. For example, it can summarize highly important information in detail and less important information concisely. Step 4: The notification unit notifies stakeholders of the information summarized by the summarization unit. The notification unit can notify stakeholders via methods such as email, push notifications, and SMS notifications. The notification unit can use AI to notify stakeholders at the appropriate time and can also adjust the notification priority based on user-specified conditions. For example, it can prioritize notifications for high-priority information and postpone notifications for lower-priority information.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] For example, the document search unit is implemented by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14. For example, the email search unit is implemented by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14. For example, the summarization unit is implemented by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14. For example, the notification unit is implemented by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] For example, the document search unit is implemented by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214. For example, the email search unit is implemented by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214. For example, the summarization unit is implemented by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214. For example, the notification unit is implemented by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] For example, the document search unit is implemented by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314. For example, the email search unit is implemented by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314. For example, the summarization unit is implemented by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314. For example, the notification unit is implemented by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] For example, the document search unit is implemented by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414. For example, the email search unit is implemented by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414. For example, the summarization unit is implemented by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414. For example, the notification unit is implemented by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) The document search department searches for documents related to the tasks to be handed over, A mail search unit that searches past emails related to the tasks to be handed over, A summarization unit that summarizes the documents retrieved by the aforementioned document search unit and the emails retrieved by the aforementioned email search unit, The system includes a notification unit that notifies relevant parties of the information summarized by the summarization unit. A system characterized by the following features. (Note 2) The aforementioned data search unit, The AI ​​automatically searches for documents related to the tasks to be handed over. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned email search unit is: The AI ​​searches past emails and extracts the information necessary for the handover. The system described in Appendix 1, characterized by the features described herein. (Note 4) The summary section above is, The AI ​​summarizes the documents and emails retrieved by the document search and email search departments. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned notification unit, Notify relevant parties of the summarized information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned data search unit, It estimates the user's emotions and adjusts the priority of the document search based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned data search unit, When searching for materials, the system analyzes the user's past search history and selects the appropriate search method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned data search unit, When searching for materials, 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 9) The aforementioned data search unit, It estimates the user's sentiment and adjusts the display order of search results based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned data search unit, When searching for materials, the system prioritizes finding highly relevant materials based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned data search unit, When searching for materials, the system analyzes the user's social media activity and searches for relevant materials. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned email search unit is: It estimates the user's sentiment and adjusts email search priorities based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned email search unit is: When searching for emails, the system analyzes the user's past email sending and receiving history to select the most suitable search method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned email search unit is: When searching emails, filter based on specific projects or areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned email search unit is: It estimates the user's sentiment and adjusts the display order of search results based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned email search unit is: When searching for emails, the system prioritizes finding relevant emails based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned email search unit is: When searching for emails, the system analyzes the user's social media activity and searches for relevant emails. The system described in Appendix 1, characterized by the features described herein. (Note 18) The summary section above is, It estimates the user's emotions and adjusts the way the summary is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The summary section above is, When generating a summary, adjust the level of detail in the summary based on the importance of the documents and emails. The system described in Appendix 1, characterized by the features described herein. (Note 20) The summary section above is, When generating summaries, different summarization algorithms are applied depending on the category of the document and email. The system described in Appendix 1, characterized by the features described herein. (Note 21) The summary section above is, It estimates the user's sentiment and adjusts the length of the summary based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The summary section above is, When generating summaries, prioritize summaries based on the submission dates of the materials and emails. The system described in Appendix 1, characterized by the features described herein. (Note 23) The summary section above is, When generating summaries, the order of the summaries is adjusted based on the relevance of the documents and emails. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned notification unit, It estimates the user's emotions and adjusts the way notifications are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned notification unit, When sending a notification, the system analyzes the past notification history of the relevant parties and selects the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification unit, When sending notifications, filtering will be performed based on the current projects and areas of interest of the stakeholders. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, When sending notifications, the system prioritizes sending highly relevant information based on the geographical location of the relevant parties. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, When sending a notification, the system analyzes the social media activity of those involved and provides relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 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 document search department searches for documents related to the tasks to be handed over, A mail search unit that searches past emails related to the tasks to be handed over, A summarization unit that summarizes the documents retrieved by the aforementioned document search unit and the emails retrieved by the aforementioned email search unit, The system includes a notification unit that notifies relevant parties of the information summarized by the summarization unit. A system characterized by the following features.

2. The aforementioned data search unit, The AI ​​automatically searches for documents related to the tasks to be handed over. The system according to feature 1.

3. The aforementioned email search unit is: The AI ​​searches past emails and extracts the information necessary for the handover. The system according to feature 1.

4. The summary section above is, The AI ​​summarizes the documents and emails retrieved by the aforementioned document search unit and email search unit. The system according to feature 1.

5. The aforementioned notification unit, Notify relevant parties of the summarized information. The system according to feature 1.

6. The aforementioned data search unit, It estimates the user's emotions and adjusts the priority of the document search based on the estimated user emotions. The system according to feature 1.

7. The aforementioned data search unit, When searching for materials, the system analyzes the user's past search history and selects the appropriate search method. The system according to feature 1.

8. The aforementioned data search unit, When searching for materials, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

9. The aforementioned data search unit, It estimates the user's sentiment and adjusts the display order of search results based on the estimated user sentiment. The system according to feature 1.

10. The aforementioned data search unit, When searching for materials, the system prioritizes finding highly relevant materials based on the user's geographical location. The system according to feature 1.

11. The aforementioned data search unit, When searching for materials, the system analyzes the user's social media activity and searches for relevant materials. The system according to feature 1.

Images