system

Abstract

The system according to this embodiment aims to efficiently facilitate the handover of business operations and reduce the burden on the person taking over the operations. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects information necessary for the handover. The analysis unit analyzes the information collected by the collection unit and organizes the information necessary for the handover. The provision unit provides the latest information in response to questions from the party receiving the handover, based on the information organized by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of 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

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that a large burden is imposed on the successor in the transfer of business, and the organization and provision of information are not carried out efficiently.

[0005] The system according to the embodiment aims to efficiently perform the transfer of business and reduce the burden on the successor.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects information necessary for the handover. The analysis unit analyzes the information collected by the collection unit and organizes the information necessary for the handover. The provision unit provides the latest information in response to questions from the party receiving the handover, based on the information organized by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently facilitate the handover of tasks and reduce the burden on the person taking over. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a reception 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 reception 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 utilizes an AI agent to streamline the handover process when an employee's duties are handed over due to transfer or resignation. First, the AI ​​agent collects the information necessary for the handover. Next, the AI ​​analyzes the collected information and organizes the information necessary for the handover. Furthermore, the AI ​​agent provides the latest information in response to questions from the person receiving the handover. This mechanism ensures a smooth handover process and reduces the burden on the person receiving the handover. It also prevents a decline in motivation for the person performing the handover and minimizes the impact on the workplace. For example, the handover support system collects project details, Excel manuals, numerical data, information on data linking departments, communication history with partners, email content, etc. Next, the AI ​​analyzes the collected information and organizes procedures for numerical reporting conducted every Tuesday, how to use SQL, how to link data in BOX, etc. Furthermore, the handover support system provides the latest information in response to questions from the person receiving the handover. For example, it can address situations such as "a new employee who joined this month wants to know the progress of the project," "they want to know a specific manual," or "they want to be briefly informed of the points to check." This system ensures a smooth handover process and reduces the burden on the person receiving the handover. It also prevents a decline in motivation for the person performing the handover and minimizes the impact on the workplace. In short, the handover support system streamlines the handover process and reduces the burden on the person receiving the handover.

[0029] The handover support system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects information necessary for the handover. For example, the collection unit collects information such as project details, Excel manuals, numerical data, information on data linking departments, communication history with partners, and email content. The collection unit can automatically collect this information using AI. The analysis unit analyzes the information collected by the collection unit and organizes the information necessary for the handover. For example, the analysis unit organizes the procedures for numerical reporting conducted every Tuesday, how to use SQL, and how to link data in BOX. The analysis unit can automatically analyze and organize this information using AI. The provision unit provides the latest information to the person receiving the handover based on the information organized by the analysis unit. For example, the provision unit can respond to requests such as "a new employee who joined this month wants to know the progress of the project," "they want to know a specific manual," or "they want to be briefly informed of the points to check." The provision unit can automatically provide the latest information to these requests using AI. As a result, the handover support system according to this embodiment can streamline the handover process and reduce the burden on the party receiving the handover.

[0030] The data collection unit gathers information necessary for the handover. For example, the unit collects information such as project details, Excel manuals, numerical data, data linkage department information, partner communication history, and email content. Specifically, project details include project objectives, progress, key milestones, and the roles of involved members. Excel manuals include data entry procedures, formula settings, and graph creation methods. Numerical data includes sales data, cost data, and performance indicators, which are important for evaluating project progress and results. Data linkage department information includes information on departments providing and receiving data, allowing for an understanding of the data flow. Partner communication history includes past communication content, agreements, and unresolved issues, enabling smooth maintenance of relationships with partners. Email content includes important announcements, instructions, and feedback, allowing for an understanding of the communication history. The data collection unit can automatically collect this information using AI. The AI ​​uses natural language processing technology to extract necessary information from emails and documents and store it in a database. Furthermore, the AI ​​regularly updates information and automatically collects any new information that is added. This allows the data collection unit to efficiently gather the information necessary for handover and always maintain the latest information.

[0031] The analysis department analyzes the information collected by the data collection department and organizes the information necessary for the handover. For example, the analysis department organizes procedures for numerical reporting conducted every Tuesday, methods for using SQL, and methods for data integration with BOX. Specifically, the numerical reporting procedures include data collection methods, report creation procedures, and methods for conducting reporting meetings. Methods for using SQL include methods for connecting to the database, creating queries, and extracting data. Methods for data integration with BOX include methods for uploading files, setting up sharing, and managing access permissions. The analysis department can use AI to automatically analyze and organize this information. The AI ​​uses machine learning algorithms to classify the collected information and group related information. The AI ​​also evaluates the importance and relevance of the information and prioritizes the information necessary for the handover. This allows the analysis department to efficiently organize the information necessary for the handover, enabling the recipient to quickly obtain the necessary information. Furthermore, the analysis department not only organizes the information but also visualizes it. For example, it converts numerical data into graphs and charts and provides it in a visually easy-to-understand format. This makes it easier for the person receiving the handover to intuitively understand the information, allowing the handover process to proceed smoothly.

[0032] The information provision department provides up-to-date information to those receiving the handover, based on the information compiled by the analysis department. For example, the department can handle situations such as "a new employee who joined this month wants to know the project's progress," "they need a specific manual," or "they want a concise overview of points to check." Specifically, if a new employee wants to know the project's progress, the department provides information such as the project overview, progress status, key milestones, and future plans. If they need a specific manual, the department provides specific procedures such as Excel manuals, SQL usage instructions, and BOX data integration methods. If they want a concise overview of points to check, the department provides a concise summary of important tasks, points to note, and priorities. The information provision department can use AI to automatically provide the latest information in response to these questions. The AI ​​uses natural language processing technology to understand user questions, search for appropriate information, and provide it. Furthermore, the AI ​​collects user feedback and continuously improves the accuracy and effectiveness of the information provided. This allows the information provision department to enable those receiving the handover to quickly and accurately obtain the necessary information, streamlining the handover process. In addition, the information provision department can reliably transmit information using multiple communication methods. For example, by using a combination of email, chat, and voice calls, important information can be reliably delivered. This reduces the burden on the receiving party and allows the handover process to proceed smoothly.

[0033] The data collection unit can collect information such as project details, Excel manuals, numerical data, data linkage department information, partner communication history, and email content. For example, the data collection unit can collect project details such as project objectives, schedules, and deliverables. The data collection unit can also collect Excel manuals such as Excel operation procedures, usage examples, and points to note. The data collection unit can also collect numerical data such as sales data, access numbers, and statistical information. The data collection unit can also collect data linkage department information such as department name, person in charge, and contact information. The data collection unit can also collect partner communication history such as email exchanges, meeting minutes, and contract details. The data collection unit can also collect email content such as criteria for selecting important emails and how to save them. This allows for a smooth handover process by comprehensively collecting the necessary information. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input project details and numerical data into a generating AI, and the generating AI can automatically collect the information.

[0034] The analysis department can analyze the collected information and organize procedures for weekly numerical reporting (every Tuesday), SQL usage, and data integration methods with Box. For example, the analysis department can organize the numerical reporting procedures, such as the format, frequency, and target audience of the reports. The analysis department can also organize how to use SQL, such as how to write queries and how to operate the database. The analysis department can also organize how to integrate data with Box, such as how to upload files and how to configure sharing settings. By organizing the information in this way, the handover process becomes more efficient. Some or all of the above processes in the analysis department may be performed using AI, or they may not. For example, the analysis department can input the collected numerical data and SQL usage into a generating AI, which can then automatically organize the information.

[0035] The information provision department can provide the latest information in response to questions from those taking over. For example, the information provision department can handle situations such as "a new employee who joined this month wants to know the progress of a project," "they want to know about a specific manual," or "they want a concise overview of the points to check." The information provision department needs to clearly define the specifics of the latest information and the update frequency. For example, it can set the last update date and real-time updates. This allows those taking over to quickly obtain the necessary information. Some or all of the above processing in the information provision department may be performed using AI or not. For example, the information provision department can input questions from those taking over into a generating AI, and the generating AI can automatically provide the latest information.

[0036] The data collection unit can analyze past handover history and select the optimal information collection method. For example, the data collection unit can analyze successful handover methods from past handover history and apply similar methods. The data collection unit can also take measures to avoid unsuccessful handover methods from past handover history. Based on past handover history, the data collection unit can also select the optimal timing and means for information collection. By selecting the optimal information collection method based on past handover history, the efficiency of the handover process is improved. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past handover history data into a generating AI, which can then automatically select the optimal information collection method.

[0037] The data collection unit can filter information based on the user's current projects and areas of interest during data collection. For example, the data collection unit prioritizes collecting information related to the project the user is currently working on. The data collection unit can also filter and provide relevant information based on the user's areas of interest. The data collection unit can also appropriately filter necessary information according to the user's project progress. This allows for efficient collection of necessary information by filtering information based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's project details and areas of interest into a generating AI, which can then automatically filter the information.

[0038] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location during data collection. For example, if the user is in a specific region, the data collection unit will prioritize collecting information related to that region. The data collection unit can also filter and provide relevant data based on the user's geographical location. If the user is on the move, the data collection unit can also collect necessary information based on their current location. This allows for the efficient collection of highly relevant information by considering the user's geographical location. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location into a generating AI, which can then automatically collect highly relevant information.

[0039] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can identify topics of interest from the user's social media activity and collect relevant information. The data collection unit can also analyze the user's social media posts and provide necessary information. The data collection unit can also collect relevant information by referring to the activities of the user's social media followers and friends. In this way, by analyzing the user's social media activity and collecting information, it is possible to provide information tailored to the user's interests. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into a generating AI, and the generating AI can automatically collect relevant information.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis on important information. The analysis unit can also perform a concise analysis on less important information. The analysis unit can also determine the priority of the analysis according to the importance of the information. This allows for efficient information analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input information importance data into a generating AI, and the generating AI can automatically adjust the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply statistical analysis algorithms to numerical data. The analysis unit can also apply natural language processing algorithms to text data. The analysis unit can also apply image analysis algorithms to image data. By applying the appropriate analysis algorithm according to the category of information, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input information category data into a generating AI, and the generating AI can automatically apply an appropriate analysis algorithm.

[0042] The analysis unit can determine the priority of analysis based on the information submission timing during the analysis process. For example, the analysis unit will prioritize the analysis of urgent information. The analysis unit can also quickly analyze information with an approaching submission deadline. The analysis unit can also adjust the priority of analysis according to the submission timing. This enables efficient information analysis by determining the priority of analysis based on the information submission timing. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input information submission timing data into a generating AI, which can then automatically determine the priority of analysis.

[0043] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant information. The analysis unit may also postpone the analysis of less relevant information. The analysis unit can also adjust the order of analysis according to the relevance of the information. This allows for efficient information analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input information relevance data into a generating AI, and the generating AI can automatically adjust the order of analysis.

[0044] The information provider can select the most suitable method of information provision by referring to the user's past question history when providing information. For example, the information provider can provide relevant information based on the content of questions the user has asked in the past. The information provider can also select the most suitable method of information provision from the user's question history. The information provider can also analyze the user's past question history and provide the necessary information. This allows the information provider to select the most suitable method of information provision by referring to the user's past question history. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input the user's past question history data into a generating AI, and the generating AI can automatically select the most suitable method of information provision.

[0045] The information delivery unit can customize the means of information delivery based on the user's current situation. For example, if the user is in a meeting, the unit can provide a concise summary. If the user is on the move, the unit can also provide information via audio. If the user is working at a desk, the unit can also provide a detailed document. By customizing the means of information delivery based on the user's current situation, it becomes possible to provide the most suitable information for the user. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit can input the user's current situation data into a generating AI, which can then automatically customize the means of information delivery.

[0046] The information provider can select the most appropriate method of information delivery by considering the user's geographical location when providing information. For example, if the user is in a specific region, the provider can provide information related to that region. The provider can also provide relevant data based on the user's geographical location. If the user is on the move, the provider can provide necessary information based on their current location. By selecting the information delivery method while considering the user's geographical location, the provider can provide highly relevant information. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider can input the user's geographical location into a generating AI, which can then automatically select the most appropriate method of information delivery.

[0047] The information provider can analyze the user's social media activity and propose methods for providing information. For example, the provider can identify topics of interest from the user's social media activity and provide relevant information. The provider can also analyze the user's social media posts and provide necessary information. The provider can also refer to the activities of the user's social media followers and friends and provide relevant information. In this way, by analyzing the user's social media activity and proposing methods for providing information, information tailored to the user's interests can be provided. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider can input the user's social media activity data into a generating AI, and the generating AI can automatically propose methods for providing information.

[0048] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0049] The data collection unit can analyze a user's past work history and prioritize the collection of information necessary for handover. For example, it can collect details and progress of projects the user has previously handled and organize the information needed for handover. The data collection unit can also collect information on how to use tools and systems the user has used in the past, providing useful information for handover. Furthermore, the data collection unit can collect information on interactions with partners and clients the user has worked with in the past, providing comprehensive information necessary for handover. This allows for the efficient collection of information necessary for handover based on the user's past work history.

[0050] The information delivery system can understand the user's current work situation in real time and provide optimal information. For example, if the user is in a meeting, information related to the meeting will be prioritized. If the user is on the go, information can be provided via audio. Furthermore, if the user is working at their desk, detailed documents can be provided. This enables the provision of information that is best suited to the user's current work situation.

[0051] The analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, it can perform a detailed analysis on important information and a concise analysis on less important information. Furthermore, it can determine the priority of the analysis according to the importance of the information. This allows for efficient information analysis by adjusting the level of detail of the analysis according to the importance of the information.

[0052] The data collection unit can filter information based on the user's current projects and areas of interest. For example, it can prioritize collecting information related to the project the user is currently working on. It can also filter and provide relevant information based on the user's areas of interest. Furthermore, it can appropriately filter the information needed according to the user's project progress. This allows for efficient collection of necessary information by filtering it based on the user's current projects and areas of interest.

[0053] The information provider can select the most appropriate method of information delivery by referring to the user's past question history. For example, it can provide relevant information based on the user's past questions. It can also select the most appropriate method of information delivery from the user's question history. Furthermore, it can analyze the user's past question history and provide the necessary information. This allows the system to select the most appropriate method of information delivery by referring to the user's past question history.

[0054] The following briefly describes the processing flow for example form 1.

[0055] Step 1: The collection unit gathers the information necessary for the handover. For example, the collection unit collects information such as project details, Excel manuals, numerical data, information on data linking departments, communication history with partners, and email content. The collection unit can use AI to automatically collect this information. Step 2: The analysis unit analyzes the information collected by the data collection unit and organizes the information necessary for handover. For example, the analysis unit organizes procedures for numerical reporting conducted every Tuesday, how to use SQL, and how to link data in BOX. The analysis unit can use AI to automatically analyze and organize this information. Step 3: The information provision department provides the latest information to the recipient in response to questions based on the information compiled by the analysis department. The information provision department can handle situations such as, "A new employee who joined this month wants to know the progress of the project," "They want to know about a specific manual," or "They want a concise overview of the points to check." The information provision department can use AI to automatically provide the latest information in response to these questions.

[0056] (Example of form 2) The handover support system according to an embodiment of the present invention is a system that utilizes an AI agent to streamline the handover process when an employee's duties are handed over due to transfer or resignation. First, the AI ​​agent collects the information necessary for the handover. Next, the AI ​​analyzes the collected information and organizes the information necessary for the handover. Furthermore, the AI ​​agent provides the latest information in response to questions from the person receiving the handover. This mechanism ensures a smooth handover process and reduces the burden on the person receiving the handover. It also prevents a decline in motivation for the person performing the handover and minimizes the impact on the workplace. For example, the handover support system collects project details, Excel manuals, numerical data, information on data linking departments, communication history with partners, email content, etc. Next, the AI ​​analyzes the collected information and organizes procedures for numerical reporting conducted every Tuesday, how to use SQL, how to link data in BOX, etc. Furthermore, the handover support system provides the latest information in response to questions from the person receiving the handover. For example, it can address situations such as "a new employee who joined this month wants to know the progress of the project," "they want to know a specific manual," or "they want to be briefly informed of the points to check." This system ensures a smooth handover process and reduces the burden on the person receiving the handover. It also prevents a decline in motivation for the person performing the handover and minimizes the impact on the workplace. In short, the handover support system streamlines the handover process and reduces the burden on the person receiving the handover.

[0057] The handover support system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects information necessary for the handover. For example, the collection unit collects information such as project details, Excel manuals, numerical data, information on data linking departments, communication history with partners, and email content. The collection unit can automatically collect this information using AI. The analysis unit analyzes the information collected by the collection unit and organizes the information necessary for the handover. For example, the analysis unit organizes the procedures for numerical reporting conducted every Tuesday, how to use SQL, and how to link data in BOX. The analysis unit can automatically analyze and organize this information using AI. The provision unit provides the latest information to the person receiving the handover based on the information organized by the analysis unit. For example, the provision unit can respond to requests such as "a new employee who joined this month wants to know the progress of the project," "they want to know a specific manual," or "they want to be briefly informed of the points to check." The provision unit can automatically provide the latest information to these requests using AI. As a result, the handover support system according to this embodiment can streamline the handover process and reduce the burden on the party receiving the handover.

[0058] The data collection unit gathers information necessary for the handover. For example, the unit collects information such as project details, Excel manuals, numerical data, data linkage department information, partner communication history, and email content. Specifically, project details include project objectives, progress, key milestones, and the roles of involved members. Excel manuals include data entry procedures, formula settings, and graph creation methods. Numerical data includes sales data, cost data, and performance indicators, which are important for evaluating project progress and results. Data linkage department information includes information on departments providing and receiving data, allowing for an understanding of the data flow. Partner communication history includes past communication content, agreements, and unresolved issues, enabling smooth maintenance of relationships with partners. Email content includes important announcements, instructions, and feedback, allowing for an understanding of the communication history. The data collection unit can automatically collect this information using AI. The AI ​​uses natural language processing technology to extract necessary information from emails and documents and store it in a database. Furthermore, the AI ​​regularly updates information and automatically collects any new information that is added. This allows the data collection unit to efficiently gather the information necessary for handover and always maintain the latest information.

[0059] The analysis department analyzes the information collected by the data collection department and organizes the information necessary for the handover. For example, the analysis department organizes procedures for numerical reporting conducted every Tuesday, methods for using SQL, and methods for data integration with BOX. Specifically, the numerical reporting procedures include data collection methods, report creation procedures, and methods for conducting reporting meetings. Methods for using SQL include methods for connecting to the database, creating queries, and extracting data. Methods for data integration with BOX include methods for uploading files, setting up sharing, and managing access permissions. The analysis department can use AI to automatically analyze and organize this information. The AI ​​uses machine learning algorithms to classify the collected information and group related information. The AI ​​also evaluates the importance and relevance of the information and prioritizes the information necessary for the handover. This allows the analysis department to efficiently organize the information necessary for the handover, enabling the recipient to quickly obtain the necessary information. Furthermore, the analysis department not only organizes the information but also visualizes it. For example, it converts numerical data into graphs and charts and provides it in a visually easy-to-understand format. This makes it easier for the person receiving the handover to intuitively understand the information, allowing the handover process to proceed smoothly.

[0060] The information provision department provides up-to-date information to those receiving the handover, based on the information compiled by the analysis department. For example, the department can handle situations such as "a new employee who joined this month wants to know the project's progress," "they need a specific manual," or "they want a concise overview of points to check." Specifically, if a new employee wants to know the project's progress, the department provides information such as the project overview, progress status, key milestones, and future plans. If they need a specific manual, the department provides specific procedures such as Excel manuals, SQL usage instructions, and BOX data integration methods. If they want a concise overview of points to check, the department provides a concise summary of important tasks, points to note, and priorities. The information provision department can use AI to automatically provide the latest information in response to these questions. The AI ​​uses natural language processing technology to understand user questions, search for appropriate information, and provide it. Furthermore, the AI ​​collects user feedback and continuously improves the accuracy and effectiveness of the information provided. This allows the information provision department to enable those receiving the handover to quickly and accurately obtain the necessary information, streamlining the handover process. In addition, the information provision department can reliably transmit information using multiple communication methods. For example, by using a combination of email, chat, and voice calls, important information can be reliably delivered. This reduces the burden on the receiving party and allows the handover process to proceed smoothly.

[0061] The data collection unit can collect information such as project details, Excel manuals, numerical data, data linkage department information, partner communication history, and email content. For example, the data collection unit can collect project details such as project objectives, schedules, and deliverables. The data collection unit can also collect Excel manuals such as Excel operation procedures, usage examples, and points to note. The data collection unit can also collect numerical data such as sales data, access numbers, and statistical information. The data collection unit can also collect data linkage department information such as department name, person in charge, and contact information. The data collection unit can also collect partner communication history such as email exchanges, meeting minutes, and contract details. The data collection unit can also collect email content such as criteria for selecting important emails and how to save them. This allows for a smooth handover process by comprehensively collecting the necessary information. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input project details and numerical data into a generating AI, and the generating AI can automatically collect the information.

[0062] The analysis department can analyze the collected information and organize procedures for weekly numerical reporting (every Tuesday), SQL usage, and data integration methods with Box. For example, the analysis department can organize the numerical reporting procedures, such as the format, frequency, and target audience of the reports. The analysis department can also organize how to use SQL, such as how to write queries and how to operate the database. The analysis department can also organize how to integrate data with Box, such as how to upload files and how to configure sharing settings. By organizing the information in this way, the handover process becomes more efficient. Some or all of the above processes in the analysis department may be performed using AI, or they may not. For example, the analysis department can input the collected numerical data and SQL usage into a generating AI, which can then automatically organize the information.

[0063] The information provision department can provide the latest information in response to questions from those taking over. For example, the information provision department can handle situations such as "a new employee who joined this month wants to know the progress of a project," "they want to know about a specific manual," or "they want a concise overview of the points to check." The information provision department needs to clearly define the specifics of the latest information and the update frequency. For example, it can set the last update date and real-time updates. This allows those taking over to quickly obtain the necessary information. Some or all of the above processing in the information provision department may be performed using AI or not. For example, the information provision department can input questions from those taking over into a generating AI, and the generating AI can automatically provide the latest information.

[0064] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of information collection to alleviate the burden. If the user is relaxed, the data collection unit can also collect more detailed information and provide more data. If the user is in a hurry, the data collection unit can prioritize the collection of important information and provide it quickly. This reduces the burden on the user by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can automatically estimate the emotions and adjust the timing of information collection.

[0065] The data collection unit can analyze past handover history and select the optimal information collection method. For example, the data collection unit can analyze successful handover methods from past handover history and apply similar methods. The data collection unit can also take measures to avoid unsuccessful handover methods from past handover history. Based on past handover history, the data collection unit can also select the optimal timing and means for information collection. By selecting the optimal information collection method based on past handover history, the efficiency of the handover process is improved. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past handover history data into a generating AI, which can then automatically select the optimal information collection method.

[0066] The data collection unit can filter information based on the user's current projects and areas of interest during data collection. For example, the data collection unit prioritizes collecting information related to the project the user is currently working on. The data collection unit can also filter and provide relevant information based on the user's areas of interest. The data collection unit can also appropriately filter necessary information according to the user's project progress. This allows for efficient collection of necessary information by filtering information based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's project details and areas of interest into a generating AI, which can then automatically filter the information.

[0067] The data collection unit can estimate the user's emotions and prioritize the information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting important information to reduce the user's burden. If the user is relaxed, the data collection unit can also collect detailed information and provide more data. If the user is in a hurry, the data collection unit can quickly collect and provide the necessary information. This reduces the user's burden by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can automatically estimate emotions and determine the priority of information.

[0068] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location during data collection. For example, if the user is in a specific region, the data collection unit will prioritize collecting information related to that region. The data collection unit can also filter and provide relevant data based on the user's geographical location. If the user is on the move, the data collection unit can also collect necessary information based on their current location. This allows for the efficient collection of highly relevant information by considering the user's geographical location. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location into a generating AI, which can then automatically collect highly relevant information.

[0069] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can identify topics of interest from the user's social media activity and collect relevant information. The data collection unit can also analyze the user's social media posts and provide necessary information. The data collection unit can also collect relevant information by referring to the activities of the user's social media followers and friends. In this way, by analyzing the user's social media activity and collecting information, it is possible to provide information tailored to the user's interests. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into a generating AI, and the generating AI can automatically collect relevant information.

[0070] The analysis unit can estimate the user's emotions and adjust the information analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a concise and to-the-point analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is in a hurry, the analysis unit can also provide a quick analysis result. In this way, by adjusting the information analysis method according to the user's emotions, the system can provide the user with the most optimal analysis result. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI, which can automatically estimate emotions and adjust the information analysis method.

[0071] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis on important information. The analysis unit can also perform a concise analysis on less important information. The analysis unit can also determine the priority of the analysis according to the importance of the information. This allows for efficient information analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input information importance data into a generating AI, and the generating AI can automatically adjust the level of detail of the analysis.

[0072] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply statistical analysis algorithms to numerical data. The analysis unit can also apply natural language processing algorithms to text data. The analysis unit can also apply image analysis algorithms to image data. By applying the appropriate analysis algorithm according to the category of information, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input information category data into a generating AI, and the generating AI can automatically apply an appropriate analysis algorithm.

[0073] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize analyzing important information. If the user is relaxed, the analysis unit can also perform a detailed analysis. If the user is in a hurry, the analysis unit can also perform a rapid analysis. This reduces the user's burden by determining the priority of analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI, which can automatically estimate emotions and determine the priority of analysis.

[0074] The analysis unit can determine the priority of analysis based on the information submission timing during the analysis process. For example, the analysis unit will prioritize the analysis of urgent information. The analysis unit can also quickly analyze information with an approaching submission deadline. The analysis unit can also adjust the priority of analysis according to the submission timing. This enables efficient information analysis by determining the priority of analysis based on the information submission timing. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input information submission timing data into a generating AI, which can then automatically determine the priority of analysis.

[0075] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant information. The analysis unit may also postpone the analysis of less relevant information. The analysis unit can also adjust the order of analysis according to the relevance of the information. This allows for efficient information analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input information relevance data into a generating AI, and the generating AI can automatically adjust the order of analysis.

[0076] The information provider can estimate the user's emotions and adjust the method of information delivery based on the estimated emotions. For example, if the user is stressed, the information provider will provide concise and to-the-point information. If the user is relaxed, the information provider may provide detailed information. If the user is in a hurry, the information provider may provide information quickly. By adjusting the method of information delivery according to the user's emotions, it becomes possible to provide the most optimal information for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input user emotion data into a generative AI, which can automatically estimate emotions and adjust the method of information delivery.

[0077] The information provider can select the most suitable method of information provision by referring to the user's past question history when providing information. For example, the information provider can provide relevant information based on the content of questions the user has asked in the past. The information provider can also select the most suitable method of information provision from the user's question history. The information provider can also analyze the user's past question history and provide the necessary information. This allows the information provider to select the most suitable method of information provision by referring to the user's past question history. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input the user's past question history data into a generating AI, and the generating AI can automatically select the most suitable method of information provision.

[0078] The information delivery unit can customize the means of information delivery based on the user's current situation. For example, if the user is in a meeting, the unit can provide a concise summary. If the user is on the move, the unit can also provide information via audio. If the user is working at a desk, the unit can also provide a detailed document. By customizing the means of information delivery based on the user's current situation, it becomes possible to provide the most suitable information for the user. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit can input the user's current situation data into a generating AI, which can then automatically customize the means of information delivery.

[0079] The information provider can estimate the user's emotions and determine the priority of information provision based on the estimated emotions. For example, if the user is stressed, the information provider will prioritize providing important information. If the user is relaxed, the information provider may also provide detailed information. If the user is in a hurry, the information provider may also provide necessary information quickly. This reduces the burden on the user by prioritizing information provision according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input user emotion data into a generative AI, which can automatically estimate emotions and determine the priority of information provision.

[0080] The information provider can select the most appropriate method of information delivery by considering the user's geographical location when providing information. For example, if the user is in a specific region, the provider can provide information related to that region. The provider can also provide relevant data based on the user's geographical location. If the user is on the move, the provider can provide necessary information based on their current location. By selecting the information delivery method while considering the user's geographical location, the provider can provide highly relevant information. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider can input the user's geographical location into a generating AI, which can then automatically select the most appropriate method of information delivery.

[0081] The information provider can analyze the user's social media activity and propose methods for providing information. For example, the provider can identify topics of interest from the user's social media activity and provide relevant information. The provider can also analyze the user's social media posts and provide necessary information. The provider can also refer to the activities of the user's social media followers and friends and provide relevant information. In this way, by analyzing the user's social media activity and proposing methods for providing information, information tailored to the user's interests can be provided. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider can input the user's social media activity data into a generating AI, and the generating AI can automatically propose methods for providing information.

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

[0083] The data collection unit can analyze a user's past work history and prioritize the collection of information necessary for handover. For example, it can collect details and progress of projects the user has previously handled and organize the information needed for handover. The data collection unit can also collect information on how to use tools and systems the user has used in the past, providing useful information for handover. Furthermore, the data collection unit can collect information on interactions with partners and clients the user has worked with in the past, providing comprehensive information necessary for handover. This allows for the efficient collection of information necessary for handover based on the user's past work history.

[0084] The analysis unit can estimate the user's emotions and adjust how the analysis results are presented based on those emotions. For example, if the user is stressed, the analysis results can be presented concisely. If the user is relaxed, detailed analysis results can be provided. Furthermore, if the user is in a hurry, important points can be prioritized. By adjusting how the analysis results are presented according to the user's emotions, it becomes possible to provide the user with the most optimal information.

[0085] The information delivery system can understand the user's current work situation in real time and provide optimal information. For example, if the user is in a meeting, information related to the meeting will be prioritized. If the user is on the go, information can be provided via audio. Furthermore, if the user is working at their desk, detailed documents can be provided. This enables the provision of information that is best suited to the user's current work situation.

[0086] The data collection unit can estimate the user's emotions and determine the priority of information collection based on those emotions. For example, if the user is stressed, it can prioritize collecting important information to reduce their burden. If the user is relaxed, it can collect detailed information and provide more data. Furthermore, if the user is in a hurry, it can quickly collect and provide the necessary information. In this way, prioritizing information collection according to the user's emotions can reduce the user's burden.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, it can perform a detailed analysis on important information and a concise analysis on less important information. Furthermore, it can determine the priority of the analysis according to the importance of the information. This allows for efficient information analysis by adjusting the level of detail of the analysis according to the importance of the information.

[0088] The information delivery system can estimate the user's emotions and adjust the method of information delivery based on those estimates. For example, if the user is stressed, it can provide concise and to-the-point information. If the user is relaxed, it can provide detailed information. Furthermore, if the user is in a hurry, it can provide information quickly. By adjusting the method of information delivery according to the user's emotions, it becomes possible to provide the most optimal information for the user.

[0089] The data collection unit can filter information based on the user's current projects and areas of interest. For example, it can prioritize collecting information related to the project the user is currently working on. It can also filter and provide relevant information based on the user's areas of interest. Furthermore, it can appropriately filter the information needed according to the user's project progress. This allows for efficient collection of necessary information by filtering it based on the user's current projects and areas of interest.

[0090] The analysis unit can estimate the user's emotions and determine the priority of analysis based on those emotions. For example, if the user is stressed, it will prioritize analyzing important information. If the user is relaxed, it can perform a more detailed analysis. Furthermore, if the user is in a hurry, it can perform a rapid analysis. By prioritizing analysis according to the user's emotions, the system can reduce the user's burden.

[0091] The information provider can select the most appropriate method of information delivery by referring to the user's past question history. For example, it can provide relevant information based on the user's past questions. It can also select the most appropriate method of information delivery from the user's question history. Furthermore, it can analyze the user's past question history and provide the necessary information. This allows the system to select the most appropriate method of information delivery by referring to the user's past question history.

[0092] The information delivery system can estimate the user's emotions and determine the priority of information delivery based on those emotions. For example, if the user is stressed, important information will be prioritized. If the user is relaxed, detailed information may be provided. Furthermore, if the user is in a hurry, necessary information can be provided quickly. This reduces the user's burden by prioritizing information delivery according to their emotions.

[0093] The following briefly describes the processing flow for example form 2.

[0094] Step 1: The collection unit gathers the information necessary for the handover. For example, the collection unit collects information such as project details, Excel manuals, numerical data, information on data linking departments, communication history with partners, and email content. The collection unit can use AI to automatically collect this information. Step 2: The analysis unit analyzes the information collected by the data collection unit and organizes the information necessary for handover. For example, the analysis unit organizes procedures for numerical reporting conducted every Tuesday, how to use SQL, and how to link data in BOX. The analysis unit can use AI to automatically analyze and organize this information. Step 3: The information provision department provides the latest information to the recipient in response to questions based on the information compiled by the analysis department. The information provision department can handle situations such as, "A new employee who joined this month wants to know the progress of the project," "They want to know about a specific manual," or "They want a concise overview of the points to check." The information provision department can use AI to automatically provide the latest information in response to these questions.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart device 14 and collects information such as project details and Excel manuals. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information and organizes the information necessary for the handover. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the latest information in response to questions from the recipient of the handover. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.).

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0112] 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.

[0113] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0114] Each of the multiple elements described above, including the data collection unit, analysis unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the smart glasses 214 and collects information such as project details and Excel manuals. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information and organizes the information necessary for the handover. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the latest information in response to questions from the recipient of the handover. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0115] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the headset terminal 314 and collects information such as project details and Excel manuals. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information and organizes the information necessary for the handover. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the latest information in response to questions from the recipient of the handover. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0131] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the data collection unit, analysis unit, and provision unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the robot 414 and collects information such as project details and Excel manuals. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information to organize the information necessary for the handover. The provision unit is implemented by the control unit 46A of the robot 414 and provides the latest information in response to questions from the party receiving the handover. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] (Note 1) A collection unit that collects information necessary for the handover, An analysis unit analyzes the information collected by the aforementioned collection unit and organizes the information necessary for the handover, The system includes a provisioning unit that provides the latest information in response to questions from the party receiving the handover, based on the information compiled by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect information such as project details, Excel manuals, numerical data, data integration department information, communication history with partners, and email content. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze the collected information and organize the procedures for weekly numerical reporting (to be done every Tuesday), how to use SQL, and how to integrate data with Box. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provide the latest information in response to questions from the person taking over. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Analyze past handover history and select the most suitable information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When gathering information, 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 8) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts the information analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way information is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing information, the system will refer to the user's past question history to select the most appropriate method of information delivery. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing information, customize the method of information delivery based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, The system estimates the user's emotions and prioritizes information provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing information, the optimal method of information delivery will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing information, we analyze users' social media activity and propose methods for providing that information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0167] 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. A collection unit that collects information necessary for the handover, An analysis unit analyzes the information collected by the aforementioned collection unit and organizes the information necessary for the handover, The system includes a provisioning unit that provides the latest information in response to questions from the party receiving the handover, based on the information compiled by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect information such as project details, Excel manuals, numerical data, data integration department information, communication history with partners, and email content. The system according to feature 1.

3. The aforementioned analysis unit, Analyze the collected information and organize the procedures for weekly numerical reporting (to be done every Tuesday), how to use SQL, and how to integrate data with Box. The system according to feature 1.

4. The aforementioned supply unit is, Provide the latest information in response to questions from the person taking over. The system according to feature 1.

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

6. The aforementioned collection unit is Analyze past handover history and select the most suitable information gathering method. The system according to feature 1.

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

8. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system according to feature 1.

10. The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system according to feature 1.

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