system

Abstract

The system according to this embodiment aims to facilitate the efficient handover of tasks and ensure the smooth progress of operations. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a generation unit. The collection unit collects past work history, documents, emails, and chat history. The analysis unit analyzes the information collected by the collection unit and automatically organizes and tags the necessary information to build a knowledge base. The generation unit automatically generates manuals and guidelines required by successors based on the knowledge base built by the analysis unit.

Description

Technical Field

[0001] The technology of this 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, due to insufficient handover of operations, there is a risk that the progress of operations may be delayed.

[0005] The system according to the embodiment aims to efficiently perform the handover of operations and smooth the progress of operations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, and a generation unit. The collection unit collects past work history, documents, emails, and chat history. The analysis unit analyzes the information collected by the collection unit and automatically organizes and tags the necessary information to build a knowledge base. The generation unit automatically generates manuals and guidelines required by successors based on the knowledge base built by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently transfer tasks and ensure the smooth progress of operations. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The business handover support system according to an embodiment of the present invention provides two mechanisms for resolving insufficient business handover using generating AI. This business handover support system combines an "AI Knowledge Assistant" that analyzes past business history, documents, emails, and chat history, and automatically organizes and tags necessary information to build a knowledge base, with an "AI Document Generator" that analyzes the predecessor's business history and documents and automatically generates manuals and guidelines needed by the successor. This eliminates business delays caused by insufficient handover, enabling the person in charge to proceed with their work quickly and efficiently. For example, the AI ​​Knowledge Assistant analyzes past project data, documents, emails, and chat history, and automatically organizes and tags necessary information to build a knowledge base. When the successor inputs a question, the AI ​​searches for appropriate documents and past interactions and provides relevant information. For example, if the successor inputs, "I want to know the procedure for a specific task that my predecessor performed," the AI ​​searches for relevant documents and emails and provides the procedure. Next, the AI ​​Document Generator analyzes the predecessor's business history and documents and automatically generates manuals and guidelines needed by the successor. This allows successors to quickly access necessary information and carry out their work smoothly. For example, the system analyzes the procedures for creating regular reports performed by the predecessor and generates a manual based on those procedures. This system yields the following benefits: First, it improves work efficiency. The person in charge can quickly grasp the work content and schedule of the predecessor and proceed with their work smoothly. Second, it is expected to reduce errors. Important tasks and email sending timings will not be missed, reducing work errors. Finally, it ensures continuity of work. Even if the person in charge changes, a system can be established that allows work to continue without interruption. In this way, by combining two systems that utilize generation AI, delays in work due to insufficient handover are eliminated, and the person in charge can proceed with their work quickly and efficiently. Furthermore, synergistic effects can be obtained by introducing it to business partners as well.As a result, the business handover support system eliminates delays in operations caused by insufficient handover, allowing employees to proceed with their tasks quickly and efficiently.

[0029] The business handover support system according to this embodiment comprises a collection unit, an analysis unit, and a generation unit. The collection unit collects past business history, documents, emails, and chat history. For example, the collection unit collects project history, task history, reports, design documents, meeting minutes, business-related emails, customer interactions, internal chats, customer support chats, etc. The collection unit can automatically collect this information using a generation AI. For example, the collection unit can input prompts to the generation AI and instruct it to collect all documents related to a specific project. The analysis unit analyzes the information collected by the collection unit and automatically organizes and tags the necessary information to build a knowledge base. For example, the analysis unit analyzes the information using techniques such as text mining, data mining, categorization, and keyword assignment. The analysis unit can perform these analyses automatically using a generation AI. For example, the analysis unit can input prompts to the generation AI and instruct it to organize the collected documents by category and assign keywords. The generation unit automatically generates manuals and guidelines required by the successor based on the knowledge base built by the analysis unit. The generation unit generates, for example, operation manuals, procedure manuals, guidelines, etc. The generation unit can automatically generate these manuals and guidelines using a generation AI. For example, the generation unit can input prompts to the generation AI and instruct it to generate a procedure manual for a specific task. As a result, the business handover support system according to the embodiment eliminates business delays caused by insufficient handover and allows the person in charge to proceed with their work quickly and efficiently. Some or all of the above-described processes in the collection unit, analysis unit, and generation unit may be performed using the generation AI or not. For example, the collection unit can collect project history using the generation AI, the analysis unit can analyze the collected information using the generation AI, and the generation unit can generate manuals using the generation AI.

[0030] The data collection unit collects past work history, documents, emails, and chat histories. Specifically, it collects project history, task history, reports, design documents, meeting minutes, business-related emails, customer interactions, internal chats, and customer support chats. Because this information is distributed across various systems and platforms within the company, the data collection unit accesses these sources and efficiently collects the necessary data. For example, it retrieves project history and task history from project management tools and collects reports and design documents from document management systems. It also retrieves business-related emails from mail servers and collects internal chat and customer support chat histories from chat applications. The data collection unit can automatically collect this information using generative AI. The generative AI utilizes natural language processing technology to extract and integrate necessary information from various data sources. For example, a prompt can be entered into the generative AI to instruct it to collect all documents related to a specific project. Based on the prompt, the generative AI searches for relevant keywords and phrases and automatically collects the corresponding documents. This allows the data collection unit to significantly reduce manual data collection work and efficiently aggregate information. Furthermore, the data collection unit centrally manages the collected data, making it easily accessible to subsequent analysis and generation units. This allows the data collection unit to efficiently collect the data that forms the basis of the business handover support system, thereby improving the overall system performance.

[0031] The analysis unit analyzes the information collected by the collection unit and automatically organizes and tags the necessary information to build a knowledge base. Specifically, it analyzes information using techniques such as text mining, data mining, categorization, and keyword assignment. Text mining techniques are used to extract important information from collected documents, emails, and chat histories, and data mining techniques are used to discover patterns and trends. Categorization techniques are used to organize the collected information by project or task, and keyword assignment techniques are used to assign keywords related to each document. The analysis unit can perform these analyses automatically using generative AI. Generative AI utilizes natural language processing techniques to analyze the collected information and automatically organize and tag the necessary information. For example, the analysis unit can input prompts to the generative AI and instruct it to organize the collected documents by category and assign keywords. Based on the prompts, the generative AI analyzes the documents and automatically assigns the relevant categories and keywords. This allows the analysis unit to significantly reduce manual information organization work and efficiently build a knowledge base. Furthermore, the analysis unit can continuously update the constructed knowledge base and add newly collected information, ensuring that the system is always up-to-date. This allows the analysis unit to efficiently build the core knowledge base of the business handover support system and improve the overall system performance.

[0032] The generation unit automatically generates manuals and guidelines needed by successors based on the knowledge base built by the analysis unit. Specifically, it generates operation manuals, procedure manuals, and guidelines. The generation unit can automatically generate these manuals and guidelines using generation AI. The generation AI utilizes natural language generation technology to automatically create documents based on the knowledge base. For example, the generation unit can input prompts to the generation AI and instruct it to generate procedure manuals for a specific task. Based on the prompts, the generation AI searches the knowledge base, extracts relevant information, and automatically generates the procedure manuals. The generated procedure manuals include specific steps and points to note to enable successors to carry out their work quickly and efficiently. Furthermore, the generation unit can continuously update the generated manuals and guidelines, reflecting new information and changes to always provide the latest information. In addition, the generation unit can customize the generated documents to meet user needs. For example, by generating manuals specialized for specific projects or tasks, it can accurately provide the information needed by successors. This allows the generation unit to efficiently generate manuals and guidelines that form the core of the business handover support system, improving the overall system performance.

[0033] The analysis unit can search for appropriate documents and past communications and provide relevant information when a successor enters a question. For example, if a successor enters "I want to know the procedure for a specific task that my predecessor performed," the analysis unit will search for relevant documents and emails and provide the procedure. The analysis unit can automate these searches and information provision using generative AI. For example, the analysis unit can input prompts to the generative AI and instruct it to search for documents related to a specific task and provide the procedure. This allows the successor to quickly obtain the necessary information. Some or all of the above processing in the analysis unit may be performed using generative AI or not. For example, the analysis unit can use generative AI to search for appropriate documents in response to a successor's question and provide relevant information.

[0034] The generation unit can analyze the work history and documents of the predecessor and automatically generate manuals and guidelines required by the successor. For example, the generation unit can analyze the procedure for creating periodic reports performed by the predecessor and generate a manual based on that procedure. The generation unit can use generation AI to automate these analyses and manual generation processes. For example, the generation unit can input prompts to the generation AI and instruct it to analyze the procedure for creating a specific report and generate a manual based on that procedure. This allows the successor to proceed with their work quickly. Some or all of the above-described processes in the generation unit may be performed using generation AI or not. For example, the generation unit can use generation AI to analyze the work history of the predecessor and generate manuals required by the successor.

[0035] The data collection unit can evaluate the reliability of information during collection and prioritize the collection of highly reliable information. For example, the data collection unit can verify the source of information and prioritize the collection of information from reliable sources. The data collection unit can use generative AI to automatically perform these reliability evaluations and information collections. For example, the data collection unit can input prompts to the generative AI and instruct it to evaluate the reliability of information and prioritize the collection of highly reliable information. This enables the provision of highly reliable information. Some or all of the above-described processes in the data collection unit may be performed using generative AI or not. For example, the data collection unit can use generative AI to evaluate the reliability of information and prioritize the collection of highly reliable information.

[0036] The data collection unit can prioritize collecting the latest information by considering the frequency of information updates during collection. For example, the data collection unit can check the update date and time of the information and prioritize collecting the latest information. The data collection unit can use a generation AI to automatically perform these update frequency evaluations and information collections. For example, the data collection unit can input prompts to the generation AI, instructing it to evaluate the information update frequency and prioritize collecting the latest information. This allows the system to provide the latest information. Some or all of the above-described processes in the data collection unit may be performed using a generation AI or not. For example, the data collection unit can use a generation AI to evaluate the information update frequency and prioritize collecting the latest information.

[0037] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information during the collection process. For example, the data collection unit can prioritize the collection of highly relevant information based on the user's current location. The data collection unit can automatically evaluate this geographical location information and collect the information using a generative AI. For example, the data collection unit can input prompts to the generative AI, instructing it to evaluate the user's geographical location information and prioritize the collection of highly relevant information. This allows the data collection unit to provide the user with highly relevant information. Some or all of the above-described processes in the data collection unit may be performed using a generative AI or not. For example, the data collection unit can use a generative AI to evaluate the user's geographical location information and prioritize the collection of highly relevant information.

[0038] The data collection unit can analyze the user's social media activity and collect relevant information during the collection process. For example, the data collection unit can analyze the user's social media posts and collect relevant information. The data collection unit can use generative AI to automatically perform this analysis of social media activity and information collection. For example, the data collection unit can input prompts to the generative AI and instruct it to analyze the user's social media activity and collect relevant information. This allows the data collection unit to provide information that is highly relevant to the user. Some or all of the above-described processes in the data collection unit may be performed using generative AI or not. For example, the data collection unit can use generative AI to analyze the user's social media activity and collect relevant information.

[0039] 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 can perform a detailed analysis on important information and a simplified analysis on less important information. The analysis unit can use a generation AI to automatically perform these importance assessments and analysis adjustments. For example, the analysis unit can input prompts to the generation AI, instructing it to assess the importance of the information and perform a detailed analysis on important information. This allows for a detailed analysis of important information. Some or all of the above-described processes in the analysis unit may be performed using the generation AI or not. For example, the analysis unit can use the generation AI to assess the importance of the information and adjust the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a text analysis algorithm to document information and a natural language processing algorithm to email information. The analysis unit can use generative AI to automatically perform these category evaluations and algorithm applications. For example, the analysis unit can input prompts to the generative AI, instructing it to evaluate the category of information and apply an appropriate analysis algorithm. This enables analysis suitable for each category. Some or all of the above-described processes in the analysis unit may be performed using generative AI or not. For example, the analysis unit can use generative AI to evaluate the category of information and apply different analysis algorithms.

[0041] The analysis unit can determine the priority of analysis based on the submission timing of the information during the analysis. For example, the analysis unit may prioritize the analysis of the latest information and postpone the analysis of older information. The analysis unit can use a generation AI to automatically perform this submission timing evaluation and analysis priority determination. For example, the analysis unit can input prompts to the generation AI, instructing it to evaluate the submission timing of the information and prioritize the analysis of the latest information. This allows for the priority analysis of information that has been submitted recently. Some or all of the above-described processes in the analysis unit may be performed using a generation AI or not. For example, the analysis unit can use a generation AI to evaluate the submission timing of the information and determine the priority of analysis.

[0042] 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 and postpone the analysis of less relevant information. The analysis unit can automatically perform these relevance evaluations and analysis order adjustments using a generation AI. For example, the analysis unit can input prompts to the generation AI, instructing it to evaluate the relevance of the information and prioritize the analysis of highly relevant information. This allows for the priority analysis of highly relevant information. Some or all of the above-described processes in the analysis unit may be performed using a generation AI or not. For example, the analysis unit can use a generation AI to evaluate the relevance of information and adjust the order of analysis.

[0043] The generation unit can adjust the level of detail in the manuals and guidelines it generates based on the importance of the information during generation. For example, it can generate detailed manuals for important information and simplified manuals for less important information. The generation unit can use a generation AI to automatically perform these importance assessments and detail adjustments. For example, the generation unit can input prompts to the generation AI, instructing it to assess the importance of the information and generate detailed manuals for important information. This allows for the provision of detailed manuals and guidelines for important information. Some or all of the above-described processes in the generation unit may be performed using the generation AI or not. For example, the generation unit can use the generation AI to assess the importance of the information and adjust the level of detail in the manuals and guidelines it generates.

[0044] The generation unit can apply different generation algorithms depending on the category of information during generation. For example, it might apply a text generation algorithm to document information and a natural language generation algorithm to email information. The generation unit can use a generation AI to automatically perform these category evaluations and generation algorithm applications. For example, the generation unit can input prompts to the generation AI, instructing it to evaluate the information category and apply the appropriate generation algorithm. This allows for the provision of manuals and guidelines suitable for each category. Some or all of the above-described processes in the generation unit may be performed using the generation AI or not. For example, the generation unit can use the generation AI to evaluate the information category and apply different generation algorithms.

[0045] The generation unit can determine the priority of manuals and guidelines to be generated based on the submission timing of the information during generation. For example, the generation unit can prioritize including information with an upcoming submission date in the manual. The generation unit can use a generation AI to automatically perform this submission timing evaluation and prioritization. For example, the generation unit can input prompts to the generation AI, instructing it to evaluate the submission timing of the information and prioritize including information with an upcoming submission date in the manual. This ensures that information with an upcoming submission date is included preferentially in the manuals and guidelines. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can use the generation AI to evaluate the submission timing of the information and determine the priority of manuals and guidelines to be generated.

[0046] The generation unit can adjust the order of the manuals and guidelines it generates based on the relevance of the information during generation. For example, the generation unit prioritizes including highly relevant information in the manuals. The generation unit can use a generation AI to automatically perform this relevance evaluation and ordering adjustment. For example, the generation unit can input prompts to the generation AI, instructing it to evaluate the relevance of the information and prioritize including highly relevant information in the manuals. This ensures that highly relevant information is included preferentially in the manuals and guidelines. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can use the generation AI to evaluate the relevance of the information and adjust the order of the manuals and guidelines it generates.

[0047] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0048] The business handover support system can also include a "feedback collection unit." The feedback collection unit collects feedback on manuals and guidelines used by the successor and provides it to the analysis unit. For example, if the successor finds the content of a manual "difficult to understand," this feedback can be collected and used by the analysis unit as data to improve the content. The feedback collection unit can also provide suggestions to the generation unit to streamline a procedure if the successor finds it "time-consuming." In this way, the business handover support system can be continuously improved based on the actual usage by the successor.

[0049] The job handover support system can also include a "Learning Support Department." This department provides individualized learning plans for successors as they learn new tasks. For example, if a successor needs to acquire a specific skill, the Learning Support Department provides relevant documentation and training materials. The Learning Support Department can also monitor the successor's progress and provide additional support as needed. This allows successors to efficiently acquire new skills.

[0050] The business handover support system can also be equipped with an "automatic notification unit." The automatic notification unit automatically notifies the successor of necessary information and task deadlines. For example, to prevent the successor from forgetting the deadline for a particular task, the automatic notification unit sends a reminder when the deadline for that task is approaching. The automatic notification unit can also notify the successor of updates to important documents in real time. This allows the successor to proceed with their work efficiently without missing important information.

[0051] The business handover support system can also include a "data integration unit." This unit integrates and centrally manages data collected from different systems and platforms. For example, if a successor needs to collect information from multiple systems, the data integration unit centrally manages that information, making it easily accessible to the successor. The data integration unit can also unify data in different formats, enabling the analysis unit to analyze it efficiently. This allows the successor to quickly obtain the necessary information and proceed with their work efficiently.

[0052] The business handover support system can also be equipped with a "predictive analytics department." This department predicts future business trends and risks based on historical data and provides this information to the successor. For example, if a particular project is likely to be delayed, the predictive analytics department will notify the successor of this risk and propose countermeasures. The predictive analytics department can also suggest improvements to streamline operations. This allows the successor to understand future risks in advance and take appropriate measures.

[0053] The following briefly describes the processing flow for example form 1.

[0054] Step 1: The collection unit collects past work history, documents, emails, and chat history. For example, it collects project history, task history, reports, design documents, meeting minutes, work-related emails, customer interactions, internal chats, and customer support chats. The collection unit can automatically collect this information using generative AI. For example, the collection unit can input prompts into the generative AI and instruct it to collect all documents related to a specific project. Step 2: The analysis unit analyzes the information collected by the collection unit and automatically organizes and tags the necessary information to build a knowledge base. For example, it analyzes the information using techniques such as text mining, data mining, categorization, and keyword assignment. The analysis unit can perform these analyses automatically using generative AI. For example, the analysis unit can input prompts to the generative AI and instruct it to organize the collected documents by category and assign keywords. Step 3: The generation unit automatically generates manuals and guidelines needed by successors based on the knowledge base built by the analysis unit. For example, it generates operation manuals, procedure manuals, and guidelines. The generation unit can automatically generate these manuals and guidelines using generation AI. For example, the generation unit can input prompts to the generation AI and instruct it to generate procedure manuals for a specific task.

[0055] (Example of form 2) The business handover support system according to an embodiment of the present invention provides two mechanisms for resolving insufficient business handover using generating AI. This business handover support system combines an "AI Knowledge Assistant" that analyzes past business history, documents, emails, and chat history, and automatically organizes and tags necessary information to build a knowledge base, with an "AI Document Generator" that analyzes the predecessor's business history and documents and automatically generates manuals and guidelines needed by the successor. This eliminates business delays caused by insufficient handover, enabling the person in charge to proceed with their work quickly and efficiently. For example, the AI ​​Knowledge Assistant analyzes past project data, documents, emails, and chat history, and automatically organizes and tags necessary information to build a knowledge base. When the successor inputs a question, the AI ​​searches for appropriate documents and past interactions and provides relevant information. For example, if the successor inputs, "I want to know the procedure for a specific task that my predecessor performed," the AI ​​searches for relevant documents and emails and provides the procedure. Next, the AI ​​Document Generator analyzes the predecessor's business history and documents and automatically generates manuals and guidelines needed by the successor. This allows successors to quickly access necessary information and carry out their work smoothly. For example, the system analyzes the procedures for creating regular reports performed by the predecessor and generates a manual based on those procedures. This system yields the following benefits: First, it improves work efficiency. The person in charge can quickly grasp the work content and schedule of the predecessor and proceed with their work smoothly. Second, it is expected to reduce errors. Important tasks and email sending timings will not be missed, reducing work errors. Finally, it ensures continuity of work. Even if the person in charge changes, a system can be established that allows work to continue without interruption. In this way, by combining two systems that utilize generation AI, delays in work due to insufficient handover are eliminated, and the person in charge can proceed with their work quickly and efficiently. Furthermore, synergistic effects can be obtained by introducing it to business partners as well.As a result, the business handover support system eliminates delays in operations caused by insufficient handover, allowing employees to proceed with their tasks quickly and efficiently.

[0056] The business handover support system according to this embodiment comprises a collection unit, an analysis unit, and a generation unit. The collection unit collects past business history, documents, emails, and chat history. For example, the collection unit collects project history, task history, reports, design documents, meeting minutes, business-related emails, customer interactions, internal chats, customer support chats, etc. The collection unit can automatically collect this information using a generation AI. For example, the collection unit can input prompts to the generation AI and instruct it to collect all documents related to a specific project. The analysis unit analyzes the information collected by the collection unit and automatically organizes and tags the necessary information to build a knowledge base. For example, the analysis unit analyzes the information using techniques such as text mining, data mining, categorization, and keyword assignment. The analysis unit can perform these analyses automatically using a generation AI. For example, the analysis unit can input prompts to the generation AI and instruct it to organize the collected documents by category and assign keywords. The generation unit automatically generates manuals and guidelines required by the successor based on the knowledge base built by the analysis unit. The generation unit generates, for example, operation manuals, procedure manuals, guidelines, etc. The generation unit can automatically generate these manuals and guidelines using a generation AI. For example, the generation unit can input prompts to the generation AI and instruct it to generate a procedure manual for a specific task. As a result, the business handover support system according to the embodiment eliminates business delays caused by insufficient handover and allows the person in charge to proceed with their work quickly and efficiently. Some or all of the above-described processes in the collection unit, analysis unit, and generation unit may be performed using the generation AI or not. For example, the collection unit can collect project history using the generation AI, the analysis unit can analyze the collected information using the generation AI, and the generation unit can generate manuals using the generation AI.

[0057] The data collection unit collects past work history, documents, emails, and chat histories. Specifically, it collects project history, task history, reports, design documents, meeting minutes, business-related emails, customer interactions, internal chats, and customer support chats. Because this information is distributed across various systems and platforms within the company, the data collection unit accesses these sources and efficiently collects the necessary data. For example, it retrieves project history and task history from project management tools and collects reports and design documents from document management systems. It also retrieves business-related emails from mail servers and collects internal chat and customer support chat histories from chat applications. The data collection unit can automatically collect this information using generative AI. The generative AI utilizes natural language processing technology to extract and integrate necessary information from various data sources. For example, a prompt can be entered into the generative AI to instruct it to collect all documents related to a specific project. Based on the prompt, the generative AI searches for relevant keywords and phrases and automatically collects the corresponding documents. This allows the data collection unit to significantly reduce manual data collection work and efficiently aggregate information. Furthermore, the data collection unit centrally manages the collected data, making it easily accessible to subsequent analysis and generation units. This allows the data collection unit to efficiently collect the data that forms the basis of the business handover support system, thereby improving the overall system performance.

[0058] The analysis unit analyzes the information collected by the collection unit and automatically organizes and tags the necessary information to build a knowledge base. Specifically, it analyzes information using techniques such as text mining, data mining, categorization, and keyword assignment. Text mining techniques are used to extract important information from collected documents, emails, and chat histories, and data mining techniques are used to discover patterns and trends. Categorization techniques are used to organize the collected information by project or task, and keyword assignment techniques are used to assign keywords related to each document. The analysis unit can perform these analyses automatically using generative AI. Generative AI utilizes natural language processing techniques to analyze the collected information and automatically organize and tag the necessary information. For example, the analysis unit can input prompts to the generative AI and instruct it to organize the collected documents by category and assign keywords. Based on the prompts, the generative AI analyzes the documents and automatically assigns the relevant categories and keywords. This allows the analysis unit to significantly reduce manual information organization work and efficiently build a knowledge base. Furthermore, the analysis unit can continuously update the constructed knowledge base and add newly collected information, ensuring that the system is always up-to-date. This allows the analysis unit to efficiently build the core knowledge base of the business handover support system and improve the overall system performance.

[0059] The generation unit automatically generates manuals and guidelines needed by successors based on the knowledge base built by the analysis unit. Specifically, it generates operation manuals, procedure manuals, and guidelines. The generation unit can automatically generate these manuals and guidelines using generation AI. The generation AI utilizes natural language generation technology to automatically create documents based on the knowledge base. For example, the generation unit can input prompts to the generation AI and instruct it to generate procedure manuals for a specific task. Based on the prompts, the generation AI searches the knowledge base, extracts relevant information, and automatically generates the procedure manuals. The generated procedure manuals include specific steps and points to note to enable successors to carry out their work quickly and efficiently. Furthermore, the generation unit can continuously update the generated manuals and guidelines, reflecting new information and changes to always provide the latest information. In addition, the generation unit can customize the generated documents to meet user needs. For example, by generating manuals specialized for specific projects or tasks, it can accurately provide the information needed by successors. This allows the generation unit to efficiently generate manuals and guidelines that form the core of the business handover support system, improving the overall system performance.

[0060] The analysis unit can search for appropriate documents and past communications and provide relevant information when a successor enters a question. For example, if a successor enters "I want to know the procedure for a specific task that my predecessor performed," the analysis unit will search for relevant documents and emails and provide the procedure. The analysis unit can automate these searches and information provision using generative AI. For example, the analysis unit can input prompts to the generative AI and instruct it to search for documents related to a specific task and provide the procedure. This allows the successor to quickly obtain the necessary information. Some or all of the above processing in the analysis unit may be performed using generative AI or not. For example, the analysis unit can use generative AI to search for appropriate documents in response to a successor's question and provide relevant information.

[0061] The generation unit can analyze the work history and documents of the predecessor and automatically generate manuals and guidelines required by the successor. For example, the generation unit can analyze the procedure for creating periodic reports performed by the predecessor and generate a manual based on that procedure. The generation unit can use generation AI to automate these analyses and manual generation processes. For example, the generation unit can input prompts to the generation AI and instruct it to analyze the procedure for creating a specific report and generate a manual based on that procedure. This allows the successor to proceed with their work quickly. Some or all of the above-described processes in the generation unit may be performed using generation AI or not. For example, the generation unit can use generation AI to analyze the work history of the predecessor and generate manuals required by the successor.

[0062] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is feeling stressed, the data collection unit will prioritize the collection of important information and provide it quickly. The data collection unit can use generative AI to automate these emotion estimation and information collection processes. For example, the data collection unit can input prompts to the generative AI, instructing it to estimate the user's emotions and prioritize the collection of important information. This allows for the provision of appropriate information tailored to the user's situation. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using or without the generative AI. For example, the data collection unit can use the generative AI to estimate the user's emotions and determine the priority of information to collect.

[0063] The data collection unit can evaluate the reliability of information during collection and prioritize the collection of highly reliable information. For example, the data collection unit can verify the source of information and prioritize the collection of information from reliable sources. The data collection unit can use generative AI to automatically perform these reliability evaluations and information collections. For example, the data collection unit can input prompts to the generative AI and instruct it to evaluate the reliability of information and prioritize the collection of highly reliable information. This enables the provision of highly reliable information. Some or all of the above-described processes in the data collection unit may be performed using generative AI or not. For example, the data collection unit can use generative AI to evaluate the reliability of information and prioritize the collection of highly reliable information.

[0064] The data collection unit can prioritize collecting the latest information by considering the frequency of information updates during collection. For example, the data collection unit can check the update date and time of the information and prioritize collecting the latest information. The data collection unit can use a generation AI to automatically perform these update frequency evaluations and information collections. For example, the data collection unit can input prompts to the generation AI, instructing it to evaluate the information update frequency and prioritize collecting the latest information. This allows the system to provide the latest information. Some or all of the above-described processes in the data collection unit may be performed using a generation AI or not. For example, the data collection unit can use a generation AI to evaluate the information update frequency and prioritize collecting the latest information.

[0065] The data collection unit can estimate the user's emotions and adjust the categories of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting information from important categories. The data collection unit can use generative AI to automate these emotion estimations and information collections. For example, the data collection unit can input prompts to the generative AI, instructing it to estimate the user's emotions and prioritize collecting information from important categories. This allows for the provision of appropriate information tailored to the user's situation. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using or without the generative AI. For example, the data collection unit can use the generative AI to estimate the user's emotions and adjust the categories of information to collect.

[0066] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information during the collection process. For example, the data collection unit can prioritize the collection of highly relevant information based on the user's current location. The data collection unit can automatically evaluate this geographical location information and collect the information using a generative AI. For example, the data collection unit can input prompts to the generative AI, instructing it to evaluate the user's geographical location information and prioritize the collection of highly relevant information. This allows the data collection unit to provide the user with highly relevant information. Some or all of the above-described processes in the data collection unit may be performed using a generative AI or not. For example, the data collection unit can use a generative AI to evaluate the user's geographical location information and prioritize the collection of highly relevant information.

[0067] The data collection unit can analyze the user's social media activity and collect relevant information during the collection process. For example, the data collection unit can analyze the user's social media posts and collect relevant information. The data collection unit can use generative AI to automatically perform this analysis of social media activity and information collection. For example, the data collection unit can input prompts to the generative AI and instruct it to analyze the user's social media activity and collect relevant information. This allows the data collection unit to provide information that is highly relevant to the user. Some or all of the above-described processes in the data collection unit may be performed using generative AI or not. For example, the data collection unit can use generative AI to analyze the user's social media activity and collect relevant information.

[0068] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit performs the analysis using a simple algorithm. The analysis unit can use generative AI to automatically perform these emotion estimations and algorithm adjustments. For example, the analysis unit can input prompts to the generative AI, instructing it to estimate the user's emotions and perform the analysis using a simple algorithm. This allows for appropriate analysis tailored to the user's situation. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using or without the generative AI. For example, the analysis unit can use the generative AI to estimate the user's emotions and adjust the analysis algorithm.

[0069] 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 can perform a detailed analysis on important information and a simplified analysis on less important information. The analysis unit can use a generation AI to automatically perform these importance assessments and analysis adjustments. For example, the analysis unit can input prompts to the generation AI, instructing it to assess the importance of the information and perform a detailed analysis on important information. This allows for a detailed analysis of important information. Some or all of the above-described processes in the analysis unit may be performed using the generation AI or not. For example, the analysis unit can use the generation AI to assess the importance of the information and adjust the level of detail of the analysis.

[0070] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a text analysis algorithm to document information and a natural language processing algorithm to email information. The analysis unit can use generative AI to automatically perform these category evaluations and algorithm applications. For example, the analysis unit can input prompts to the generative AI, instructing it to evaluate the category of information and apply an appropriate analysis algorithm. This enables analysis suitable for each category. Some or all of the above-described processes in the analysis unit may be performed using generative AI or not. For example, the analysis unit can use generative AI to evaluate the category of information and apply different analysis algorithms.

[0071] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit provides a simple and highly visible display method. The analysis unit can automatically perform these emotion estimations and display method adjustments using a generative AI. For example, the analysis unit can input prompts to the generative AI and instruct it to estimate the user's emotions and provide a simple and highly visible display method. This allows for the provision of an appropriate display method according to the user's situation. Emotion estimation is achieved using an emotion estimation function, for example, using 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-described processes in the analysis unit may be performed using a generative AI or not. For example, the analysis unit can use a generative AI to estimate the user's emotions and adjust the display method of the analysis results.

[0072] The analysis unit can determine the priority of analysis based on the submission timing of the information during the analysis. For example, the analysis unit may prioritize the analysis of the latest information and postpone the analysis of older information. The analysis unit can use a generation AI to automatically perform this submission timing evaluation and analysis priority determination. For example, the analysis unit can input prompts to the generation AI, instructing it to evaluate the submission timing of the information and prioritize the analysis of the latest information. This allows for the priority analysis of information that has been submitted recently. Some or all of the above-described processes in the analysis unit may be performed using a generation AI or not. For example, the analysis unit can use a generation AI to evaluate the submission timing of the information and determine the priority of analysis.

[0073] 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 and postpone the analysis of less relevant information. The analysis unit can automatically perform these relevance evaluations and analysis order adjustments using a generation AI. For example, the analysis unit can input prompts to the generation AI, instructing it to evaluate the relevance of the information and prioritize the analysis of highly relevant information. This allows for the priority analysis of highly relevant information. Some or all of the above-described processes in the analysis unit may be performed using a generation AI or not. For example, the analysis unit can use a generation AI to evaluate the relevance of information and adjust the order of analysis.

[0074] The generation unit can estimate the user's emotions and adjust the expression of the manuals and guidelines it generates based on the estimated emotions. For example, if the user is stressed, the generation unit will generate a simple and highly visual manual. The generation unit can use a generation AI to automatically perform these emotion estimations and expression adjustments. For example, the generation unit can input prompts to the generation AI, instructing it to estimate the user's emotions and generate a simple and highly visual manual. This allows for the provision of appropriate expression methods tailored to the user's situation. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation 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-described processes in the generation unit may be performed using a generation AI or not. For example, the generation unit can use a generation AI to estimate the user's emotions and adjust the expression of the manuals and guidelines it generates.

[0075] The generation unit can adjust the level of detail in the manuals and guidelines it generates based on the importance of the information during generation. For example, it can generate detailed manuals for important information and simplified manuals for less important information. The generation unit can use a generation AI to automatically perform these importance assessments and detail adjustments. For example, the generation unit can input prompts to the generation AI, instructing it to assess the importance of the information and generate detailed manuals for important information. This allows for the provision of detailed manuals and guidelines for important information. Some or all of the above-described processes in the generation unit may be performed using the generation AI or not. For example, the generation unit can use the generation AI to assess the importance of the information and adjust the level of detail in the manuals and guidelines it generates.

[0076] The generation unit can apply different generation algorithms depending on the category of information during generation. For example, it might apply a text generation algorithm to document information and a natural language generation algorithm to email information. The generation unit can use a generation AI to automatically perform these category evaluations and generation algorithm applications. For example, the generation unit can input prompts to the generation AI, instructing it to evaluate the information category and apply the appropriate generation algorithm. This allows for the provision of manuals and guidelines suitable for each category. Some or all of the above-described processes in the generation unit may be performed using the generation AI or not. For example, the generation unit can use the generation AI to evaluate the information category and apply different generation algorithms.

[0077] The generation unit can estimate the user's emotions and adjust the length of the manuals and guidelines it generates based on those estimated emotions. For example, if the user is stressed, the generation unit will generate a short, concise manual. The generation unit can use a generation AI to automatically perform these emotion estimations and length adjustments. For example, the generation unit can input prompts to the generation AI, instructing it to estimate the user's emotions and generate a short, concise manual. This allows for the provision of manuals and guidelines of appropriate length according to the user's situation. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation 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-described processes in the generation unit may be performed using a generation AI or not. For example, the generation unit can use a generation AI to estimate the user's emotions and adjust the length of the manuals and guidelines it generates.

[0078] The generation unit can determine the priority of manuals and guidelines to be generated based on the submission timing of the information during generation. For example, the generation unit can prioritize including information with an upcoming submission date in the manual. The generation unit can use a generation AI to automatically perform this submission timing evaluation and prioritization. For example, the generation unit can input prompts to the generation AI, instructing it to evaluate the submission timing of the information and prioritize including information with an upcoming submission date in the manual. This ensures that information with an upcoming submission date is included preferentially in the manuals and guidelines. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can use the generation AI to evaluate the submission timing of the information and determine the priority of manuals and guidelines to be generated.

[0079] The generation unit can adjust the order of the manuals and guidelines it generates based on the relevance of the information during generation. For example, the generation unit prioritizes including highly relevant information in the manuals. The generation unit can use a generation AI to automatically perform this relevance evaluation and ordering adjustment. For example, the generation unit can input prompts to the generation AI, instructing it to evaluate the relevance of the information and prioritize including highly relevant information in the manuals. This ensures that highly relevant information is included preferentially in the manuals and guidelines. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can use the generation AI to evaluate the relevance of the information and adjust the order of the manuals and guidelines it generates.

[0080] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0081] The business handover support system can also include a "feedback collection unit." The feedback collection unit collects feedback on manuals and guidelines used by the successor and provides it to the analysis unit. For example, if the successor finds the content of a manual "difficult to understand," this feedback can be collected and used by the analysis unit as data to improve the content. The feedback collection unit can also provide suggestions to the generation unit to streamline a procedure if the successor finds it "time-consuming." In this way, the business handover support system can be continuously improved based on the actual usage by the successor.

[0082] The business handover support system can also include an "emotional analysis unit." This unit analyzes the emotions of the successor as they use manuals and guidelines in real time and provides this information to the analysis unit. For example, if the successor is experiencing stress, the emotional analysis unit provides this information to the analysis unit, which can then provide simplified procedures to reduce stress. Furthermore, if the successor is satisfied, the emotional analysis unit can use this data to recommend the procedures to other successors. This allows the business handover support system to respond flexibly based on the successor's emotions.

[0083] The job handover support system can also include a "Learning Support Department." This department provides individualized learning plans for successors as they learn new tasks. For example, if a successor needs to acquire a specific skill, the Learning Support Department provides relevant documentation and training materials. The Learning Support Department can also monitor the successor's progress and provide additional support as needed. This allows successors to efficiently acquire new skills.

[0084] The business handover support system can also be equipped with an "emotional feedback unit." The emotional feedback unit periodically collects the emotions of the successor as they carry out their duties and provides this information to the analysis unit. For example, if the successor feels anxious about their duties, the emotional feedback unit provides this information to the analysis unit, which can then provide additional support to alleviate the anxiety. Furthermore, if the successor is satisfied with their duties, the emotional feedback unit can use this data to recommend their procedures to other successors. This allows the business handover support system to respond flexibly based on the emotions of the successor.

[0085] The business handover support system can also be equipped with an "automatic notification unit." The automatic notification unit automatically notifies the successor of necessary information and task deadlines. For example, to prevent the successor from forgetting the deadline for a particular task, the automatic notification unit sends a reminder when the deadline for that task is approaching. The automatic notification unit can also notify the successor of updates to important documents in real time. This allows the successor to proceed with their work efficiently without missing important information.

[0086] The business handover support system can also be equipped with an "emotional monitoring unit." The emotional monitoring unit monitors the emotions of the successor as they perform their duties in real time and provides this information to the analysis unit. For example, if the successor is feeling stressed about their duties, the emotional monitoring unit provides this information to the analysis unit, which can then provide simplified procedures to reduce stress. Furthermore, if the successor is satisfied with their duties, the emotional monitoring unit can use this data to recommend those procedures to other successors. This allows the business handover support system to respond flexibly based on the emotions of the successor.

[0087] The business handover support system can also include a "data integration unit." This unit integrates and centrally manages data collected from different systems and platforms. For example, if a successor needs to collect information from multiple systems, the data integration unit centrally manages that information, making it easily accessible to the successor. The data integration unit can also unify data in different formats, enabling the analysis unit to analyze it efficiently. This allows the successor to quickly obtain the necessary information and proceed with their work efficiently.

[0088] The business handover support system can also be equipped with an "emotional adaptation unit." This unit adapts the system's interface and functions based on the successor's emotions. For example, if the successor is stressed, the emotional adaptation unit simplifies the system interface and prioritizes displaying important information. Conversely, if the successor is relaxed, the emotional adaptation unit can provide detailed information to allow for deeper understanding. This allows the business handover support system to respond flexibly to the successor's emotions.

[0089] The business handover support system can also be equipped with a "predictive analytics department." This department predicts future business trends and risks based on historical data and provides this information to the successor. For example, if a particular project is likely to be delayed, the predictive analytics department will notify the successor of this risk and propose countermeasures. The predictive analytics department can also suggest improvements to streamline operations. This allows the successor to understand future risks in advance and take appropriate measures.

[0090] The business handover support system can also be equipped with an "emotion prediction unit." The emotion prediction unit predicts the successor's future emotions based on their past emotional data and provides this information to the analysis unit. For example, if the successor is likely to feel stressed about a particular task, the emotion prediction unit provides this information to the analysis unit, which can then provide simplified procedures to reduce stress. Furthermore, if the emotion prediction unit predicts that the successor is likely to be satisfied with a particular task, this data can be used to recommend those procedures to other successors. This allows the business handover support system to respond flexibly based on the successor's emotions.

[0091] The following briefly describes the processing flow for example form 2.

[0092] Step 1: The collection unit collects past work history, documents, emails, and chat history. For example, it collects project history, task history, reports, design documents, meeting minutes, work-related emails, customer interactions, internal chats, and customer support chats. The collection unit can automatically collect this information using generative AI. For example, the collection unit can input prompts into the generative AI and instruct it to collect all documents related to a specific project. Step 2: The analysis unit analyzes the information collected by the collection unit and automatically organizes and tags the necessary information to build a knowledge base. For example, it analyzes the information using techniques such as text mining, data mining, categorization, and keyword assignment. The analysis unit can perform these analyses automatically using generative AI. For example, the analysis unit can input prompts to the generative AI and instruct it to organize the collected documents by category and assign keywords. Step 3: The generation unit automatically generates manuals and guidelines needed by successors based on the knowledge base built by the analysis unit. For example, it generates operation manuals, procedure manuals, and guidelines. The generation unit can automatically generate these manuals and guidelines using generation AI. For example, the generation unit can input prompts to the generation AI and instruct it to generate procedure manuals for a specific task.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] Each of the multiple elements described above, including the collection unit, analysis unit, and generation unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects past work history, documents, emails, and chat history. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to build a knowledge base. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and automatically generates manuals and guidelines based on the knowledge base. 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.

[0097] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the collection unit, analysis unit, and generation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects past work history, documents, emails, and chat history. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information to build a knowledge base. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates manuals and guidelines based on the knowledge base. 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.

[0113] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0114] 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.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The 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.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 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.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the 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.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 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.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, and generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects past work history, documents, emails, and chat history. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information to build a knowledge base. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates manuals and guidelines based on the knowledge base. 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.

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

[0130] 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.

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

[0132] The 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.

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

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0143] 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.

[0144] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0145] Each of the multiple elements described above, including the collection unit, analysis unit, and generation unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects past work history, documents, emails, and chat history. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information to build a knowledge base. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates manuals and guidelines based on the knowledge base. 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] (Note 1) The collection department collects past work history, documents, emails, and chat history. An analysis unit analyzes the information collected by the aforementioned collection unit, automatically organizes and tags the necessary information, and constructs a knowledge base. The system includes a generation unit that automatically generates manuals and guidelines required by a successor based on the knowledge base constructed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, When a successor enters a question, the system searches for appropriate documents and past communications and provides relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is It analyzes the work history and documents of the previous person in charge and automatically generates the manuals and guidelines that the successor will need. The system described in Appendix 1, characterized by the features described herein. (Note 4) 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 5) The aforementioned collection unit is During data collection, the reliability of the information is evaluated, and reliable information is prioritized for collection. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting data, prioritize the collection of the latest information, taking into account the frequency of information updates. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the categories of information collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, the user's social media activity is analyzed to gather relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) 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 12) 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 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) 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 15) 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 16) The generating unit is We estimate user emotions and adjust the way manuals and guidelines are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, adjust the level of detail in the generated manuals and guidelines based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, different generation algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates user sentiment and adjusts the length of manuals and guidelines generated based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the priority of manuals and guidelines to be generated is determined based on the timing of information submission. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, the order of the generated manuals and guidelines is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects past work history, documents, emails, and chat history. An analysis unit analyzes the information collected by the aforementioned collection unit, automatically organizes and tags the necessary information, and constructs a knowledge base. The system includes a generation unit that automatically generates manuals and guidelines required by a successor based on the knowledge base constructed by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned analysis unit, When a successor enters a question, the system searches for appropriate documents and past communications and provides relevant information. The system according to feature 1.

3. The generating unit is It analyzes the work history and documents of the previous person in charge and automatically generates the manuals and guidelines that the successor will need. The system according to feature 1.

4. 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.

5. The aforementioned collection unit is During data collection, the reliability of the information is evaluated, and reliable information is prioritized for collection. The system according to feature 1.

6. The aforementioned collection unit is When collecting data, prioritize the collection of the latest information, taking into account the frequency of information updates. The system according to feature 1.

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

8. The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant information, taking into account the user's geographical location. The system according to feature 1.

9. The aforementioned collection unit is During data collection, the user's social media activity is analyzed to gather relevant information. The system according to feature 1.

10. The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system according to feature 1.

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