System

Abstract

An object of a system according to an embodiment is to prevent information leakage or misunderstanding in a business takeover and realize a smooth takeover.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection part inputs daily business contents and know-how by each employee. The analysis unit performs analysis based on the information collected by the collection unit. The generation unit generates a handover document based on the information analyzed by the analysis unit. The providing unit provides the handover document generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there is a risk of information leaks and misunderstandings occurring during the handover of work.

[0005] The system according to the embodiment aims to prevent information leaks and misunderstandings during the handover of work, and to achieve a smooth handover. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. Each employee inputs their daily work details and know-how into the collection unit. The analysis unit performs analysis based on the information collected by the collection unit. The generation unit generates a handover document based on the information analyzed by the analysis unit. The provision unit provides the handover document generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can prevent information leaks and misunderstandings during the handover of work, and can achieve a smooth handover. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A handover platform according to an embodiment of the present invention is a system for accumulating business knowledge and know-how and sharing information. Each employee inputs their daily work details and know-how into the handover platform, and a generation AI analyzes the input information and automatically generates a handover document. This handover document includes detailed workflow and explanations of technical terms. For example, when a new project begins, a handover document is automatically generated that outlines the work performed by the predecessor and important points to note, and is provided to the new employee. This prevents information leaks and misunderstandings and ensures a smooth handover. The document also includes explanations of workflow and technical terms, allowing the new employee to quickly understand the work and proceed efficiently. Furthermore, the handover platform centrally manages the knowledge and know-how of the entire organization and quickly provides necessary information. For example, past cases and success stories related to specific tasks can be searched and referenced. This is expected to improve work efficiency and quality. Furthermore, the generation AI responds to changes in work content and the addition of new information, constantly generating the latest handover document. For example, when a new system is introduced or a business process is changed, the generation AI automatically updates the handover document to provide the latest information. This allows the handover platform to efficiently accumulate and share knowledge and know-how related to work, contributing to improved productivity throughout the organization. For example, the work content written by an employee can be quickly and accurately generated into a handover document and provided to the next person in charge. Employees can also learn specific areas for improvement in their work, which is expected to lead to improved work efficiency and quality.

[0029] The handover platform according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit inputs daily work content and know-how from each employee. For example, the collection unit provides an interface through which employees manually input work content. The collection unit may also include a function for automatically collecting work content. For example, the collection unit periodically collects employee work content and stores it in a database. The collection unit also includes a function for setting the collection frequency. For example, the collection unit can collect work content daily, weekly, monthly, etc. The analysis unit performs analysis based on the information collected by the collection unit. For example, the analysis unit analyzes the work content using data mining technology. The analysis unit can also analyze the work content using statistical analysis technology. The analysis unit can also analyze the work content using a machine learning algorithm. For example, the analysis unit analyzes patterns in the work content and extracts important information. The generation unit generates a handover document based on the information analyzed by the analysis unit. For example, the generation unit automatically generates a handover document based on the analyzed information. The generation unit can also generate a handover document that includes details of the business flow and explanations of technical terms. For example, the generation unit generates a handover document that includes detailed explanations for each step of the business flow. Furthermore, the generation unit can also generate a handover document that includes explanations of technical terms. For example, the generation unit generates a handover document that includes definitions and usage examples of technical terms related to the business. The provision unit provides the handover document generated by the generation unit. For example, the provision unit sends the generated handover document to the new employee by email. The provision unit can also store the generated handover document in cloud storage to make it accessible. Furthermore, the provision unit can provide the generated handover document on paper. For example, the provision unit prints the generated handover document and distributes it to the new employee. This enables the handover platform according to the embodiment to efficiently collect and analyze business content and know-how, and generate and provide handover documents.

[0030] The collection unit can analyze each employee's past input history and select the optimal input method. For example, the collection unit can analyze the input methods (voice, text, etc.) used by each employee in the past and suggest the optimal input method. The collection unit can also analyze each employee's input speed and accuracy and select the optimal input method. The collection unit can also suggest the optimal input method for a specific time period based on each employee's input history. This improves input efficiency by providing the optimal input method to each employee. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the employee's past input data into a generation AI and have the generation AI select the optimal input method.

[0031] When inputting work content and know-how, the collection unit can filter the information based on the employee's current project or area of ​​interest. For example, the collection unit prioritizes input of information related to the project the employee is currently working on. The collection unit can also filter and input related work content and know-how based on the employee's area of ​​interest. The collection unit can also filter and input related information by referring to the employee's past project history. This allows related information to be input preferentially based on the employee's area of ​​interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the employee's project data to the generation AI and have the generation AI filter the related information.

[0032] The collection unit can select an appropriate input means according to the employee's input method when inputting work content and know-how. For example, if the employee prefers voice input, the collection unit can provide voice input preferentially. Furthermore, if the employee prefers text input, the collection unit can provide text input preferentially. Furthermore, if the employee prefers image input, the collection unit can provide image input preferentially. This improves input convenience by providing an input means according to the employee's preferences. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input employee input method data into the generation AI and have the generation AI select the optimal input means.

[0033] When inputting work content and know-how, the collection unit can prioritize inputting highly relevant information based on the employee's geographical location information. For example, if an employee works in a specific area, the collection unit prioritizes inputting information related to that area. Furthermore, if an employee is on a business trip, the collection unit can prioritize inputting information related to the business trip destination. Furthermore, if an employee is working remotely, the collection unit can prioritize inputting information related to the remote work. This allows highly relevant information to be prioritized based on the geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the employee's geographical location data to the generation AI and cause the generation AI to select relevant information.

[0034] The collection unit can analyze the employee's social media activity and input related information when inputting work content and know-how. The collection unit can input related work content and know-how based on, for example, information shared by the employee on social media. The collection unit can also analyze the employee's social media activity and input related information. The collection unit can also input related information based on the activity of the employee's friends on social media. In this way, related information can be input based on social media activity. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the employee's social media data into the generation AI and have the generation AI select related information.

[0035] The collection unit can customize the input method by reflecting the employee's past feedback when inputting work content and know-how. The collection unit, for example, suggests the optimal input method based on feedback provided by the employee in the past. The collection unit can also customize the input interface by reflecting the employee's past feedback. The collection unit can also improve the input procedure by referring to the employee's past feedback. In this way, the input method can be customized by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the employee's feedback data into the generation AI and have the generation AI customize the input method.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the business content and know-how. For example, the analysis unit performs a detailed analysis on business content and know-how that are highly important. The analysis unit can also perform a simplified analysis on business content and know-how that are less important. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the business content and know-how. This allows the level of detail of the analysis to be adjusted according to the importance of the business content and know-how. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the business content and know-how to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0037] During analysis, the analysis unit can apply an appropriate analysis algorithm depending on the business content and know-how category. For example, if the business content is technical, the analysis unit can apply a technical analysis algorithm. Furthermore, if the business content is managerial, the analysis unit can also apply a managerial analysis algorithm. Furthermore, if the business content is creative, the analysis unit can also apply a creative analysis algorithm. This makes it possible to apply the optimal analysis algorithm depending on the business content and know-how category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the business content and know-how into the generation AI and cause the generation AI to apply the analysis algorithm.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the employee's past analysis results. For example, the analysis unit can improve accuracy by adjusting the analysis algorithm based on the employee's past analysis results. The analysis unit can also optimize the analysis parameters by referring to the employee's past analysis results. The analysis unit can also analyze the employee's past analysis results and provide feedback to improve the analysis accuracy. This makes it possible to improve the analysis accuracy by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the employee's past analysis data into the generation AI and have the generation AI improve the analysis accuracy.

[0039] During analysis, the analysis unit can determine the priority of analysis based on the business content and the submission time of the know-how. For example, the analysis unit prioritizes the analysis of business content and know-how that are due to be submitted soon. The analysis unit can also postpone the analysis of business content and know-how that are due to be submitted further in the future. The analysis unit can also dynamically adjust the priority of analysis based on the submission time. This makes it possible to adjust the priority of analysis based on the submission time. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission time of the business content and know-how to the generation AI and have the generation AI determine the priority of analysis.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the business content and know-how. For example, the analysis unit prioritizes analysis of highly relevant business content and know-how. The analysis unit can also postpone analysis of less relevant business content and know-how. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the business content and know-how. This makes it possible to adjust the order of analysis based on the relevance of the business content and know-how. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the business content and know-how to the generation AI and cause the generation AI to adjust the order of analysis.

[0041] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the employee's level of expertise. For example, the analysis unit can provide analysis results that use a lot of technical terminology to employees with a high level of expertise. The analysis unit can also provide analysis results that avoid technical terminology to employees with a low level of expertise. The analysis unit can also dynamically adjust the use of technical terminology in the analysis results according to the employee's level of expertise. This allows the use of technical terminology in the analysis results to be adjusted according to the employee's level of expertise. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input employee expertise level data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0042] When generating the handover document, the generation unit can adjust the level of detail of the handover document based on the importance of the business content and know-how. For example, the generation unit generates a detailed handover document for business content and know-how that are highly important. The generation unit can also generate a simplified handover document for business content and know-how that are less important. The generation unit can also dynamically adjust the level of detail of the handover document according to the importance of the business content and know-how. This makes it possible to adjust the level of detail of the handover document according to the importance of the business content and know-how. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input importance data of the business content and know-how into the generation AI and cause the generation AI to adjust the level of detail of the handover document.

[0043] When generating the handover document, the generation unit can apply an appropriate generation algorithm depending on the business content and the category of know-how. For example, if the business content is technical, the generation unit can apply a technical generation algorithm. Furthermore, if the business content is managerial, the generation unit can also apply a managerial generation algorithm. Furthermore, if the business content is creative, the generation unit can also apply a creative generation algorithm. This makes it possible to apply the optimal generation algorithm depending on the business content and the category of know-how. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input category data of the business content and know-how into the generation AI and cause the generation AI to apply the generation algorithm.

[0044] When generating a handover document, the generation unit can improve the accuracy of the generation by referring to the employee's past handover document results. For example, the generation unit can improve accuracy by adjusting the generation algorithm based on the employee's past handover document results. The generation unit can also optimize the generation parameters by referring to the employee's past handover document results. The generation unit can also analyze the employee's past handover document results and provide feedback to improve the accuracy of the generation. This makes it possible to improve the accuracy of the generation by referring to the past handover document results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the employee's past handover document data into the generation AI and cause the generation AI to improve the accuracy of the generation.

[0045] When generating the handover document, the generation unit can determine the priority of the handover document based on the submission date of the work content and know-how. For example, the generation unit prioritizes the work content and know-how that are due to be submitted soon in the handover document. The generation unit can also reflect the work content and know-how that are due to be submitted further back in the handover document. The generation unit can also dynamically adjust the priority of the handover document based on the submission date. This makes it possible to adjust the priority of the handover document based on the submission date. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the submission date of the work content and know-how into the generation AI and have the generation AI determine the priority of the handover document.

[0046] When generating the handover document, the generation unit can adjust the order of the handover document based on the relevance of the business content and know-how. For example, the generation unit prioritizes reflecting highly relevant business content and know-how in the handover document. The generation unit can also reflect less relevant business content and know-how in the handover document later. The generation unit can also dynamically adjust the order of the handover document based on the relevance of the business content and know-how. This makes it possible to adjust the order of the handover document based on the relevance of the business content and know-how. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input relevance data of the business content and know-how into the generation AI and cause the generation AI to adjust the order of the handover document.

[0047] When generating the handover document, the generation unit can adjust the use of technical terminology in the handover document according to the employee's level of expertise. For example, the generation unit generates a handover document that uses a lot of technical terminology for an employee with a high level of expertise. The generation unit can also generate a handover document that avoids technical terminology for an employee with a low level of expertise. The generation unit can also dynamically adjust the use of technical terminology in the handover document according to the employee's level of expertise. This allows the use of technical terminology in the handover document to be adjusted according to the employee's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input employee expertise level data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0048] When providing the handover document, the provision unit can select an appropriate delivery method based on the employee's past handover document usage history. The provision unit selects the optimal delivery method, for example, based on the format of the handover document used by the employee in the past. The provision unit can also analyze the employee's past handover document usage history and propose the optimal delivery method. The provision unit can also customize the delivery method by referring to the employee's past handover document usage history. This makes it possible to select the optimal delivery method by referring to the past handover document usage history. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the employee's past usage history data into the generation AI and have the generation AI select the delivery method.

[0049] When providing the handover document, the providing unit can customize the content to be provided according to the employee's current task. For example, the providing unit prioritizes providing information related to the task the employee is currently working on. The providing unit can also customize the content of the handover document based on the employee's current task. The providing unit can also dynamically adjust the content to be provided taking into account the employee's current task. This allows the content to be customized according to the employee's current task. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the employee's task data into a generating AI and have the generating AI customize the content to be provided.

[0050] When providing the handover document, the providing unit can select an appropriate delivery method based on the employee's device information. For example, if the employee is using a smartphone, the providing unit selects a delivery method that matches the screen size. Furthermore, if the employee is using a tablet, the providing unit can select a delivery method optimized for a large screen. Furthermore, if the employee is using a desktop, the providing unit can select a delivery method that includes detailed information. This makes it possible to select the optimal delivery method based on the employee's device information. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the employee's device information into the generation AI and have the generation AI select the delivery method.

[0051] When providing the handover document, the providing unit can select an appropriate delivery method based on the employee's device information. For example, if the employee is using a smartphone, the providing unit selects a delivery method that matches the screen size. Furthermore, if the employee is using a tablet, the providing unit can select a delivery method optimized for a large screen. Furthermore, if the employee is using a desktop, the providing unit can select a delivery method that includes detailed information. This makes it possible to select the optimal delivery method based on the employee's device information. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the employee's device information into the generation AI and have the generation AI select the delivery method.

[0052] When providing the handover document, the providing unit can make the provided content multilingual according to the employee's language setting. The providing unit, for example, automatically sets the language of the handover document based on the language setting of the employee's device. The providing unit can also provide a language switching function if the employee speaks multiple languages. The providing unit can also provide the handover document in a specific language if the employee selects that language. This makes it possible to provide a multilingual handover document according to the employee's language setting. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the employee's language setting data into a generating AI and cause the generating AI to provide multilingual support.

[0053] When providing the handover document, the providing unit can analyze the employee's social media activity and provide related information. For example, the providing unit can provide information about locations where the employee has checked in on social media. The providing unit can also analyze the employee's social media posts and provide related work content and know-how. The providing unit can also provide related information by referring to the activities of the employee's friends on social media. This makes it possible to provide related information based on social media activity. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the employee's social media data into a generation AI and have the generation AI provide related information.

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

[0055] The collection unit can acquire employees' biometric information and take their health conditions into consideration when entering work content and know-how. For example, the collection unit can monitor employees' heart rate and blood pressure and pause entry if any abnormalities are detected. The collection unit can also analyze employees' sleep data and delay entry if they are not getting enough rest. Furthermore, the collection unit can display messages encouraging moderate exercise based on employees' exercise data. This makes it possible to enter work content and know-how that takes employees' health conditions into consideration.

[0056] The analysis department can analyze employees' past work performance data and personalize the analysis results of work content and know-how. For example, the analysis department can provide advice for success in similar work content based on data from projects that the employee was successful in the past. The analysis department can also provide analysis results that include areas for improvement for work content that the employee struggled with in the past. Furthermore, the analysis department can suggest optimal work flows based on the employee's skill set. This makes it possible to personalize the analysis results by utilizing employees' past performance data.

[0057] The generator can analyze an employee's past usage history of the handover document and personalize the content of the handover document. For example, the generator can highlight sections that the employee has frequently referred to in the past. It can also provide additional explanations and examples for content that the employee had difficulty understanding in the past. Furthermore, it can adjust the format and layout of the handover document based on the employee's past feedback. This makes it possible to generate personalized handover documents that utilize the employee's past usage history.

[0058] The delivery department can select the optimal delivery method based on the employee's past usage history of handover documents. For example, the delivery department can provide handover documents in the format (PDF, HTML, etc.) that the employee has previously preferred. It can also provide documents in a format optimized for the device (smartphone, tablet, etc.) that the employee has previously used. Furthermore, it can also customize the delivery method by reflecting feedback provided by the employee in the past. This makes it possible to select the optimal delivery method by utilizing the employee's past usage history.

[0059] The delivery department can customize the content of the handover document based on the employee's current tasks and project progress. For example, the delivery department can prioritize providing information related to the tasks the employee is currently working on. It can also dynamically add necessary information depending on the employee's project progress. Furthermore, it can adjust the content of the handover document based on the employee's task priority. This makes it possible to provide the optimal handover document based on the employee's current tasks and projects.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection unit inputs each employee's daily work details and know-how. For example, the collection unit provides an interface for employees to manually input their work details. The collection unit can also have a function to automatically collect work details. For example, the collection unit periodically collects employee work details and stores them in a database. Furthermore, the collection unit has a function to set the collection frequency. For example, the collection unit can collect work details daily, weekly, monthly, etc. Step 2: The analysis unit performs analysis based on the information collected by the collection unit. For example, the analysis unit analyzes the business content using data mining technology. The analysis unit can also analyze the business content using statistical analysis technology. Furthermore, the analysis unit can analyze the business content using machine learning algorithms. For example, the analysis unit analyzes patterns in the business content and extracts important information. Step 3: The generation unit generates a handover document based on the information analyzed by the analysis unit. For example, the generation unit automatically generates a handover document based on the analyzed information. The generation unit can also generate a handover document that includes details of the business flow and explanations of technical terms. For example, the generation unit generates a handover document that includes detailed explanations for each step of the business flow. The generation unit can also generate a handover document that includes explanations of technical terms. For example, the generation unit generates a handover document that includes definitions and usage examples of technical terms related to the business. Step 4: The providing unit provides the handover document generated by the generating unit. For example, the providing unit sends the generated handover document to the new employee by email. The providing unit can also store the generated handover document in cloud storage to make it accessible. Furthermore, the providing unit can also provide the generated handover document in paper form. For example, the providing unit prints the generated handover document and distributes it to the new employee.

[0062] (Example 2) A handover platform according to an embodiment of the present invention is a system for accumulating business knowledge and know-how and sharing information. Each employee inputs their daily work details and know-how into the handover platform, and a generation AI analyzes the input information and automatically generates a handover document. This handover document includes detailed workflow and explanations of technical terms. For example, when a new project begins, a handover document is automatically generated that outlines the work performed by the predecessor and important points to note, and is provided to the new employee. This prevents information leaks and misunderstandings and ensures a smooth handover. The document also includes explanations of workflow and technical terms, allowing the new employee to quickly understand the work and proceed efficiently. Furthermore, the handover platform centrally manages the knowledge and know-how of the entire organization and quickly provides necessary information. For example, past cases and success stories related to specific tasks can be searched and referenced. This is expected to improve work efficiency and quality. Furthermore, the generation AI responds to changes in work content and the addition of new information, constantly generating the latest handover document. For example, when a new system is introduced or a business process is changed, the generation AI automatically updates the handover document to provide the latest information. This allows the handover platform to efficiently accumulate and share knowledge and know-how related to work, contributing to improved productivity throughout the organization. For example, the work content written by an employee can be quickly and accurately generated into a handover document and provided to the next person in charge. Employees can also learn specific areas for improvement in their work, which is expected to lead to improved work efficiency and quality.

[0063] The handover platform according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit inputs daily work content and know-how from each employee. For example, the collection unit provides an interface through which employees manually input work content. The collection unit may also include a function for automatically collecting work content. For example, the collection unit periodically collects employee work content and stores it in a database. The collection unit also includes a function for setting the collection frequency. For example, the collection unit can collect work content daily, weekly, monthly, etc. The analysis unit performs analysis based on the information collected by the collection unit. For example, the analysis unit analyzes the work content using data mining technology. The analysis unit can also analyze the work content using statistical analysis technology. The analysis unit can also analyze the work content using a machine learning algorithm. For example, the analysis unit analyzes patterns in the work content and extracts important information. The generation unit generates a handover document based on the information analyzed by the analysis unit. For example, the generation unit automatically generates a handover document based on the analyzed information. The generation unit can also generate a handover document that includes details of the business flow and explanations of technical terms. For example, the generation unit generates a handover document that includes detailed explanations for each step of the business flow. Furthermore, the generation unit can also generate a handover document that includes explanations of technical terms. For example, the generation unit generates a handover document that includes definitions and usage examples of technical terms related to the business. The provision unit provides the handover document generated by the generation unit. For example, the provision unit sends the generated handover document to the new employee by email. The provision unit can also store the generated handover document in cloud storage to make it accessible. Furthermore, the provision unit can provide the generated handover document on paper. For example, the provision unit prints the generated handover document and distributes it to the new employee. This enables the handover platform according to the embodiment to efficiently collect and analyze business content and know-how, and generate and provide handover documents.

[0064] The collection unit can estimate the user's emotions and adjust the timing of inputting work content and know-how based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can delay the input timing to allow the user to input in a relaxed state. Furthermore, if the user is concentrating, the collection unit can also accelerate the input timing to allow the user to input work content and know-how efficiently. Furthermore, if the user is tired, the collection unit can adjust the input timing to encourage the user to input after a break. This allows the user to input work content and know-how at the optimal timing depending on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0065] The collection unit can analyze each employee's past input history and select the optimal input method. For example, the collection unit can analyze the input methods (voice, text, etc.) used by each employee in the past and suggest the optimal input method. The collection unit can also analyze each employee's input speed and accuracy and select the optimal input method. The collection unit can also suggest the optimal input method for a specific time period based on each employee's input history. This improves input efficiency by providing the optimal input method to each employee. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the employee's past input data into a generation AI and have the generation AI select the optimal input method.

[0066] When inputting work content and know-how, the collection unit can filter the information based on the employee's current project or area of ​​interest. For example, the collection unit prioritizes input of information related to the project the employee is currently working on. The collection unit can also filter and input related work content and know-how based on the employee's area of ​​interest. The collection unit can also filter and input related information by referring to the employee's past project history. This allows related information to be input preferentially based on the employee's area of ​​interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the employee's project data to the generation AI and have the generation AI filter the related information.

[0067] The collection unit can select an appropriate input means according to the employee's input method when inputting work content and know-how. For example, if the employee prefers voice input, the collection unit can provide voice input preferentially. Furthermore, if the employee prefers text input, the collection unit can provide text input preferentially. Furthermore, if the employee prefers image input, the collection unit can provide image input preferentially. This improves input convenience by providing an input means according to the employee's preferences. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input employee input method data into the generation AI and have the generation AI select the optimal input means.

[0068] The collection unit can estimate the user's emotions and determine the priority of the work content and know-how to be input based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit prioritizes input of less important work content and know-how. Furthermore, when the user is relaxed, the collection unit can prioritize input of more important work content and know-how. Furthermore, when the user is concentrating, the collection unit can prioritize input of more important work content and know-how. This allows the priority of work content and know-how to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and have the generation AI determine the priority.

[0069] When inputting work content and know-how, the collection unit can prioritize inputting highly relevant information based on the employee's geographical location information. For example, if an employee works in a specific area, the collection unit prioritizes inputting information related to that area. Furthermore, if an employee is on a business trip, the collection unit can prioritize inputting information related to the business trip destination. Furthermore, if an employee is working remotely, the collection unit can prioritize inputting information related to the remote work. This allows highly relevant information to be prioritized based on the geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the employee's geographical location data to the generation AI and cause the generation AI to select relevant information.

[0070] The collection unit can analyze the employee's social media activity and input related information when inputting work content and know-how. The collection unit can input related work content and know-how based on, for example, information shared by the employee on social media. The collection unit can also analyze the employee's social media activity and input related information. The collection unit can also input related information based on the activity of the employee's friends on social media. In this way, related information can be input based on social media activity. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the employee's social media data into the generation AI and have the generation AI select related information.

[0071] The collection unit can customize the input method by reflecting the employee's past feedback when inputting work content and know-how. The collection unit, for example, suggests the optimal input method based on feedback provided by the employee in the past. The collection unit can also customize the input interface by reflecting the employee's past feedback. The collection unit can also improve the input procedure by referring to the employee's past feedback. In this way, the input method can be customized by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the employee's feedback data into the generation AI and have the generation AI customize the input method.

[0072] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a summary analysis result. This allows the way the analysis is presented to be adjusted according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the analysis is presented.

[0073] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the business content and know-how. For example, the analysis unit performs a detailed analysis on business content and know-how that are highly important. The analysis unit can also perform a simplified analysis on business content and know-how that are less important. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the business content and know-how. This allows the level of detail of the analysis to be adjusted according to the importance of the business content and know-how. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the business content and know-how to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0074] During analysis, the analysis unit can apply an appropriate analysis algorithm depending on the business content and know-how category. For example, if the business content is technical, the analysis unit can apply a technical analysis algorithm. Furthermore, if the business content is managerial, the analysis unit can also apply a managerial analysis algorithm. Furthermore, if the business content is creative, the analysis unit can also apply a creative analysis algorithm. This makes it possible to apply the optimal analysis algorithm depending on the business content and know-how category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the business content and know-how into the generation AI and cause the generation AI to apply the analysis algorithm.

[0075] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the employee's past analysis results. For example, the analysis unit can improve accuracy by adjusting the analysis algorithm based on the employee's past analysis results. The analysis unit can also optimize the analysis parameters by referring to the employee's past analysis results. The analysis unit can also analyze the employee's past analysis results and provide feedback to improve the analysis accuracy. This makes it possible to improve the analysis accuracy by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the employee's past analysis data into the generation AI and have the generation AI improve the analysis accuracy.

[0076] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with a visually stimulating effect. This allows the length of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0077] During analysis, the analysis unit can determine the priority of analysis based on the business content and the submission time of the know-how. For example, the analysis unit prioritizes the analysis of business content and know-how that are due to be submitted soon. The analysis unit can also postpone the analysis of business content and know-how that are due to be submitted further in the future. The analysis unit can also dynamically adjust the priority of analysis based on the submission time. This makes it possible to adjust the priority of analysis based on the submission time. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission time of the business content and know-how to the generation AI and have the generation AI determine the priority of analysis.

[0078] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the business content and know-how. For example, the analysis unit prioritizes analysis of highly relevant business content and know-how. The analysis unit can also postpone analysis of less relevant business content and know-how. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the business content and know-how. This makes it possible to adjust the order of analysis based on the relevance of the business content and know-how. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the business content and know-how to the generation AI and cause the generation AI to adjust the order of analysis.

[0079] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the employee's level of expertise. For example, the analysis unit can provide analysis results that use a lot of technical terminology to employees with a high level of expertise. The analysis unit can also provide analysis results that avoid technical terminology to employees with a low level of expertise. The analysis unit can also dynamically adjust the use of technical terminology in the analysis results according to the employee's level of expertise. This allows the use of technical terminology in the analysis results to be adjusted according to the employee's level of expertise. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input employee expertise level data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0080] The generation unit can estimate the user's emotions and adjust the expression style of the handover document based on the estimated user emotions. For example, if the user is nervous, the generation unit generates a simple, highly visible handover document. Furthermore, if the user is relaxed, the generation unit can generate a handover document that includes detailed information. Furthermore, if the user is in a hurry, the generation unit can generate a handover document that focuses on the main points. This allows the expression style of the handover document to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the expression style of the handover document.

[0081] When generating the handover document, the generation unit can adjust the level of detail of the handover document based on the importance of the business content and know-how. For example, the generation unit generates a detailed handover document for business content and know-how that are highly important. The generation unit can also generate a simplified handover document for business content and know-how that are less important. The generation unit can also dynamically adjust the level of detail of the handover document according to the importance of the business content and know-how. This makes it possible to adjust the level of detail of the handover document according to the importance of the business content and know-how. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input importance data of the business content and know-how into the generation AI and cause the generation AI to adjust the level of detail of the handover document.

[0082] When generating the handover document, the generation unit can apply an appropriate generation algorithm depending on the business content and the category of know-how. For example, if the business content is technical, the generation unit can apply a technical generation algorithm. Furthermore, if the business content is managerial, the generation unit can also apply a managerial generation algorithm. Furthermore, if the business content is creative, the generation unit can also apply a creative generation algorithm. This makes it possible to apply the optimal generation algorithm depending on the business content and the category of know-how. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input category data of the business content and know-how into the generation AI and cause the generation AI to apply the generation algorithm.

[0083] When generating a handover document, the generation unit can improve the accuracy of the generation by referring to the employee's past handover document results. For example, the generation unit can improve accuracy by adjusting the generation algorithm based on the employee's past handover document results. The generation unit can also optimize the generation parameters by referring to the employee's past handover document results. The generation unit can also analyze the employee's past handover document results and provide feedback to improve the accuracy of the generation. This makes it possible to improve the accuracy of the generation by referring to the past handover document results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the employee's past handover document data into the generation AI and cause the generation AI to improve the accuracy of the generation.

[0084] The generation unit can estimate the user's emotions and adjust the length of the handover document based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise handover document. If the user is relaxed, the generation unit can generate a longer handover document with detailed explanations. If the user is excited, the generation unit can generate a handover document with visually stimulating effects. This allows the length of the handover document to be adjusted according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the handover document.

[0085] When generating the handover document, the generation unit can determine the priority of the handover document based on the submission date of the work content and know-how. For example, the generation unit prioritizes the work content and know-how that are due to be submitted soon in the handover document. The generation unit can also reflect the work content and know-how that are due to be submitted further back in the handover document. The generation unit can also dynamically adjust the priority of the handover document based on the submission date. This makes it possible to adjust the priority of the handover document based on the submission date. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the submission date of the work content and know-how into the generation AI and have the generation AI determine the priority of the handover document.

[0086] When generating the handover document, the generation unit can adjust the order of the handover document based on the relevance of the business content and know-how. For example, the generation unit prioritizes reflecting highly relevant business content and know-how in the handover document. The generation unit can also reflect less relevant business content and know-how in the handover document later. The generation unit can also dynamically adjust the order of the handover document based on the relevance of the business content and know-how. This makes it possible to adjust the order of the handover document based on the relevance of the business content and know-how. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input relevance data of the business content and know-how into the generation AI and cause the generation AI to adjust the order of the handover document.

[0087] When generating the handover document, the generation unit can adjust the use of technical terminology in the handover document according to the employee's level of expertise. For example, the generation unit generates a handover document that uses a lot of technical terminology for an employee with a high level of expertise. The generation unit can also generate a handover document that avoids technical terminology for an employee with a low level of expertise. The generation unit can also dynamically adjust the use of technical terminology in the handover document according to the employee's level of expertise. This allows the use of technical terminology in the handover document to be adjusted according to the employee's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input employee expertise level data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0088] The providing unit can estimate the user's emotions and adjust the method of providing the handover document based on the estimated user's emotions. For example, if the user is nervous, the providing unit selects a simple, highly visible method of providing the handover document. Furthermore, if the user is relaxed, the providing unit can select a method of providing detailed information. Furthermore, if the user is in a hurry, the providing unit can select a method of providing the handover document that focuses on the main points. This allows the method of providing the handover document to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the method of providing the handover document.

[0089] When providing the handover document, the provision unit can select an appropriate delivery method based on the employee's past handover document usage history. The provision unit selects the optimal delivery method, for example, based on the format of the handover document used by the employee in the past. The provision unit can also analyze the employee's past handover document usage history and propose the optimal delivery method. The provision unit can also customize the delivery method by referring to the employee's past handover document usage history. This makes it possible to select the optimal delivery method by referring to the past handover document usage history. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the employee's past usage history data into the generation AI and have the generation AI select the delivery method.

[0090] When providing the handover document, the providing unit can customize the content to be provided according to the employee's current task. For example, the providing unit prioritizes providing information related to the task the employee is currently working on. The providing unit can also customize the content of the handover document based on the employee's current task. The providing unit can also dynamically adjust the content to be provided taking into account the employee's current task. This allows the content to be customized according to the employee's current task. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the employee's task data into a generating AI and have the generating AI customize the content to be provided.

[0091] When providing the handover document, the providing unit can select an appropriate delivery method based on the employee's device information. For example, if the employee is using a smartphone, the providing unit selects a delivery method that matches the screen size. Furthermore, if the employee is using a tablet, the providing unit can select a delivery method optimized for a large screen. Furthermore, if the employee is using a desktop, the providing unit can select a delivery method that includes detailed information. This makes it possible to select the optimal delivery method based on the employee's device information. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the employee's device information into the generation AI and have the generation AI select the delivery method.

[0092] The providing unit can estimate the user's emotions and adjust the procedures for providing the handover document based on the estimated user's emotions. For example, if the user is nervous, the providing unit selects a procedure that is simple and easy to read. Furthermore, if the user is relaxed, the providing unit can select a procedure that includes detailed information. Furthermore, if the user is in a hurry, the providing unit can select a procedure that focuses on the main points. This allows the procedures for providing the handover document to be adjusted according to the user's emotions. The estimation of emotions is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the procedures for providing the handover document.

[0093] When providing the handover document, the providing unit can select an appropriate delivery method based on the employee's device information. For example, if the employee is using a smartphone, the providing unit selects a delivery method that matches the screen size. Furthermore, if the employee is using a tablet, the providing unit can select a delivery method optimized for a large screen. Furthermore, if the employee is using a desktop, the providing unit can select a delivery method that includes detailed information. This makes it possible to select the optimal delivery method based on the employee's device information. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the employee's device information into the generation AI and have the generation AI select the delivery method.

[0094] When providing the handover document, the providing unit can make the provided content multilingual according to the employee's language setting. The providing unit, for example, automatically sets the language of the handover document based on the language setting of the employee's device. The providing unit can also provide a language switching function if the employee speaks multiple languages. The providing unit can also provide the handover document in a specific language if the employee selects that language. This makes it possible to provide a multilingual handover document according to the employee's language setting. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the employee's language setting data into a generating AI and cause the generating AI to provide multilingual support.

[0095] When providing the handover document, the providing unit can analyze the employee's social media activity and provide related information. For example, the providing unit can provide information about locations where the employee has checked in on social media. The providing unit can also analyze the employee's social media posts and provide related work content and know-how. The providing unit can also provide related information by referring to the activities of the employee's friends on social media. This makes it possible to provide related information based on social media activity. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the employee's social media data into a generation AI and have the generation AI provide related information. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit provides an interface through which employees input their work details using the reception device 38 of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates a handover document based on the analyzed information. The provision unit provides the generated handover document to the new employee using, for example, the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit provides an interface through which an employee uses the microphone 238 of the smart glasses 214 to voice-input the details of their work. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically generates a handover document based on the analyzed information. The provision unit provides the generated handover document to the new employee, for example, by using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit provides an interface through which an employee uses the microphone 238 of the headset terminal 314 to voice-input the details of their work. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates a handover document based on the analyzed information. The provision unit provides the generated handover document to the new employee using, for example, the display 343 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit provides an interface through which an employee uses the microphone 238 of the robot 414 to input work details by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates a handover document based on the analyzed information. The provision unit provides the generated handover document to the new employee using, for example, the speaker 240 of the robot 414.

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

[0097] The collection unit can acquire employees' biometric information and take their health conditions into consideration when entering work content and know-how. For example, the collection unit can monitor employees' heart rate and blood pressure and pause entry if any abnormalities are detected. The collection unit can also analyze employees' sleep data and delay entry if they are not getting enough rest. Furthermore, the collection unit can display messages encouraging moderate exercise based on employees' exercise data. This makes it possible to enter work content and know-how that takes employees' health conditions into consideration.

[0098] The collection unit can estimate the employee's emotions and dynamically change the design of the input interface based on the estimated emotions. For example, if the employee is feeling stressed, the collection unit can adopt colors and designs that have a relaxing effect. Also, if the employee is concentrating, the collection unit can provide a simple, highly visible interface. Furthermore, if the employee is tired, the collection unit can display a message on the input interface encouraging them to take a break. This makes it possible to provide an optimal input environment according to the employee's emotions.

[0099] The analysis department can analyze employees' past work performance data and personalize the analysis results of work content and know-how. For example, the analysis department can provide advice for success in similar work content based on data from projects that the employee was successful in the past. The analysis department can also provide analysis results that include areas for improvement for work content that the employee struggled with in the past. Furthermore, the analysis department can suggest optimal work flows based on the employee's skill set. This makes it possible to personalize the analysis results by utilizing employees' past performance data.

[0100] The analysis unit can estimate the employee's emotions and adjust the way the analysis results are presented based on the estimated emotions. For example, if the employee is tense, the analysis unit can present the analysis results using simple, highly visible graphs and charts. If the employee is relaxed, the analysis unit can provide analysis results that include detailed data and explanations. Furthermore, if the employee is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. This makes it possible to present optimal analysis results according to the employee's emotions.

[0101] The generator can analyze an employee's past usage history of the handover document and personalize the content of the handover document. For example, the generator can highlight sections that the employee has frequently referred to in the past. It can also provide additional explanations and examples for content that the employee had difficulty understanding in the past. Furthermore, it can adjust the format and layout of the handover document based on the employee's past feedback. This makes it possible to generate personalized handover documents that utilize the employee's past usage history.

[0102] The generation unit can estimate the employee's emotions and dynamically adjust the content of the handover document based on the estimated emotions. For example, if the employee is nervous, the generation unit can generate a simple handover document that is to the point. If the employee is relaxed, the generation unit can generate a handover document that includes detailed information and background explanations. Furthermore, if the employee is in a hurry, the generation unit can generate a condensed handover document that can be understood in a short amount of time. This makes it possible to provide the optimal handover document content according to the employee's emotions.

[0103] The delivery department can select the optimal delivery method based on the employee's past usage history of handover documents. For example, the delivery department can provide handover documents in the format (PDF, HTML, etc.) that the employee has previously preferred. It can also provide documents in a format optimized for the device (smartphone, tablet, etc.) that the employee has previously used. Furthermore, it can also customize the delivery method by reflecting feedback provided by the employee in the past. This makes it possible to select the optimal delivery method by utilizing the employee's past usage history.

[0104] The providing unit can estimate the employee's emotions and adjust the timing of providing the handover document based on the estimated emotions. For example, if the employee is feeling stressed, the providing unit can delay the timing of providing the handover document so that the employee can receive it in a relaxed state. Also, if the employee is concentrating, the providing unit can provide the handover document immediately. Furthermore, if the employee is tired, the providing unit can provide the handover document after a break. This makes it possible to set the optimal timing of providing the handover document according to the employee's emotions.

[0105] The delivery department can customize the content of the handover document based on the employee's current tasks and project progress. For example, the delivery department can prioritize providing information related to the tasks the employee is currently working on. It can also dynamically add necessary information depending on the employee's project progress. Furthermore, it can adjust the content of the handover document based on the employee's task priority. This makes it possible to provide the optimal handover document based on the employee's current tasks and projects.

[0106] The provision unit can estimate the employee's emotions and customize the method for providing the handover document based on the estimated emotions. For example, if the employee is nervous, the provision unit can provide the handover document in a simple, highly visible format. If the employee is relaxed, the provision unit can provide the handover document in a format that includes detailed information. Furthermore, if the employee is in a hurry, the provision unit can provide the handover document in a concise format that focuses on the main points. This makes it possible to set the optimal method for providing the handover document according to the employee's emotions.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The collection unit inputs each employee's daily work details and know-how. For example, the collection unit provides an interface for employees to manually input their work details. The collection unit can also have a function to automatically collect work details. For example, the collection unit periodically collects employee work details and stores them in a database. Furthermore, the collection unit has a function to set the collection frequency. For example, the collection unit can collect work details daily, weekly, monthly, etc. Step 2: The analysis unit performs analysis based on the information collected by the collection unit. For example, the analysis unit analyzes the business content using data mining technology. The analysis unit can also analyze the business content using statistical analysis technology. Furthermore, the analysis unit can analyze the business content using machine learning algorithms. For example, the analysis unit analyzes patterns in the business content and extracts important information. Step 3: The generation unit generates a handover document based on the information analyzed by the analysis unit. For example, the generation unit automatically generates a handover document based on the analyzed information. The generation unit can also generate a handover document that includes details of the business flow and explanations of technical terms. For example, the generation unit generates a handover document that includes detailed explanations for each step of the business flow. The generation unit can also generate a handover document that includes explanations of technical terms. For example, the generation unit generates a handover document that includes definitions and usage examples of technical terms related to the business. Step 4: The providing unit provides the handover document generated by the generating unit. For example, the providing unit sends the generated handover document to the new employee by email. The providing unit can also store the generated handover document in cloud storage to make it accessible. Furthermore, the providing unit can also provide the generated handover document in paper form. For example, the providing unit prints the generated handover document and distributes it to the new employee.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0114] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0132] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

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

[0136] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0146] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0149] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0152] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0153] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0154] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0155] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0156] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0157] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0159] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0165] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0166] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0170] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0173] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0174] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0175] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0178] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0180] [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection section where each employee inputs their daily work details and know-how; an analysis unit that performs analysis based on the information collected by the collection unit; a generation unit that generates a handover document based on the information analyzed by the analysis unit; a providing unit that provides the handover document generated by the generating unit; Equipped with A system characterized by:

2. The collecting unit Estimates the user's emotions and adjusts the timing of inputting work content and know-how based on the estimated user emotions 2. The system of claim 1.

3. The collecting unit Analyze each employee's past data entry history and select the appropriate data entry method 2. The system of claim 1.

4. The collecting unit Filter job descriptions and know-how based on employees' current projects and areas of interest 2. The system of claim 1.

5. The collecting unit When entering work details and know-how, select the appropriate input method according to the input method of employees.

2. The system of claim 1.

6. The collecting unit Estimate the user's emotions and determine the priority of the work content and know-how to be entered based on the estimated user emotions 2. The system of claim 1.

7. The collecting unit Prioritize relevant information based on employee geographic location when entering work details and know-how 2. The system of claim 1.

8. The collecting unit When entering work details and know-how, analyze employees' social media activity and enter relevant information.

2. The system of claim 1.

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