system

Abstract

The system according to the embodiment aims to ensure business continuity when an employee is transferred or leaves the company. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a monitoring unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a copy robot based on the data analyzed by the analysis unit. The monitoring unit monitors the progress of work by the copy robot generated by the generation unit.

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 are issues with the loss and transfer of personal know-how when employees are transferred or leave the company, which creates challenges in business continuity.

[0005] The system according to the embodiment aims to ensure business continuity when an employee is transferred or leaves the company. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a monitoring unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a copy robot based on the data analyzed by the analysis unit. The monitoring unit monitors the progress of work by the copy robot generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can ensure business continuity in the event of a transfer or resignation. [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 business continuity support system according to an embodiment of the present invention continuously supports the work of each employee, taking into consideration risks such as the loss of past personal know-how due to transfers, organizational changes, resignations, and leaves of absence, business stagnation due to the long time required for handover, and accidents due to missed handovers or incorrect understanding. The business continuity support system accumulates emails, chats, local files, and other data, and a generation AI understands each employee's work content and know-how to generate a copy robot. The generated copy robot supports business continuity in the event of an emergency. For example, if an employee is absent due to transfer, resignation, or leave of absence, the copy robot performs the employee's work in their place. This reduces the risk of business stagnation, missed handovers, or accidents due to incorrect understanding. The business continuity support system also monitors the progress of work in real time and makes adjustments as necessary. For example, it checks the progress of work and notifies other employees if a delay occurs. It also determines the priority of work and prioritizes important work. This ensures business continuity while taking into consideration risks such as transfers, organizational changes, resignations, and leaves of absence. This allows the business continuity support system to ensure business continuity while taking into account risks such as transfers, resignations, and leave of absence. For example, even if a specific employee resigns, a copy robot can take over that employee's work, preventing business stagnation. In addition, business can be continued smoothly without the need for a huge amount of time for handover.

[0029] A business continuity support system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a monitoring unit. The collection unit collects data. Examples of data include, but are not limited to, emails, chats, and local files. The collection unit collects data from, for example, an email server, a chat app, or a local file system. The collection unit can also select an optimal collection method depending on the type of data. For example, in the case of text data, the collection unit analyzes and collects the content of emails and chats. In the case of image data, the collection unit collects image files from a local file system and stores them in a cloud as needed. In the case of audio data, the collection unit collects recorded data of meetings and converts them into text using speech recognition technology. The analysis unit uses a generation AI to analyze the data collected by the collection unit. The analysis can be performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, examples. For example, the generation AI can analyze the collected data and understand the business content using a text generation AI (e.g., GPT-4 (registered trademark)). The analysis unit can also apply different analysis algorithms depending on the data category. For example, a natural language processing algorithm is applied to text data to analyze the content. An image recognition algorithm is applied to image data to analyze the content. A voice recognition algorithm is applied to voice data to analyze the content. The generation unit generates a copy robot based on the data analyzed by the analysis unit. The generation is performed, for example, using a generation AI, but is not limited to this example. For example, the generation AI generates a copy robot that includes specific work procedures, important contacts, details of past projects, etc. based on the analysis results. The generation unit can also apply different generation algorithms depending on the category of work. For example, for text-based work, a natural language processing algorithm is applied to generate a copy robot. For image-based work, an image recognition algorithm is applied to generate a copy robot. For voice-based work, a voice recognition algorithm is applied to generate a copy robot. The monitoring unit monitors the progress of the work by the copy robot generated by the generation unit. The monitoring is performed, for example, in real time, but is not limited to this example.For example, the monitoring unit checks the progress of work and notifies other employees if there is a delay. The monitoring unit can also determine the priority of work and prioritize important work. This makes it possible for the business continuity support system according to the embodiment to collect and analyze data, generate copy robots, and monitor the progress of work.

[0030] The collection unit can collect data from a mail server, a chat app, or a local file system. The collection unit collects data, for example, from a mail server. For example, the collection unit collects email data using protocols such as IMAP, POP3, and SMTP. The collection unit can also collect data from chat apps. For example, the collection unit collects data from chat apps such as Slack (registered trademark), Microsoft Teams (registered trademark), and WhatsApp (registered trademark). The collection unit can also collect data from a local file system. For example, the collection unit collects data from file systems such as NTFS, FAT32, and ext4. This makes it possible to collect data from a variety of data sources. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input data acquired from a mail server into a generation AI and have the generation AI analyze the data.

[0031] The analysis unit can analyze the collected data using the generation AI and understand the business content. The analysis unit, for example, analyzes the collected data using the generation AI. For example, the generation AI analyzes the collected data using a text generation AI (e.g., GPT-4) to understand the business content. The analysis unit can also apply different analysis algorithms depending on the data category. For example, a natural language processing algorithm is applied to text data to analyze the content. An image recognition algorithm is applied to image data to analyze the content. A voice recognition algorithm is applied to audio data to analyze the content. In this way, the use of the generation AI improves the accuracy of data analysis. 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 collected data into the generation AI and have the generation AI understand the business content.

[0032] The generation unit can generate a copy robot based on the analysis results. The generation unit generates a copy robot based on, for example, the analysis results. For example, the generation AI generates a copy robot that includes specific work procedures, important contacts, details of past projects, etc. based on the analysis results. The generation unit can also apply different generation algorithms depending on the category of work. For example, for text-based work, a natural language processing algorithm is applied to generate a copy robot. For image-based work, an image recognition algorithm is applied to generate a copy robot. For voice-based work, a voice recognition algorithm is applied to generate a copy robot. This makes it possible to generate an accurate copy robot based on the analysis 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 analysis results to the generation AI and cause the generation AI to generate a copy robot.

[0033] The monitoring unit can monitor the progress of work in real time and make adjustments if necessary. The monitoring unit, for example, monitors the progress of work in real time. For example, the monitoring unit checks the progress of work and notifies other employees if there is a delay. The monitoring unit can also determine the priority of work and prioritize important work. This makes it possible to monitor the progress of work in real time and make appropriate adjustments. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input the progress of work into the generation AI and have the generation AI monitor the progress.

[0034] The monitoring unit can check the progress of work and notify other employees when a delay occurs. The monitoring unit, for example, checks the progress of work. For example, the monitoring unit monitors the progress of work in real time and notifies other employees when a delay occurs. The monitoring unit also clarifies specific standards and measurement methods for delays. For example, it sets the number of minutes or more of delay that is considered. This enables work delays to be detected early and notified to other employees, enabling a prompt response. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the progress of work into a generation AI and have the generation AI detect and notify delays.

[0035] The monitoring unit can determine the priority of tasks and prioritize important tasks. The monitoring unit, for example, determines the priority of tasks. For example, the monitoring unit determines the priority based on the importance and deadline of the tasks. The monitoring unit also clarifies specific criteria and methods for determining the priority. For example, the priorities are set based on the importance and deadline of the tasks. This allows important tasks to be prioritized, thereby improving the efficiency of tasks. Some or all of the above-mentioned processing in the monitoring unit may be performed using, or without, AI, for example. For example, the monitoring unit can input the priority of tasks into a generation AI and have the generation AI determine the priority.

[0036] The collection unit can determine the priority of data to be collected based on the importance of each data source. For example, the collection unit determines the priority of data to be collected based on the importance of each data source. For example, the collection unit may collect data from an email server as a first priority to ensure important communications. The collection unit may also collect data from a chat app as a second priority to record daily communications. The collection unit may also collect data from a local file system last to store work-related documents. This enables efficient data management by prioritizing the collection of important data. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input the importance of each data source to the generation AI and have the generation AI determine the priority of the data.

[0037] The collection unit can select the optimal collection means depending on the type of data when collecting data. The collection unit, for example, selects the optimal collection means depending on the type of data when collecting data. For example, in the case of text data, the collection unit analyzes the content of emails and chats. In the case of image data, image files are collected from the local file system and stored in the cloud as necessary. In the case of audio data, recorded data of meetings is collected and converted into text using voice recognition technology. This enables efficient data collection by selecting the optimal collection means depending on the type of data. 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 type of data into a generation AI and have the generation AI select the optimal collection means.

[0038] The collection unit can analyze the user's past data usage history and select the optimal collection method when collecting data. For example, the collection unit can analyze the user's past data usage history when collecting data. For example, the collection unit prioritizes collecting data sources that the user frequently accessed in the past. The collection unit can also analyze the user's past data usage patterns and determine the optimal collection timing. The collection unit can also identify data that the user considered important in the past and prioritize collecting that data. In this way, the optimal collection method can be selected by analyzing the user's past data usage history. 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 user's past data usage history into a generation AI and have the generation AI select the optimal collection method.

[0039] The collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information when collecting data. The collection unit, for example, considers the user's geographical location information when collecting data. For example, when the user is in a specific office, the collection unit can prioritize collecting data related to the office. Furthermore, when the user is on a business trip, the collection unit can prioritize collecting data related to the business trip destination. Furthermore, when the user is working from home, the collection unit can prioritize collecting data related to working from home. In this way, by taking the user's geographical location information into account, highly relevant data can be collected preferentially. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0040] The collection unit can analyze the user's social media activities and collect related data when collecting data. The collection unit, for example, analyzes the user's social media activities when collecting data. For example, the collection unit collects information shared by the user on social media and extracts business-related data. The collection unit can also analyze the user's social media activity history and collect related data. The collection unit can also collect related data by referring to the activities of the user's friends and colleagues on social media. In this way, related data can be efficiently collected by analyzing the user's social media activities. 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 user's social media activity data into a generation AI and cause the generation AI to collect related data.

[0041] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit, for example, reflects the user's past feedback when collecting data. For example, the collection unit adjusts the type of data to be collected based on feedback provided by the user in the past. The collection unit can also optimize the collection timing by referring to the user's past feedback. The collection unit can also customize the collection method (email, chat, local file, etc.) by reflecting the user's feedback. In this way, the optimal collection method can be customized by reflecting the user's 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 user's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, a detailed analysis can be performed on important data to provide a deep understanding. A standard analysis can be performed on general data to provide a basic understanding. A simplified analysis can be performed on less important data to provide the minimum necessary information. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. 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 importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies different analysis algorithms depending on the data category during analysis. For example, a natural language processing algorithm can be applied to text data to analyze the content. An image recognition algorithm can be applied to image data to analyze the content. A voice recognition algorithm can be applied to audio data to analyze the content. This enables highly accurate analysis by applying an analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0044] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit refers to the user's past analysis results during analysis. For example, the analysis unit adjusts the analysis algorithm based on feedback provided by the user in the past. The analysis unit can also analyze the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also select the optimal analysis method by referring to the user's past analysis history. In this way, the accuracy of the analysis can be improved by referring to the user's 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 user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0045] The analysis unit can determine the analysis priority based on the time of data submission during analysis. The analysis unit, for example, determines the analysis priority based on the time of data submission during analysis. For example, urgent data can be analyzed with the highest priority. Data with an approaching submission deadline can also be analyzed with priority. Data with a distant submission deadline can also be analyzed later. In this way, efficient analysis is possible by determining the analysis priority based on the time of data submission. 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 time of data submission to the generation AI and have the generation AI determine the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, highly relevant data can be analyzed first to deepen overall understanding. It is also possible to analyze less relevant data later for efficient processing. The analysis unit can also analyze the relevance of the data and determine the optimal analysis order. This enables efficient analysis by adjusting the analysis order based on the relevance of the data. 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 relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0047] The analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit can provide analysis results using a lot of technical terminology. Alternatively, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. The analysis unit can also analyze the user's level of expertise and adjust the use of optimal technical terminology. This allows for the provision of analysis results that are easy to understand by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0048] The generation unit can adjust the level of detail of the copy robot based on the importance of the analysis results at the time of generation. The generation unit, for example, adjusts the level of detail of the copy robot based on the importance of the analysis results at the time of generation. For example, a copy robot including detailed procedures can be generated for important tasks. A copy robot including standard procedures can also be generated for general tasks. A copy robot including simplified procedures can also be generated for less important tasks. By adjusting the level of detail of the copy robot based on the importance of the analysis results, efficient work execution is possible. 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 the importance of the analysis results to the generation AI and cause the generation AI to adjust the level of detail of the copy robot.

[0049] The generation unit can apply different generation algorithms depending on the category of the task during generation. For example, the generation unit applies different generation algorithms depending on the category of the task during generation. For example, a natural language processing algorithm can be applied to a text-based task to generate a copy robot. Also, an image recognition algorithm can be applied to an image-based task to generate a copy robot. Also, a voice recognition algorithm can be applied to a voice-based task to generate a copy robot. In this way, by applying a generation algorithm depending on the task category, a highly accurate copy robot can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the task category into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0050] The generation unit can improve the accuracy of the copy robot by referring to the user's past generation results during generation. The generation unit, for example, refers to the user's past generation results during generation. For example, the generation unit adjusts the generation algorithm based on feedback from copy robots previously generated by the user. The generation unit can also analyze the user's past generation results to generate a highly accurate copy robot. The generation unit can also select the optimal generation method by referring to the user's past generation history. In this way, a highly accurate copy robot can be generated by referring to the user's past generation results. 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 the user's past generation results into the generation AI and cause the generation AI to improve the accuracy of the copy robot.

[0051] The generation unit can adjust the order in which copy robots are generated based on the submission dates of tasks at the time of generation. The generation unit, for example, adjusts the order in which copy robots are generated based on the submission dates of tasks at the time of generation. For example, copy robots can be generated with the highest priority for urgent tasks. Copy robots can also be generated with priority for tasks with an approaching submission deadline. Copy robots can also be generated later for tasks with a distant submission deadline. This allows for efficient task execution by adjusting the order in which copy robots are generated based on the submission dates of tasks. 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 the submission dates of tasks into the generation AI and cause the generation AI to adjust the order in which copy robots are generated.

[0052] The generation unit can adjust the generation order of copy robots based on the relevance of tasks at the time of generation. The generation unit, for example, adjusts the generation order of copy robots based on the relevance of tasks at the time of generation. For example, the generation unit generates copy robots that prioritize tasks that are highly relevant. It can also generate copy robots that prioritize tasks that are less relevant. The generation unit can also analyze the relevance of tasks and determine the optimal generation order. This enables efficient task execution by adjusting the generation order of copy robots based on the relevance of tasks. 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 the relevance of tasks to the generation AI and have the generation AI adjust the generation order.

[0053] The generation unit can adjust the copy robot generation method according to the user's level of expertise at the time of generation. For example, the generation unit can adjust the copy robot generation method according to the user's level of expertise at the time of generation. For example, if the user has expertise, the generation unit can generate a copy robot including detailed instructions. On the other hand, if the user does not have expertise, the generation unit can generate a copy robot including simple instructions. The generation unit can also analyze the user's level of expertise and adjust the optimal generation method. In this way, an appropriate copy robot can be generated by adjusting the copy robot generation method according to the user'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 the user's level of expertise to the generation AI and cause the generation AI to adjust the generation method.

[0054] The monitoring unit can adjust the level of monitoring detail based on the importance of the task during monitoring. The monitoring unit, for example, adjusts the level of monitoring detail based on the importance of the task during monitoring. For example, detailed monitoring can be performed for important tasks to check their progress in detail. Standard monitoring can be performed for general tasks to check their progress. Simple monitoring can be performed for low-importance tasks to check their minimum necessary progress. This enables efficient monitoring by adjusting the level of monitoring detail based on the importance of the task. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the importance of the task into a generation AI and have the generation AI adjust the level of monitoring detail.

[0055] The monitoring unit can apply different monitoring algorithms depending on the category of the task during monitoring. For example, the monitoring unit can apply different monitoring algorithms depending on the category of the task during monitoring. For example, for text-based tasks, a natural language processing algorithm can be applied to monitor the progress. For image-based tasks, an image recognition algorithm can be applied to monitor the progress. For voice-based tasks, a voice recognition algorithm can be applied to monitor the progress. This enables highly accurate monitoring by applying a monitoring algorithm depending on the task category. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input the task category into a generation AI and cause the generation AI to apply an appropriate monitoring algorithm.

[0056] The monitoring unit can improve the accuracy of monitoring by referring to the user's past monitoring results during monitoring. For example, the monitoring unit refers to the user's past monitoring results during monitoring. For example, the monitoring unit adjusts the monitoring algorithm based on feedback provided by the user in the past. The monitoring unit can also analyze the user's past monitoring results to improve the accuracy of monitoring. The monitoring unit can also select an optimal monitoring method by referring to the user's past monitoring history. In this way, the accuracy of monitoring can be improved by referring to the user's past monitoring results. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's past monitoring results into the generation AI and cause the generation AI to improve the accuracy of monitoring.

[0057] The monitoring unit can determine the monitoring priority based on the submission time of the work during monitoring. The monitoring unit, for example, determines the monitoring priority based on the submission time of the work during monitoring. For example, for urgent work, monitoring of the progress can be given top priority. Furthermore, the progress can be monitored as a priority for work with an approaching submission deadline. Furthermore, the progress can be monitored on a later basis for work with a distant submission deadline. In this way, efficient monitoring is possible by determining the monitoring priority based on the submission time of the work. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the submission time of the work into the generation AI and have the generation AI determine the monitoring priority.

[0058] The monitoring unit can adjust the monitoring order based on the relevance of tasks during monitoring. The monitoring unit, for example, adjusts the monitoring order based on the relevance of tasks during monitoring. For example, it can prioritize monitoring of highly relevant tasks to grasp the overall progress status. It can also postpone monitoring of less relevant tasks to efficiently check their progress status. The monitoring unit can also analyze the relevance of tasks and determine the optimal monitoring order. This enables efficient monitoring by adjusting the monitoring order based on the relevance of tasks. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the relevance of tasks into a generation AI and have the generation AI adjust the monitoring order.

[0059] The monitoring unit can adjust the use of monitoring terminology according to the user's level of expertise during monitoring. For example, the monitoring unit can adjust the use of monitoring terminology according to the user's level of expertise during monitoring. For example, if the user has specialized knowledge, the monitoring unit can report the progress using extensive use of technical terminology. On the other hand, if the user does not have specialized knowledge, the monitoring unit can also report the progress using simple language. The monitoring unit can also analyze the user's level of expertise and adjust the use of optimal technical terminology. This allows for the provision of easy-to-understand monitoring results by adjusting the use of monitoring terminology according to the user's level of expertise. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

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

[0061] The collection unit can analyze the user's past data usage history and select the optimal collection method. For example, the collection unit prioritizes collection of data sources that the user frequently accessed in the past. The collection unit can also analyze the user's past data usage patterns and determine the optimal collection timing. Furthermore, the collection unit can identify data that the user considered important in the past and prioritize collection of that data. In this way, the optimal collection method can be selected by analyzing the user's past data usage history. 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 user's past data usage history into the generation AI and have the generation AI select the optimal collection method.

[0062] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, a detailed analysis can be performed on important data to provide a deep understanding. A standard analysis can be performed on general data to provide a basic understanding. Furthermore, a simplified analysis can be performed on less important data to provide the minimum necessary information. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. 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 importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0063] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific office, the collection unit can prioritize collecting data related to that office. Furthermore, if the user is on a business trip, the collection unit can prioritize collecting data related to the business trip destination. Furthermore, if the user is working from home, the collection unit can prioritize collecting data related to working from home. In this way, highly relevant data can be collected preferentially by taking the user's geographical location information into account. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0064] The generation unit can adjust the order in which copy robots are generated based on the submission dates of tasks during generation. For example, copy robots for urgent tasks can be generated with the highest priority. Copy robots can also be generated with priority for tasks with upcoming submission deadlines. Furthermore, copy robots for tasks with distant submission deadlines can be generated later. This allows for efficient task execution by adjusting the order in which copy robots are generated based on the submission dates of tasks. 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 the submission dates of tasks into the generation AI and have the generation AI adjust the order in which copy robots are generated.

[0065] During monitoring, the monitoring unit can adjust the monitoring order based on the relevance of tasks. For example, it can prioritize monitoring of highly relevant tasks to grasp the overall progress status. It can also postpone monitoring of less relevant tasks to efficiently check their progress. Furthermore, the monitoring unit can analyze the relevance of tasks and determine the optimal monitoring order. This enables efficient monitoring by adjusting the monitoring order based on the relevance of tasks. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the relevance of tasks into a generation AI and have the generation AI adjust the monitoring order.

[0066] During generation, the generation unit can improve the accuracy of the copy robot by referring to the user's past generation results. For example, the generation unit adjusts the generation algorithm based on feedback from copy robots previously generated by the user. The generation unit can also analyze the user's past generation results to generate a highly accurate copy robot. Furthermore, the generation unit can select the optimal generation method by referring to the user's past generation history. In this way, a highly accurate copy robot can be generated by referring to the user's past generation results. 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 the user's past generation results into the generation AI and have the generation AI improve the accuracy of the copy robot.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The collection unit collects data. This data includes emails, chats, local files, etc. The collection unit collects data from email servers, chat apps, and local file systems, and selects the optimal collection method depending on the type of data. For example, in the case of text data, the content of emails and chats is analyzed and collected; in the case of image data, image files are collected from the local file system and stored in the cloud as needed. In the case of audio data, recordings of meetings are collected and converted into text using voice recognition technology. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. For example, the generation AI uses a text generation AI (e.g., GPT-4) to analyze the collected data and understand the business content. The analysis unit can also apply different analysis algorithms depending on the data category. For example, it applies a natural language processing algorithm to text data, an image recognition algorithm to image data, and a voice recognition algorithm to audio data to analyze the content. Step 3: The generation unit generates a copy robot based on the data analyzed by the analysis unit. Generation is performed using a generation AI. For example, the generation AI generates a copy robot that includes specific work procedures, important contacts, details of past projects, etc. based on the analysis results. The generation unit can also apply different generation algorithms depending on the work category. For example, a natural language processing algorithm is applied to text-based work, an image recognition algorithm to image-based work, and a voice recognition algorithm to voice-based work to generate a copy robot. Step 4: The monitoring department monitors the progress of the work performed by the copy robots generated by the generation department. Monitoring is performed in real time. For example, the monitoring department checks the progress of work and notifies other employees if there is a delay. The monitoring department can also determine the priority of work and prioritize important work.

[0069] (Example 2) A business continuity support system according to an embodiment of the present invention continuously supports the work of each employee, taking into consideration risks such as the loss of past personal know-how due to transfers, organizational changes, resignations, and leaves of absence, business stagnation due to the long time required for handover, and accidents due to missed handovers or incorrect understanding. The business continuity support system accumulates emails, chats, local files, and other data, and a generation AI understands each employee's work content and know-how to generate a copy robot. The generated copy robot supports business continuity in the event of an emergency. For example, if an employee is absent due to transfer, resignation, or leave of absence, the copy robot performs the employee's work in their place. This reduces the risk of business stagnation, missed handovers, or accidents due to incorrect understanding. The business continuity support system also monitors the progress of work in real time and makes adjustments as necessary. For example, it checks the progress of work and notifies other employees if a delay occurs. It also determines the priority of work and prioritizes important work. This ensures business continuity while taking into consideration risks such as transfers, organizational changes, resignations, and leaves of absence. This allows the business continuity support system to ensure business continuity while taking into account risks such as transfers, resignations, and leave of absence. For example, even if a specific employee resigns, a copy robot can take over that employee's work, preventing business stagnation. In addition, business can be continued smoothly without the need for a huge amount of time for handover.

[0070] The business continuity support system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a monitoring unit. The collection unit collects data. Examples of data include, but are not limited to, emails, chats, and local files. The collection unit collects data from, for example, a mail server, a chat app, or a local file system. The collection unit can also select an optimal collection method depending on the type of data. For example, in the case of text data, the collection unit analyzes and collects the content of emails and chats. In the case of image data, the collection unit collects image files from a local file system and stores them in a cloud as needed. In the case of audio data, the collection unit collects recorded data of meetings and converts them into text using speech recognition technology. The analysis unit uses a generation AI to analyze the data collected by the collection unit. The analysis can be performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, examples. For example, the generation AI can analyze the collected data and understand the business content using a text generation AI (e.g., GPT-4). The analysis unit can also apply different analysis algorithms depending on the data category. For example, a natural language processing algorithm is applied to text data to analyze the content. An image recognition algorithm is applied to image data to analyze the content. A voice recognition algorithm is applied to voice data to analyze the content. The generation unit generates a copy robot based on the data analyzed by the analysis unit. The generation is performed, for example, using a generation AI, but is not limited to this example. For example, the generation AI generates a copy robot that includes specific work procedures, important contacts, details of past projects, etc. based on the analysis results. The generation unit can also apply different generation algorithms depending on the category of work. For example, for text-based work, a natural language processing algorithm is applied to generate a copy robot. For image-based work, an image recognition algorithm is applied to generate a copy robot. For voice-based work, a voice recognition algorithm is applied to generate a copy robot. The monitoring unit monitors the progress of the work by the copy robot generated by the generation unit. The monitoring is performed, for example, in real time, but is not limited to this example.For example, the monitoring unit checks the progress of work and notifies other employees if there is a delay. The monitoring unit can also determine the priority of work and prioritize important work. This makes it possible for the business continuity support system according to the embodiment to collect and analyze data, generate copy robots, and monitor the progress of work.

[0071] The collection unit can collect data from a mail server, a chat app, or a local file system. The collection unit collects data, for example, from a mail server. For example, the collection unit collects email data using protocols such as IMAP, POP3, and SMTP. The collection unit can also collect data from chat apps. For example, the collection unit collects data from chat apps such as Slack, Microsoft Teams, and WhatsApp. The collection unit can also collect data from a local file system. For example, the collection unit collects data from file systems such as NTFS, FAT32, and ext4. This makes it possible to collect data from a variety of data sources. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input data obtained from the mail server into a generation AI and have the generation AI analyze the data.

[0072] The analysis unit can analyze the collected data using the generation AI and understand the business content. The analysis unit, for example, analyzes the collected data using the generation AI. For example, the generation AI analyzes the collected data using a text generation AI (e.g., GPT-4) to understand the business content. The analysis unit can also apply different analysis algorithms depending on the data category. For example, a natural language processing algorithm is applied to text data to analyze the content. An image recognition algorithm is applied to image data to analyze the content. A voice recognition algorithm is applied to audio data to analyze the content. In this way, the use of the generation AI improves the accuracy of data analysis. 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 collected data into the generation AI and have the generation AI understand the business content.

[0073] The generation unit can generate a copy robot based on the analysis results. The generation unit generates a copy robot based on, for example, the analysis results. For example, the generation AI generates a copy robot that includes specific work procedures, important contacts, details of past projects, etc. based on the analysis results. The generation unit can also apply different generation algorithms depending on the category of work. For example, for text-based work, a natural language processing algorithm is applied to generate a copy robot. For image-based work, an image recognition algorithm is applied to generate a copy robot. For voice-based work, a voice recognition algorithm is applied to generate a copy robot. This makes it possible to generate an accurate copy robot based on the analysis 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 analysis results to the generation AI and cause the generation AI to generate a copy robot.

[0074] The monitoring unit can monitor the progress of work in real time and make adjustments if necessary. The monitoring unit, for example, monitors the progress of work in real time. For example, the monitoring unit checks the progress of work and notifies other employees if there is a delay. The monitoring unit can also determine the priority of work and prioritize important work. This makes it possible to monitor the progress of work in real time and make appropriate adjustments. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input the progress of work into the generation AI and have the generation AI monitor the progress.

[0075] The monitoring unit can check the progress of work and notify other employees when a delay occurs. The monitoring unit, for example, checks the progress of work. For example, the monitoring unit monitors the progress of work in real time and notifies other employees when a delay occurs. The monitoring unit also clarifies specific standards and measurement methods for delays. For example, it sets the number of minutes or more of delay that is considered. This enables work delays to be detected early and notified to other employees, enabling a prompt response. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the progress of work into a generation AI and have the generation AI detect and notify delays.

[0076] The monitoring unit can determine the priority of tasks and prioritize important tasks. The monitoring unit, for example, determines the priority of tasks. For example, the monitoring unit determines the priority based on the importance and deadline of the tasks. The monitoring unit also clarifies specific criteria and methods for determining the priority. For example, the priorities are set based on the importance and deadline of the tasks. This allows important tasks to be prioritized, thereby improving the efficiency of tasks. Some or all of the above-mentioned processing in the monitoring unit may be performed using, or without, AI, for example. For example, the monitoring unit can input the priority of tasks into a generation AI and have the generation AI determine the priority.

[0077] The collection unit can estimate a user's emotions and adjust the timing of data collection based on the estimated user emotions. The collection unit, for example, estimates the user's emotions. For example, the collection unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. The collection unit also adjusts the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of data collection can be reduced to reduce the burden on the user. Alternatively, if the user is relaxed, the frequency of data collection can be increased to collect more detailed data. Alternatively, if the user is in a hurry, only important data can be collected preferentially and processed quickly. This allows the workload to be reduced by adjusting the timing of data collection 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 collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the timing of data collection based on the emotion.

[0078] The collection unit can determine the priority of data to be collected based on the importance of each data source. For example, the collection unit determines the priority of data to be collected based on the importance of each data source. For example, the collection unit may collect data from an email server as a first priority to ensure important communications. The collection unit may also collect data from a chat app as a second priority to record daily communications. The collection unit may also collect data from a local file system last to store work-related documents. This enables efficient data management by prioritizing the collection of important data. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input the importance of each data source to the generation AI and have the generation AI determine the priority of the data.

[0079] The collection unit can select the optimal collection means depending on the type of data when collecting data. The collection unit, for example, selects the optimal collection means depending on the type of data when collecting data. For example, in the case of text data, the collection unit analyzes the content of emails and chats. In the case of image data, image files are collected from the local file system and stored in the cloud as necessary. In the case of audio data, recorded data of meetings is collected and converted into text using voice recognition technology. This enables efficient data collection by selecting the optimal collection means depending on the type of data. 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 type of data into a generation AI and have the generation AI select the optimal collection means.

[0080] The collection unit can analyze the user's past data usage history and select the optimal collection method when collecting data. For example, the collection unit can analyze the user's past data usage history when collecting data. For example, the collection unit prioritizes collecting data sources that the user frequently accessed in the past. The collection unit can also analyze the user's past data usage patterns and determine the optimal collection timing. The collection unit can also identify data that the user considered important in the past and prioritize collecting that data. In this way, the optimal collection method can be selected by analyzing the user's past data usage history. 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 user's past data usage history into a generation AI and have the generation AI select the optimal collection method.

[0081] The collection unit can estimate the user's emotions and filter the data to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions. For example, the collection unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. The collection unit also filters the data to be collected based on the estimated user emotions. For example, if the user is stressed, the collection unit collects only important data and filters unnecessary data. Alternatively, if the user is relaxed, the collection unit can collect detailed data and filter a wide range of information. Alternatively, if the user is in a hurry, the collection unit can collect only data that can be processed quickly and filter other data. This allows important data to be collected preferentially by filtering data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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-mentioned processing in the collection unit may be performed using, for example, an AI, or without an AI. For example, the collection unit can input user emotion data into the generation AI and cause the generation AI to filter the data based on the emotion.

[0082] The collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information when collecting data. The collection unit, for example, considers the user's geographical location information when collecting data. For example, when the user is in a specific office, the collection unit can prioritize collecting data related to the office. Furthermore, when the user is on a business trip, the collection unit can prioritize collecting data related to the business trip destination. Furthermore, when the user is working from home, the collection unit can prioritize collecting data related to working from home. In this way, by taking the user's geographical location information into account, highly relevant data can be collected preferentially. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0083] The collection unit can analyze the user's social media activities and collect related data when collecting data. The collection unit, for example, analyzes the user's social media activities when collecting data. For example, the collection unit collects information shared by the user on social media and extracts business-related data. The collection unit can also analyze the user's social media activity history and collect related data. The collection unit can also collect related data by referring to the activities of the user's friends and colleagues on social media. In this way, related data can be efficiently collected by analyzing the user's social media activities. 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 user's social media activity data into a generation AI and cause the generation AI to collect related data.

[0084] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit, for example, reflects the user's past feedback when collecting data. For example, the collection unit adjusts the type of data to be collected based on feedback provided by the user in the past. The collection unit can also optimize the collection timing by referring to the user's past feedback. The collection unit can also customize the collection method (email, chat, local file, etc.) by reflecting the user's feedback. In this way, the optimal collection method can be customized by reflecting the user's 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 user's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0085] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. For example, the analysis unit estimates the user's emotion using technologies such as facial expression recognition, voice analysis, and text analysis. The analysis unit also adjusts the presentation method of the analysis based on the estimated user's emotion. For example, if the user is relaxed, detailed analysis results can be provided to deepen understanding. If the user is stressed, concise analysis results can be provided that focus on the main points. If the user is in a hurry, visually easy-to-understand analysis results can be provided to enable quick understanding. In this way, by adjusting the presentation method of the analysis according to the user's emotion, easy-to-understand analysis results can be provided. 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 these examples. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the analysis is expressed based on the emotion.

[0086] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, a detailed analysis can be performed on important data to provide a deep understanding. A standard analysis can be performed on general data to provide a basic understanding. A simplified analysis can be performed on less important data to provide the minimum necessary information. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. 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 importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0087] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies different analysis algorithms depending on the data category during analysis. For example, a natural language processing algorithm can be applied to text data to analyze the content. An image recognition algorithm can be applied to image data to analyze the content. A voice recognition algorithm can be applied to audio data to analyze the content. This enables highly accurate analysis by applying an analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0088] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit refers to the user's past analysis results during analysis. For example, the analysis unit adjusts the analysis algorithm based on feedback provided by the user in the past. The analysis unit can also analyze the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also select the optimal analysis method by referring to the user's past analysis history. In this way, the accuracy of the analysis can be improved by referring to the user's 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 user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0089] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. For example, the analysis unit estimates the user's emotion using technologies such as facial expression recognition, voice analysis, and text analysis. The analysis unit also adjusts the length of the analysis based on the estimated user's emotion. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is stressed, the analysis unit can provide a concise, visually easy-to-understand analysis result. By adjusting the length of the analysis according to the user's emotion, it is possible to provide an easy-to-understand analysis result. 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 these examples. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the length of the analysis based on the emotion.

[0090] The analysis unit can determine the analysis priority based on the time of data submission during analysis. The analysis unit, for example, determines the analysis priority based on the time of data submission during analysis. For example, urgent data can be analyzed with the highest priority. Data with an approaching submission deadline can also be analyzed with priority. Data with a distant submission deadline can also be analyzed later. In this way, efficient analysis is possible by determining the analysis priority based on the time of data submission. 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 time of data submission to the generation AI and have the generation AI determine the analysis priority.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, highly relevant data can be analyzed first to deepen overall understanding. It is also possible to analyze less relevant data later for efficient processing. The analysis unit can also analyze the relevance of the data and determine the optimal analysis order. This enables efficient analysis by adjusting the analysis order based on the relevance of the data. 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 relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0092] The analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit can provide analysis results using a lot of technical terminology. Alternatively, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. The analysis unit can also analyze the user's level of expertise and adjust the use of optimal technical terminology. This allows for the provision of analysis results that are easy to understand by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0093] The generation unit can estimate the user's emotions and adjust the copy robot generation method based on the estimated user emotions. The generation unit, for example, estimates the user's emotions. For example, the generation unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. The generation unit also adjusts the copy robot generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates a copy robot that includes detailed work procedures. If the user is stressed, the generation unit can generate a copy robot that is concise and to the point. If the user is in a hurry, the generation unit can generate a copy robot that can quickly perform tasks. In this way, by adjusting the copy robot generation method according to the user's emotions, an appropriate copy robot can be generated. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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-mentioned processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the method of generating a copy robot based on the emotion.

[0094] The generation unit can adjust the level of detail of the copy robot based on the importance of the analysis results at the time of generation. The generation unit, for example, adjusts the level of detail of the copy robot based on the importance of the analysis results at the time of generation. For example, a copy robot including detailed procedures can be generated for important tasks. A copy robot including standard procedures can also be generated for general tasks. A copy robot including simplified procedures can also be generated for less important tasks. By adjusting the level of detail of the copy robot based on the importance of the analysis results, efficient work execution is possible. 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 the importance of the analysis results to the generation AI and cause the generation AI to adjust the level of detail of the copy robot.

[0095] The generation unit can apply different generation algorithms depending on the category of the task during generation. For example, the generation unit applies different generation algorithms depending on the category of the task during generation. For example, a natural language processing algorithm can be applied to a text-based task to generate a copy robot. Also, an image recognition algorithm can be applied to an image-based task to generate a copy robot. Also, a voice recognition algorithm can be applied to a voice-based task to generate a copy robot. In this way, by applying a generation algorithm depending on the task category, a highly accurate copy robot can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the task category into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0096] The generation unit can improve the accuracy of the copy robot by referring to the user's past generation results during generation. The generation unit, for example, refers to the user's past generation results during generation. For example, the generation unit adjusts the generation algorithm based on feedback from copy robots previously generated by the user. The generation unit can also analyze the user's past generation results to generate a highly accurate copy robot. The generation unit can also select the optimal generation method by referring to the user's past generation history. In this way, a highly accurate copy robot can be generated by referring to the user's past generation results. 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 the user's past generation results into the generation AI and cause the generation AI to improve the accuracy of the copy robot.

[0097] The generation unit can estimate the user's emotions and prioritize copy robots based on the estimated user emotions. The generation unit, for example, estimates the user's emotions. For example, the generation unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. The generation unit also prioritizes copy robots based on the estimated user emotions. For example, if the user is stressed, the generation unit generates a copy robot that prioritizes important tasks. Also, if the user is relaxed, the generation unit can generate a copy robot that includes detailed task procedures. Also, if the user is in a hurry, the generation unit can generate a copy robot that can quickly perform tasks. This allows important tasks to be prioritized by prioritizing copy robots 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 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 may be performed using, for example, AI, or without AI. For example, the generation unit can input the user's emotional data into the generation AI and have the generation AI determine the priority of copy robots based on emotions.

[0098] The generation unit can adjust the order in which copy robots are generated based on the submission dates of tasks at the time of generation. The generation unit, for example, adjusts the order in which copy robots are generated based on the submission dates of tasks at the time of generation. For example, copy robots can be generated with the highest priority for urgent tasks. Copy robots can also be generated with priority for tasks with an approaching submission deadline. Copy robots can also be generated later for tasks with a distant submission deadline. This allows for efficient task execution by adjusting the order in which copy robots are generated based on the submission dates of tasks. 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 the submission dates of tasks into the generation AI and cause the generation AI to adjust the order in which copy robots are generated.

[0099] The generation unit can adjust the generation order of copy robots based on the relevance of tasks at the time of generation. The generation unit, for example, adjusts the generation order of copy robots based on the relevance of tasks at the time of generation. For example, the generation unit generates copy robots that prioritize tasks that are highly relevant. It can also generate copy robots that prioritize tasks that are less relevant. The generation unit can also analyze the relevance of tasks and determine the optimal generation order. This enables efficient task execution by adjusting the generation order of copy robots based on the relevance of tasks. 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 the relevance of tasks to the generation AI and have the generation AI adjust the generation order.

[0100] The generation unit can adjust the copy robot generation method according to the user's level of expertise at the time of generation. For example, the generation unit can adjust the copy robot generation method according to the user's level of expertise at the time of generation. For example, if the user has expertise, the generation unit can generate a copy robot including detailed instructions. On the other hand, if the user does not have expertise, the generation unit can generate a copy robot including simple instructions. The generation unit can also analyze the user's level of expertise and adjust the optimal generation method. In this way, an appropriate copy robot can be generated by adjusting the copy robot generation method according to the user'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 the user's level of expertise to the generation AI and cause the generation AI to adjust the generation method.

[0101] The monitoring unit can estimate the user's emotions and adjust the monitoring method for the progress of work based on the estimated user emotions. The monitoring unit, for example, estimates the user's emotions. For example, the monitoring unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. The monitoring unit also adjusts the monitoring method for the progress of work based on the estimated user emotions. For example, if the user is feeling stressed, the progress of important work can be monitored as a priority. Also, if the user is relaxed, the progress of detailed work can be monitored. Also, if the user is in a hurry, the progress of work can be quickly checked and adjusted as necessary. This enables appropriate monitoring by adjusting the monitoring method for the progress of work based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using 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-mentioned processing in the monitoring unit can be performed using, for example, an AI, or without an AI. For example, the monitoring unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the monitoring method based on the emotion.

[0102] The monitoring unit can adjust the level of monitoring detail based on the importance of the task during monitoring. The monitoring unit, for example, adjusts the level of monitoring detail based on the importance of the task during monitoring. For example, detailed monitoring can be performed for important tasks to check their progress in detail. Standard monitoring can be performed for general tasks to check their progress. Simple monitoring can be performed for low-importance tasks to check their minimum necessary progress. This enables efficient monitoring by adjusting the level of monitoring detail based on the importance of the task. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the importance of the task into a generation AI and have the generation AI adjust the level of monitoring detail.

[0103] The monitoring unit can apply different monitoring algorithms depending on the category of the task during monitoring. For example, the monitoring unit can apply different monitoring algorithms depending on the category of the task during monitoring. For example, for text-based tasks, a natural language processing algorithm can be applied to monitor the progress. For image-based tasks, an image recognition algorithm can be applied to monitor the progress. For voice-based tasks, a voice recognition algorithm can be applied to monitor the progress. This enables highly accurate monitoring by applying a monitoring algorithm depending on the task category. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input the task category into a generation AI and cause the generation AI to apply an appropriate monitoring algorithm.

[0104] The monitoring unit can improve the accuracy of monitoring by referring to the user's past monitoring results during monitoring. For example, the monitoring unit refers to the user's past monitoring results during monitoring. For example, the monitoring unit adjusts the monitoring algorithm based on feedback provided by the user in the past. The monitoring unit can also analyze the user's past monitoring results to improve the accuracy of monitoring. The monitoring unit can also select an optimal monitoring method by referring to the user's past monitoring history. In this way, the accuracy of monitoring can be improved by referring to the user's past monitoring results. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's past monitoring results into the generation AI and cause the generation AI to improve the accuracy of monitoring.

[0105] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated user emotions. The monitoring unit, for example, estimates the user's emotions. For example, the monitoring unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. The monitoring unit also determines monitoring priorities based on the estimated user emotions. For example, if the user is stressed, the monitoring unit prioritizes monitoring the progress of important tasks. If the user is relaxed, the monitoring unit can also monitor the progress of detailed tasks. If the user is in a hurry, the monitoring unit can quickly check the progress of tasks and adjust them as necessary. This allows important tasks to be prioritized by determining monitoring priorities based on 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 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-mentioned processing in the monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can input the user's emotional data into the generation AI and have the generation AI determine monitoring priorities based on emotions.

[0106] The monitoring unit can determine the monitoring priority based on the submission time of the work during monitoring. The monitoring unit, for example, determines the monitoring priority based on the submission time of the work during monitoring. For example, for urgent work, monitoring of the progress can be given top priority. Furthermore, the progress can be monitored as a priority for work with an approaching submission deadline. Furthermore, the progress can be monitored on a later basis for work with a distant submission deadline. In this way, efficient monitoring is possible by determining the monitoring priority based on the submission time of the work. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the submission time of the work into the generation AI and have the generation AI determine the monitoring priority.

[0107] The monitoring unit can adjust the monitoring order based on the relevance of tasks during monitoring. The monitoring unit, for example, adjusts the monitoring order based on the relevance of tasks during monitoring. For example, it can prioritize monitoring of highly relevant tasks to grasp the overall progress status. It can also postpone monitoring of less relevant tasks to efficiently check their progress status. The monitoring unit can also analyze the relevance of tasks and determine the optimal monitoring order. This enables efficient monitoring by adjusting the monitoring order based on the relevance of tasks. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the relevance of tasks into a generation AI and have the generation AI adjust the monitoring order.

[0108] The monitoring unit can adjust the use of monitoring terminology according to the user's level of expertise during monitoring. For example, the monitoring unit can adjust the use of monitoring terminology according to the user's level of expertise during monitoring. For example, if the user has specialized knowledge, the monitoring unit can report the progress using extensive use of technical terminology. On the other hand, if the user does not have specialized knowledge, the monitoring unit can also report the progress using simple language. The monitoring unit can also analyze the user's level of expertise and adjust the use of optimal technical terminology. This allows for the provision of easy-to-understand monitoring results by adjusting the use of monitoring terminology according to the user's level of expertise. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and monitoring 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 collects data using the camera 42 and microphone 38B of the smart device 14 and transmits the collected data to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a copy robot based on the analysis results. The monitoring unit is realized, for example, by the control unit 46A of the smart device 14 and monitors the progress of the generated copy robot's work. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and monitoring 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 collects data using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected data to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a copy robot based on the analysis results. The monitoring unit is realized, for example, by the control unit 46A of the smart glasses 214 and monitors the progress of the generated copy robot's work. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and monitoring unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the headset type terminal 314 and transmits the collected data to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a copy robot based on the analysis results. The monitoring unit is realized, for example, by the control unit 46A of the headset type terminal 314 and monitors the progress of work by the generated copy robot. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and monitoring 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 collects data using the camera 42 and microphone 238 of the robot 414 and transmits the collected data to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a copy robot based on the analysis results. The monitoring unit is realized, for example, by the control unit 46A of the robot 414 and monitors the progress of the generated copy robot's work.

[0109] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0110] The analysis unit can estimate the user's emotions and determine analysis priorities based on the estimated user emotions. For example, if the user is stressed, important data can be analyzed first and results can be provided quickly. If the user is relaxed, detailed analysis can be performed to provide a deeper understanding. Furthermore, if the user is in a hurry, concise, to-the-point analysis results can be provided. This enables efficient and appropriate analysis by adjusting the analysis priorities 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 determine the analysis priorities based on emotions.

[0111] The collection unit can analyze the user's past data usage history and select the optimal collection method. For example, the collection unit prioritizes collection of data sources that the user frequently accessed in the past. The collection unit can also analyze the user's past data usage patterns and determine the optimal collection timing. Furthermore, the collection unit can identify data that the user considered important in the past and prioritize collection of that data. In this way, the optimal collection method can be selected by analyzing the user's past data usage history. 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 user's past data usage history into the generation AI and have the generation AI select the optimal collection method.

[0112] The generation unit can estimate the user's emotions and adjust the copy robot generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a copy robot that includes detailed work procedures. If the user is stressed, the generation unit can generate a copy robot that is concise and focused on the main points. Furthermore, if the user is in a hurry, the generation unit can generate a copy robot that can quickly perform tasks. By adjusting the copy robot generation method according to the user's emotions, an appropriate copy robot can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using 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, AI, or without AI. For example, the generation unit can input user emotion data into the generation AI and cause the generation AI to adjust the copy robot generation method based on the emotion.

[0113] The monitoring unit can estimate the user's emotions and adjust the monitoring method for the progress of work based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit can prioritize monitoring the progress of important work. Also, if the user is relaxed, the monitoring unit can monitor the progress of detailed work. Furthermore, if the user is in a hurry, the monitoring unit can quickly check the progress of work and adjust it as necessary. This enables appropriate monitoring by adjusting the monitoring method for the progress of work based on 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 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 monitoring unit can be performed using, for example, an AI, or without an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and have the generation AI adjust the monitoring method based on the emotion.

[0114] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, a detailed analysis can be performed on important data to provide a deep understanding. A standard analysis can be performed on general data to provide a basic understanding. Furthermore, a simplified analysis can be performed on less important data to provide the minimum necessary information. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. 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 importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0115] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific office, the collection unit can prioritize collecting data related to that office. Furthermore, if the user is on a business trip, the collection unit can prioritize collecting data related to the business trip destination. Furthermore, if the user is working from home, the collection unit can prioritize collecting data related to working from home. In this way, highly relevant data can be collected preferentially by taking the user's geographical location information into account. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0116] The generation unit can adjust the order in which copy robots are generated based on the submission dates of tasks during generation. For example, copy robots for urgent tasks can be generated with the highest priority. Copy robots can also be generated with priority for tasks with upcoming submission deadlines. Furthermore, copy robots for tasks with distant submission deadlines can be generated later. This allows for efficient task execution by adjusting the order in which copy robots are generated based on the submission dates of tasks. 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 the submission dates of tasks into the generation AI and have the generation AI adjust the order in which copy robots are generated.

[0117] During monitoring, the monitoring unit can adjust the monitoring order based on the relevance of tasks. For example, it can prioritize monitoring of highly relevant tasks to grasp the overall progress status. It can also postpone monitoring of less relevant tasks to efficiently check their progress. Furthermore, the monitoring unit can analyze the relevance of tasks and determine the optimal monitoring order. This enables efficient monitoring by adjusting the monitoring order based on the relevance of tasks. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the relevance of tasks into a generation AI and have the generation AI adjust the monitoring order.

[0118] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated user emotions. For example, if the user is relaxed, detailed analysis results can be provided to deepen understanding. Furthermore, if the user is stressed, concise and concise analysis results can be provided. Furthermore, if the user is in a hurry, visually easy-to-understand analysis results can be provided to facilitate quick understanding. By adjusting the presentation of the analysis based on the user's emotions, it is possible to provide easy-to-understand analysis results. Emotion estimation is achieved using an emotion estimation function, such as 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 analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation of the analysis based on the emotion.

[0119] During generation, the generation unit can improve the accuracy of the copy robot by referring to the user's past generation results. For example, the generation unit adjusts the generation algorithm based on feedback from copy robots previously generated by the user. The generation unit can also analyze the user's past generation results to generate a highly accurate copy robot. Furthermore, the generation unit can select the optimal generation method by referring to the user's past generation history. In this way, a highly accurate copy robot can be generated by referring to the user's past generation results. 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 the user's past generation results into the generation AI and have the generation AI improve the accuracy of the copy robot.

[0120] The processing flow of the second embodiment will be briefly explained below.

[0121] Step 1: The collection unit collects data. This data includes emails, chats, local files, etc. The collection unit collects data from email servers, chat apps, and local file systems, and selects the optimal collection method depending on the type of data. For example, in the case of text data, the content of emails and chats is analyzed and collected; in the case of image data, image files are collected from the local file system and stored in the cloud as needed. In the case of audio data, recordings of meetings are collected and converted into text using voice recognition technology. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. For example, the generation AI uses a text generation AI (e.g., GPT-4) to analyze the collected data and understand the business content. The analysis unit can also apply different analysis algorithms depending on the data category. For example, it applies a natural language processing algorithm to text data, an image recognition algorithm to image data, and a voice recognition algorithm to audio data to analyze the content. Step 3: The generation unit generates a copy robot based on the data analyzed by the analysis unit. Generation is performed using a generation AI. For example, the generation AI generates a copy robot that includes specific work procedures, important contacts, details of past projects, etc. based on the analysis results. The generation unit can also apply different generation algorithms depending on the work category. For example, a natural language processing algorithm is applied to text-based work, an image recognition algorithm to image-based work, and a voice recognition algorithm to voice-based work to generate a copy robot. Step 4: The monitoring department monitors the progress of the work performed by the copy robots generated by the generation department. Monitoring is performed in real time. For example, the monitoring department checks the progress of work and notifies other employees if there is a delay. The monitoring department can also determine the priority of work and prioritize important work.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] [Explanation of symbols]

[0194] 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 unit that collects data; an analysis unit that analyzes the data collected by the collection unit; a generation unit that generates a copy robot based on the data analyzed by the analysis unit; a monitoring unit that monitors the progress of the work of the copy robot generated by the generation unit. A system characterized by:

2. The collecting unit Collect data from mail servers, chat apps, and local file systems 2. The system of claim 1.

3. The analysis unit Analyze collected data using generative AI to understand business operations 2. The system of claim 1.

4. The generation unit Generate a copy robot based on the analysis results 2. The system of claim 1.

5. The monitoring unit Monitor work progress in real time and make adjustments when necessary 2. The system of claim 1.

6. The monitoring unit Check the progress of work and notify other employees when delays occur 2. The system of claim 1.

7. The monitoring unit Determine the priorities of work and prioritize important tasks 2. The system of claim 1.

8. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

9. The collecting unit Prioritize the data you collect based on the importance of each data source 2. The system of claim 1.

10. The collecting unit When collecting data, choose the most appropriate collection method depending on the type of data.

2. The system of claim 1.

Images