system

Abstract

The system according to the embodiment aims to enable a superior to efficiently grasp the work progress and issues of his / her subordinates and provide appropriate follow-up. [Solution] The system according to the embodiment comprises a collection unit, a learning unit, a summary creation unit, a reporting unit, a question receiving unit, and an advice providing unit. The collection unit collects data on subordinates' business emails or chats. The learning unit allows the generation AI to learn from the data collected by the collection unit. The summary creation unit allows the generation AI to compile a summary of the subordinates' activities based on the data learned by the learning unit. The reporting unit reports the summary created by the summary creation unit to the superior. The question receiving unit receives questions from the superior based on the summary reported by the reporting unit. The advice providing unit allows the generation AI to provide advice based on the questions received by the question receiving 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, it was difficult for managers to efficiently grasp the work progress and issues of their subordinates, which resulted in insufficient follow-up with their subordinates.

[0005] The system according to the embodiment aims to enable a superior to efficiently grasp the work progress and issues of his / her subordinates and provide appropriate follow-up. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a learning unit, a summary creation unit, a reporting unit, a question receiving unit, and an advice providing unit. The collection unit collects data on subordinates' business emails or chats. The learning unit allows the generation AI to learn from the data collected by the collection unit. The summary creation unit allows the generation AI to create a summary of the subordinates' activities based on the data learned by the learning unit. The reporting unit reports the summary created by the summary creation unit to the superior. The question receiving unit receives questions from the superior based on the summary reported by the reporting unit. The advice providing unit allows the generation AI to provide advice based on the questions received by the question receiving unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a supervisor to efficiently grasp the work progress and issues of his / her subordinates and provide appropriate follow-up. [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 reporting system according to an embodiment of the present invention has a generation AI that learns from subordinates' work emails and chats, and then summarizes the subordinates' activities and reports them to their superiors. This reporting system collects data on subordinates' work emails and chats, and the generation AI learns from this data and summarizes the subordinates' activities. This summary is then reported to the superior. By reviewing this summary, the superior can understand the subordinates' work progress and challenges. The superior can also ask the generation AI questions to receive follow-up and advice. For example, if a subordinate reports project progress via email, the generation AI learns from the email and summarizes the project progress. By reviewing this summary, the superior can quickly understand the project progress. Furthermore, when a superior asks the generation AI, "What is the next step in this project?", the generation AI advises on the next step based on the content of the subordinates' emails and chats. This system allows superiors to follow up on their subordinates even when they are busy, and it also solves the problem of subordinates not having time to consult with their superiors. Furthermore, the generation AI summarizes the activities of subordinates, allowing superiors to quickly grasp the progress and issues of their subordinates' work, thereby improving work efficiency.This reporting system allows superiors to quickly grasp the progress and issues of their subordinates' work.

[0029] A reporting system according to an embodiment includes a collection unit, a learning unit, a summary creation unit, a reporting unit, a question receiving unit, and an advice providing unit. The collection unit collects data on subordinates' business emails or chats. The data on subordinates' business emails or chats includes, but is not limited to, text data, attachments, and image data. The collection unit, for example, builds a system that automatically collects subordinates' business emails. The collection unit can also collect chat data on subordinates in real time. For example, the collection unit extracts and collects data from the subordinates' chat applications. The learning unit allows a generation AI to learn from the data collected by the collection unit. The generation AI learns the data using, for example, a text generation AI (e.g., LLM). The learning unit can also implement an algorithm for the generation AI to learn the data. For example, the generation AI learns from large amounts of text data and has advanced natural language processing capabilities. The summary creation unit allows the generation AI to summarize the subordinates' activities based on the data learned by the learning unit. The summary is created based on, for example, the length of the summary and the type of information included, but is not limited to such examples. For example, the generation AI summarizes the contents of subordinates' business emails and chats to create a summary. The summary creation unit may also implement an algorithm for the generation AI to create a summary. The reporting unit reports the summary created by the summary creation unit to a superior. The reporting unit provides the summary to the superior through, for example, a web application or a mobile application. The reporting unit may also send the summary to the superior by email. The question reception unit accepts questions from the superior based on the summary reported by the reporting unit. The question reception unit may, for example, provide an interface for the superior to input questions about the summary. The question reception unit may also send the superior's questions to the generation AI and receive answers. The advice provision unit allows the generation AI to provide advice based on the questions accepted by the question reception unit. The advice provision unit may, for example, implement an algorithm for the generation AI to generate appropriate advice in response to the superior's questions.The generation AI provides specific advice to the superior based on the content of the subordinate's business emails and chats. This allows the reporting system according to the embodiment to enable the superior to quickly grasp the progress and challenges of the subordinate's work. Some or all of the above-described processing in the advice providing unit may be performed using, for example, the generation AI. For example, the advice providing unit inputs the superior's question into the generation AI, which then generates an answer. Furthermore, the reporting system allows the superior to ask the generation AI questions and receive follow-up and advice for the subordinate. For example, when the superior asks the generation AI, "What's the next step in this project?", the generation AI advises the subordinate on the next step based on the content of the subordinate's emails and chats. This allows the superior to follow up with the subordinate even when they are busy, and also solves the problem of subordinates not having time to consult with their superiors.

[0030] The collection unit can analyze the subordinate's past work email or chat history and select an appropriate collection method. For example, the collection unit prioritizes collecting data from communication tools frequently used by the subordinate. The collection unit can also adjust the timing of data collection to match the subordinate's work hours. Furthermore, the collection unit can analyze the subordinate's past work patterns and select the most efficient collection method. In this way, the optimal data collection method can be selected by analyzing the subordinate's past work history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit inputs the subordinate's past work history data into the generation AI, which selects the optimal collection method.

[0031] When collecting data, the collection unit can filter the data based on the subordinate's current project or area of ​​interest. For example, the collection unit prioritizes collecting data related to the project the subordinate is currently working on. The collection unit can also filter and collect relevant data based on the subordinate's area of ​​interest. Furthermore, the collection unit can collect only necessary data depending on the subordinate's current work content. In this way, highly relevant data can be collected by filtering the data based on the subordinate's current project or area of ​​interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit inputs the subordinate's project data into the generation AI, and the generation AI performs the filtering.

[0032] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the subordinate. For example, when the subordinate is in the office, the collection unit can prioritize collecting office-related data. Furthermore, when the subordinate is on a business trip, the collection unit can also prioritize collecting data related to the business trip destination. Furthermore, when the subordinate is working remotely, the collection unit can also prioritize collecting data related to home or remote work. In this way, highly relevant data can be collected preferentially by taking into account the geographical location information of the subordinate. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit inputs the geographical location information of the subordinate into the generation AI, which then preferentially collects highly relevant data.

[0033] The collection unit can analyze the social media activities of subordinates and collect related data when collecting data. The collection unit, for example, collects work-related information shared by subordinates on social media. The collection unit can also collect data by extracting work-related topics from the social media activities of subordinates. Furthermore, the collection unit can collect data according to the time periods of the subordinates' social media activities. In this way, related data can be collected by analyzing the social media activities of subordinates. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI, for example. For example, the collection unit inputs the social media data of subordinates into the generation AI, which then collects the related data.

[0034] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. For example, the learning unit extracts effective learning patterns from past learning data and optimizes the algorithm. The learning unit can also adjust the parameters of the learning algorithm based on the past learning data. Furthermore, the learning unit can also improve learning efficiency by referring to past learning data. In this way, the learning algorithm can be optimized by referring to past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit inputs past learning data into the generation AI, and the generation AI optimizes the learning algorithm.

[0035] During learning, the learning unit can apply different learning algorithms depending on the work content of the subordinate. For example, if the subordinate is in charge of project management, the learning unit can apply a learning algorithm specialized for project management. Furthermore, if the subordinate is in charge of sales, the learning unit can also apply a learning algorithm specialized for sales. Furthermore, if the subordinate is in charge of technology development, the learning unit can also apply a learning algorithm specialized for technology development. In this way, by applying an appropriate learning algorithm depending on the work content of the subordinate, the effectiveness of learning is improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit inputs data on the work content of the subordinate into a generation AI, which then applies an appropriate learning algorithm.

[0036] During learning, the learning unit can weight the learning data based on the time of data submission. For example, the learning unit may weight the most recently submitted data during learning. The learning unit can also weight the data based on the time of submission while taking past data into consideration. Furthermore, the learning unit can also weight data submitted earlier and weight the most recent data during learning. In this way, weighting based on the time of data submission enables learning that emphasizes the most recent data. Some or all of the above-described processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit inputs the time of data submission into the generation AI, and the generation AI weights the learning data.

[0037] During learning, the learning unit can analyze the work performance of the subordinate and select learning data. For example, if the work performance of the subordinate is high, the learning unit will prioritize learning that data. Also, if the work performance of the subordinate is low, the learning unit can analyze and learn the data to find areas for improvement. Furthermore, the learning unit can select optimal learning data based on the work performance of the subordinate. In this way, optimal learning data can be selected by analyzing the work performance of the subordinate. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit inputs the work performance data of the subordinate into the generation AI, and the generation AI selects the learning data.

[0038] When creating a summary, the summary creation unit can adjust the level of detail of the summary based on the importance of the data. For example, the summary creation unit may describe data of high importance in detail and summarize data of low importance briefly. The summary creation unit may also prioritize including data of high importance in the summary. Furthermore, the summary creation unit can adjust the level of detail of the summary according to the importance. In this way, by adjusting the level of detail of the summary based on the importance of the data, important information can be conveyed preferentially. Some or all of the above-mentioned processing in the summary creation unit may be performed using, or without, a generation AI. For example, the summary creation unit inputs the importance of the data into the generation AI, and the generation AI adjusts the level of detail of the summary.

[0039] When creating summaries, the summary creation unit can apply different summary algorithms depending on the category of data. For example, the summary creation unit can apply a summary algorithm specialized for project management to data related to project management. The summary creation unit can also apply a summary algorithm specialized for sales to data related to sales. The summary creation unit can also apply a summary algorithm specialized for technology development to data related to technology development. This allows for the creation of more effective summaries by applying an appropriate summary algorithm depending on the data category. Some or all of the above-mentioned processing in the summary creation unit can be performed using, or without, a generation AI, for example. For example, the summary creation unit inputs the data category into the generation AI, which then applies an appropriate summary algorithm.

[0040] When creating summaries, the summary creation unit can determine the priority of summaries based on the time of data submission. For example, the summary creation unit can prioritize recently submitted data in the summary. The summary creation unit can also briefly summarize data that was submitted earlier and describe the most recent data in detail. Furthermore, the summary creation unit can also determine the priority of summaries based on the time of submission. In this way, by determining the priority of summaries based on the time of data submission, the most recent information can be conveyed preferentially. Some or all of the above-mentioned processing in the summary creation unit may be performed using, or without, a generation AI. For example, the summary creation unit inputs the time of data submission into the generation AI, and the generation AI determines the priority of summaries.

[0041] When creating a summary, the summary creation unit can adjust the order of summaries based on the relevance of the data. For example, the summary creation unit prioritizes highly relevant data and lists it at the beginning of the summary. The summary creation unit can also include less relevant data later in the summary. Furthermore, the summary creation unit can adjust the order of summaries based on the relevance of the data. In this way, by adjusting the order of summaries based on the relevance of the data, important information can be conveyed preferentially. Some or all of the above-mentioned processing in the summary creation unit may be performed using, or without, a generation AI. For example, the summary creation unit inputs the relevance of the data into the generation AI, and the generation AI adjusts the order of the summaries.

[0042] The reporting unit can adjust the level of detail of the report based on the importance of the summary when reporting. For example, the reporting unit reports summaries with high importance in detail and summaries with low importance in brief. The reporting unit can also prioritize reporting summaries with high importance. Furthermore, the reporting unit can adjust the level of detail of the report according to the importance. In this way, by adjusting the level of detail of the report based on the importance of the summary, important information can be conveyed preferentially. Some or all of the above-mentioned processing in the reporting unit may be performed using, or without, the generation AI. For example, the reporting unit inputs the importance of the summary into the generation AI, and the generation AI adjusts the level of detail of the report.

[0043] The reporting department can apply different reporting algorithms depending on the category of the summary when reporting. For example, the reporting department can apply a reporting algorithm specialized for project management to a summary related to project management. The reporting department can also apply a reporting algorithm specialized for sales to a summary related to sales. Furthermore, the reporting department can apply a reporting algorithm specialized for technology development to a summary related to technology development. This enables more effective reporting by applying an appropriate reporting algorithm depending on the category of the summary. Some or all of the above-mentioned processing in the reporting department can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reporting department inputs the category of the summary into the generation AI, which then applies an appropriate reporting algorithm.

[0044] The reporting unit can determine the priority of reports based on the time of submission of the summaries when reporting. For example, the reporting unit prioritizes the most recently submitted summaries. The reporting unit can also report older summaries briefly and the most recent summaries in detail. Furthermore, the reporting unit can also determine the priority of reports based on the time of submission. In this way, by determining the priority of reports based on the time of submission of summaries, the latest information can be conveyed preferentially. Some or all of the above-mentioned processing in the reporting unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reporting unit inputs the time of submission of summaries into the generation AI, and the generation AI determines the priority of reports.

[0045] The reporting unit can adjust the order of reports based on the relevance of summaries when reporting. For example, the reporting unit prioritizes highly relevant summaries and lists them at the beginning of the report. The reporting unit can also report less relevant summaries later. Furthermore, the reporting unit can adjust the order of reports based on the relevance of summaries. In this way, important information can be conveyed preferentially by adjusting the order of reports based on the relevance of summaries. Some or all of the above-mentioned processing in the reporting unit may be performed using, or without, a generation AI. For example, the reporting unit inputs the relevance of summaries into the generation AI, and the generation AI adjusts the order of reports.

[0046] When accepting a question, the question acceptance unit can select the optimal acceptance method by referring to the supervisor's past question history. For example, the question acceptance unit can preferentially provide question acceptance methods that the supervisor has frequently used in the past. The question acceptance unit can also suggest the optimal question acceptance method from the supervisor's past question history. Furthermore, the question acceptance unit can analyze the supervisor's past question history and select the most efficient question acceptance method. In this way, the optimal question acceptance method can be selected by referring to the supervisor's past question history. Some or all of the above-mentioned processing in the question acceptance unit may be performed using, or without, a generation AI. For example, the question acceptance unit inputs the supervisor's past question history data into the generation AI, and the generation AI selects the optimal question acceptance method.

[0047] When receiving a question, the question reception unit can apply different reception algorithms depending on the supervisor's job content. For example, if the supervisor is in charge of project management, the question reception unit can apply a question reception algorithm specialized for project management. Furthermore, if the supervisor is in charge of sales, the question reception unit can also apply a question reception algorithm specialized for sales. Furthermore, if the supervisor is in charge of technology development, the question reception unit can also apply a question reception algorithm specialized for technology development. This enables more effective question reception by applying an appropriate reception algorithm depending on the supervisor's job content. Some or all of the above-mentioned processing in the question reception unit may be performed using, or without, a generation AI, for example. For example, the question reception unit inputs the supervisor's job content data into a generation AI, which then applies an appropriate reception algorithm.

[0048] When receiving a question, the question receiving unit can prioritize receiving highly relevant questions by taking into account the geographical location information of the superior. For example, when the superior is in the office, the question receiving unit can prioritize receiving office-related questions. Furthermore, when the superior is on a business trip, the question receiving unit can also prioritize receiving questions related to the business trip destination. Furthermore, when the superior is working remotely, the question receiving unit can also prioritize receiving questions related to home or remote work. In this way, by taking the superior's geographical location information into account, highly relevant questions can be prioritized. Some or all of the above-described processing in the question receiving unit may be performed using, or without, a generation AI. For example, the question receiving unit inputs the superior's geographical location information into the generation AI, which then prioritizes receiving highly relevant questions.

[0049] The question reception unit can analyze the supervisor's social media activity when receiving a question and receive related questions. The question reception unit, for example, receives questions based on work-related information shared by the supervisor on social media. The question reception unit can also extract work-related topics from the supervisor's social media activity and receive questions. Furthermore, the question reception unit can also receive questions based on the time period during which the supervisor is active on social media. In this way, related questions can be received by analyzing the supervisor's social media activity. Some or all of the above-described processing in the question reception unit may be performed using, or without, a generation AI. For example, the question reception unit inputs the supervisor's social media data into a generation AI, which then receives related questions.

[0050] When providing advice, the advice providing unit can adjust the level of detail of the advice based on the importance of the question. For example, the advice providing unit provides detailed advice for questions of high importance and provides concise advice for questions of low importance. The advice providing unit can also provide advice by prioritizing questions of high importance. Furthermore, the advice providing unit can adjust the level of detail of the advice according to the importance. In this way, by adjusting the level of detail of the advice based on the importance of the question, detailed advice can be provided for important questions. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without, a generation AI, for example. For example, the advice providing unit inputs the importance of the question to the generation AI, and the generation AI adjusts the level of detail of the advice.

[0051] When providing advice, the advice providing unit can apply different advice algorithms depending on the category of the question. For example, the advice providing unit applies an advice algorithm specialized for project management to a question about project management. The advice providing unit can also apply an advice algorithm specialized for sales to a question about sales. Furthermore, the advice providing unit can also apply an advice algorithm specialized for technology development to a question about technology development. This enables more effective advice by applying an appropriate advice algorithm depending on the category of the question. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without, a generation AI, for example. For example, the advice providing unit inputs the category of the question into the generation AI, which then applies an appropriate advice algorithm.

[0052] When providing advice, the advice providing unit can determine the priority of advice based on the time of submission of the question. For example, the advice providing unit can provide advice by prioritizing the most recently submitted question. The advice providing unit can also provide brief advice for older questions and detailed advice for the most recent questions. Furthermore, the advice providing unit can also determine the priority of advice based on the time of submission. In this way, by determining the priority of advice based on the time of submission of the question, advice can be quickly provided for the most recent questions. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without using, the generation AI, for example. For example, the advice providing unit inputs the time of submission of the question to the generation AI, and the generation AI determines the priority of advice.

[0053] When providing advice, the advice providing unit can adjust the order of advice based on the relevance of the questions. For example, the advice providing unit prioritizes highly relevant questions and lists them at the beginning of the advice. The advice providing unit can also provide advice for less relevant questions later. Furthermore, the advice providing unit can adjust the order of advice based on the relevance of the questions. In this way, by adjusting the order of advice based on the relevance of the questions, advice can be provided preferentially for important questions. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without using, a generation AI. For example, the advice providing unit inputs the relevance of questions to the generation AI, and the generation AI adjusts the order of advice.

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

[0055] The reporting system can also be equipped with a performance monitoring unit that monitors the work performance of subordinates in real time. The performance monitoring unit evaluates the work progress and quality of the deliverables of subordinates and provides this information to the unit. For example, when a subordinate reports the progress of a project, the performance monitoring unit analyzes the report in real time and issues an alert if progress is behind schedule. In addition, if the quality of the subordinate's deliverables is low, the performance monitoring unit can provide specific advice for quality improvement. Furthermore, the performance monitoring unit can regularly evaluate the work performance of subordinates and report the results to their superiors. This allows superiors to understand their subordinates' work performance in real time and provide appropriate follow-up and guidance.

[0056] The learning unit can analyze the subordinate's past work history and select optimal learning data. For example, it can prioritize learning data from projects where the subordinate was successful in the past and reproduce similar success patterns. It can also analyze data from projects where the subordinate failed in the past to identify the cause of the failure and learn improvement measures. Furthermore, it can select learning data to strengthen specific skills and knowledge based on the subordinate's work history. This enables more effective learning by utilizing the subordinate's past work history. Some or all of the above-mentioned processing in the learning unit may be performed using or without the generation AI. For example, the learning unit inputs the subordinate's work history data into the generation AI, which selects optimal learning data.

[0057] The question reception unit can analyze the supervisor's past question history and select the optimal question reception method. For example, it can prioritize the question reception method that the supervisor has frequently used in the past. It can also suggest the optimal question reception method based on the supervisor's past question history. It can also analyze the supervisor's past question history and select the most efficient question reception method. In this way, the optimal question reception method can be selected by referring to the supervisor's past question history. Some or all of the above-mentioned processing in the question reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the question reception unit inputs the supervisor's past question history data into the generation AI, and the generation AI selects the optimal question reception method.

[0058] The reporting system can further include an evaluation section that evaluates the work performance of subordinates. The evaluation section periodically evaluates the work performance of subordinates and reports the results to their superiors. For example, if a subordinate's work performance is high, the evaluation section can report the results to the superior to increase the subordinate's motivation. Also, if a subordinate's work performance is low, the evaluation section can identify areas for improvement and propose specific improvement measures to the superior. Furthermore, the evaluation section can propose training programs to help improve the subordinate's skills and knowledge based on the subordinate's work performance. This allows the superior to understand the subordinate's work performance and provide appropriate follow-up and guidance.

[0059] The reporting system can further include a prediction unit that predicts the work performance of subordinates. The prediction unit analyzes the subordinates' past work data and predicts their future work performance. For example, the prediction unit predicts the probability of success of future projects based on data on projects that the subordinates have succeeded in the past. It can also analyze data on projects that the subordinates have failed in the past and predict the risk of failure. Furthermore, the prediction unit can predict future workloads based on the subordinates' work performance and propose appropriate resource allocation. This allows managers to predict the future work performance of their subordinates and provide appropriate follow-up and guidance.

[0060] The reporting system can further include a visualization unit that visualizes the work performance of subordinates. The visualization unit displays the work performance of subordinates in visual formats such as graphs and charts, making it easy for supervisors to understand. For example, the work progress of subordinates can be displayed in a Gantt chart, allowing the progress of a project to be grasped at a glance. The work results of subordinates can also be displayed in bar graphs or pie charts, visually showing the distribution and percentage of results. Furthermore, the visualization unit can display the work performance of subordinates in chronological order, allowing the supervisor to grasp the fluctuations in work performance from the past to the present. This allows the supervisor to visually grasp the work performance of subordinates and provide appropriate follow-up and guidance.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The collection department collects data from subordinates' business emails or chats. The data from subordinates' business emails or chats includes text data, attachments, image data, etc. The collection department can also build a system to automatically collect subordinates' business emails and collect subordinates' chat data in real time. For example, the collection department extracts and collects data from subordinates' chat applications. Step 2: The learning unit trains the generation AI on the data collected by the collection unit. The generation AI learns the data using text generation AI (e.g., LLM) and has advanced natural language processing capabilities. The learning unit can also implement algorithms for the generation AI to learn the data. Step 3: In the summary creation section, the generation AI creates a summary of the subordinate's activities based on the data learned by the learning section. The summary is created based on the length of the summary and the type of information it contains. The generation AI can also implement an algorithm to summarize the content of the subordinate's work emails and chats and create a summary. Step 4: The reporting department reports the summary created by the summary creation department to the superior. The reporting department can provide the summary to the superior through a web application or a mobile application, or can send the summary to the superior by email. Step 5: The question reception unit receives questions from the superior based on the summary reported by the reporting unit. The question reception unit provides an interface for the superior to input questions about the summary, and can also send the superior's questions to the generation AI and receive answers. Step 6: The advice providing unit uses the generation AI to provide advice based on the questions received by the question receiving unit. The generation AI can also implement an algorithm to provide specific advice to superiors based on the content of subordinates' business emails and chats.

[0063] (Example 2) A reporting system according to an embodiment of the present invention has a generation AI that learns from subordinates' work emails and chats, and then summarizes the subordinates' activities and reports them to their superiors. This reporting system collects data on subordinates' work emails and chats, and the generation AI learns from this data and summarizes the subordinates' activities. This summary is then reported to the superior. By reviewing this summary, the superior can understand the subordinates' work progress and challenges. The superior can also ask the generation AI questions to receive follow-up and advice. For example, if a subordinate reports project progress via email, the generation AI learns from the email and summarizes the project progress. By reviewing this summary, the superior can quickly understand the project progress. Furthermore, when a superior asks the generation AI, "What is the next step in this project?", the generation AI advises on the next step based on the content of the subordinates' emails and chats. This system allows superiors to follow up on their subordinates even when they are busy, and it also solves the problem of subordinates not having time to consult with their superiors. Furthermore, the generation AI summarizes the activities of subordinates, allowing superiors to quickly grasp the progress and issues of their subordinates' work, thereby improving work efficiency.This reporting system allows superiors to quickly grasp the progress and issues of their subordinates' work.

[0064] A reporting system according to an embodiment includes a collection unit, a learning unit, a summary creation unit, a reporting unit, a question receiving unit, and an advice providing unit. The collection unit collects data on subordinates' business emails or chats. The data on subordinates' business emails or chats includes, but is not limited to, text data, attachments, and image data. The collection unit, for example, builds a system that automatically collects subordinates' business emails. The collection unit can also collect chat data on subordinates in real time. For example, the collection unit extracts and collects data from the subordinates' chat applications. The learning unit allows a generation AI to learn from the data collected by the collection unit. The generation AI learns the data using, for example, a text generation AI (e.g., LLM). The learning unit can also implement an algorithm for the generation AI to learn the data. For example, the generation AI learns from large amounts of text data and has advanced natural language processing capabilities. The summary creation unit allows the generation AI to summarize the subordinates' activities based on the data learned by the learning unit. The summary is created based on, for example, the length of the summary and the type of information included, but is not limited to such examples. For example, the generation AI summarizes the contents of subordinates' business emails and chats to create a summary. The summary creation unit may also implement an algorithm for the generation AI to create a summary. The reporting unit reports the summary created by the summary creation unit to a superior. The reporting unit provides the summary to the superior through, for example, a web application or a mobile application. The reporting unit may also send the summary to the superior by email. The question reception unit accepts questions from the superior based on the summary reported by the reporting unit. The question reception unit may, for example, provide an interface for the superior to input questions about the summary. The question reception unit may also send the superior's questions to the generation AI and receive answers. The advice provision unit allows the generation AI to provide advice based on the questions accepted by the question reception unit. The advice provision unit may, for example, implement an algorithm for the generation AI to generate appropriate advice in response to the superior's questions.The generation AI provides specific advice to the superior based on the content of the subordinate's business emails and chats. This allows the reporting system according to the embodiment to enable the superior to quickly grasp the progress and challenges of the subordinate's work. Some or all of the above-described processing in the advice providing unit may be performed using, for example, the generation AI. For example, the advice providing unit inputs the superior's question into the generation AI, which then generates an answer. Furthermore, the reporting system allows the superior to ask the generation AI questions and receive follow-up and advice for the subordinate. For example, when the superior asks the generation AI, "What's the next step in this project?", the generation AI advises the subordinate on the next step based on the content of the subordinate's emails and chats. This allows the superior to follow up with the subordinate even when they are busy, and also solves the problem of subordinates not having time to consult with their superiors.

[0065] The collection unit can estimate the emotions of the subordinate and adjust the timing of data collection based on the estimated emotions of the subordinate. For example, if the subordinate is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the workload. Furthermore, if the subordinate is relaxed, the collection unit can also collect data at the normal collection timing. Furthermore, if the subordinate is busy, the collection unit can collect data by avoiding peak work hours. This allows for more appropriate data collection by adjusting the timing of data collection according to the subordinate's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the collection unit inputs the subordinate's emotion data into the generation AI, and the generation AI adjusts the timing of data collection.

[0066] The collection unit can analyze the subordinate's past work email or chat history and select an appropriate collection method. For example, the collection unit prioritizes collecting data from communication tools frequently used by the subordinate. The collection unit can also adjust the timing of data collection to match the subordinate's work hours. Furthermore, the collection unit can analyze the subordinate's past work patterns and select the most efficient collection method. In this way, the optimal data collection method can be selected by analyzing the subordinate's past work history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit inputs the subordinate's past work history data into the generation AI, which selects the optimal collection method.

[0067] When collecting data, the collection unit can filter the data based on the subordinate's current project or area of ​​interest. For example, the collection unit prioritizes collecting data related to the project the subordinate is currently working on. The collection unit can also filter and collect relevant data based on the subordinate's area of ​​interest. Furthermore, the collection unit can collect only necessary data depending on the subordinate's current work content. In this way, highly relevant data can be collected by filtering the data based on the subordinate's current project or area of ​​interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit inputs the subordinate's project data into the generation AI, and the generation AI performs the filtering.

[0068] The collection unit can estimate the emotions of the subordinates and determine the priority of data to be collected based on the estimated emotions of the subordinates. For example, if the subordinates are feeling stressed, the collection unit can prioritize collecting data of high importance. Furthermore, if the subordinates are relaxed, the collection unit can also collect data with normal priority. Furthermore, if the subordinates are busy, the collection unit can prioritize collecting data of high urgency. Thus, by determining the priority of data according to the emotions of the subordinates, important data can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the collection unit inputs the emotion data of the subordinates into the generation AI, and the generation AI determines the priority of the data.

[0069] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the subordinate. For example, when the subordinate is in the office, the collection unit can prioritize collecting office-related data. Furthermore, when the subordinate is on a business trip, the collection unit can also prioritize collecting data related to the business trip destination. Furthermore, when the subordinate is working remotely, the collection unit can also prioritize collecting data related to home or remote work. In this way, highly relevant data can be collected preferentially by taking into account the geographical location information of the subordinate. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit inputs the geographical location information of the subordinate into the generation AI, which then preferentially collects highly relevant data.

[0070] The collection unit can analyze the social media activities of subordinates and collect related data when collecting data. The collection unit, for example, collects work-related information shared by subordinates on social media. The collection unit can also collect data by extracting work-related topics from the social media activities of subordinates. Furthermore, the collection unit can collect data according to the time periods of the subordinates' social media activities. In this way, related data can be collected by analyzing the social media activities of subordinates. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI, for example. For example, the collection unit inputs the social media data of subordinates into the generation AI, which then collects the related data.

[0071] The learning unit can estimate the emotions of the subordinate and select learning data based on the estimated emotions of the subordinate. For example, if the subordinate is feeling stressed, the learning unit prioritizes learning data that is useful for stress reduction. Furthermore, if the subordinate is relaxed, the learning unit can also use regular learning data. Furthermore, if the subordinate is busy, the learning unit can prioritize learning data of high importance. This enables more effective learning by selecting learning data according to the subordinate's emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI 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 learning unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the learning unit inputs the subordinate's emotion data into the generation AI, which then selects the learning data.

[0072] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. For example, the learning unit extracts effective learning patterns from past learning data and optimizes the algorithm. The learning unit can also adjust the parameters of the learning algorithm based on the past learning data. Furthermore, the learning unit can also improve learning efficiency by referring to past learning data. In this way, the learning algorithm can be optimized by referring to past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit inputs past learning data into the generation AI, and the generation AI optimizes the learning algorithm.

[0073] During learning, the learning unit can apply different learning algorithms depending on the work content of the subordinate. For example, if the subordinate is in charge of project management, the learning unit can apply a learning algorithm specialized for project management. Furthermore, if the subordinate is in charge of sales, the learning unit can also apply a learning algorithm specialized for sales. Furthermore, if the subordinate is in charge of technology development, the learning unit can also apply a learning algorithm specialized for technology development. In this way, by applying an appropriate learning algorithm depending on the work content of the subordinate, the effectiveness of learning is improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit inputs data on the work content of the subordinate into a generation AI, which then applies an appropriate learning algorithm.

[0074] The learning unit can estimate the emotions of the subordinate and adjust the frequency of learning based on the estimated emotions of the subordinate. For example, if the subordinate is feeling stressed, the learning unit reduces the frequency of learning to reduce the burden. The learning unit can also maintain a normal frequency of learning if the subordinate is relaxed. Furthermore, if the subordinate is busy, the learning unit can adjust the frequency of learning to reduce the work burden. This allows for effective learning while reducing the burden by adjusting the frequency of learning according to the subordinate'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 these examples. Some or all of the above-mentioned processing in the learning unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the learning unit inputs the subordinate's emotion data into the generation AI, and the generation AI adjusts the frequency of learning.

[0075] During learning, the learning unit can weight the learning data based on the time of data submission. For example, the learning unit may weight the most recently submitted data during learning. The learning unit can also weight the data based on the time of submission while taking past data into consideration. Furthermore, the learning unit can also weight data submitted earlier and weight the most recent data during learning. In this way, weighting based on the time of data submission enables learning that emphasizes the most recent data. Some or all of the above-described processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit inputs the time of data submission into the generation AI, and the generation AI weights the learning data.

[0076] During learning, the learning unit can analyze the work performance of the subordinate and select learning data. For example, if the work performance of the subordinate is high, the learning unit will prioritize learning that data. Also, if the work performance of the subordinate is low, the learning unit can analyze and learn the data to find areas for improvement. Furthermore, the learning unit can select optimal learning data based on the work performance of the subordinate. In this way, optimal learning data can be selected by analyzing the work performance of the subordinate. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit inputs the work performance data of the subordinate into the generation AI, and the generation AI selects the learning data.

[0077] The summary creation unit can estimate the subordinate's emotions and adjust the summary expression method based on the estimated emotions. For example, if the subordinate is feeling stressed, the summary creation unit can create a concise summary that focuses on the main points. Furthermore, if the subordinate is relaxed, the summary creation unit can also create a summary that includes detailed information. Furthermore, if the subordinate is busy, the summary creation unit can prioritize important information in the summary. This allows for the creation of a more appropriate summary by adjusting the summary expression method according to the subordinate's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the summary creation unit can be performed using, for example, the generation AI. For example, the summary creation unit inputs the subordinate's emotion data into the generation AI, which then adjusts the summary expression method.

[0078] When creating a summary, the summary creation unit can adjust the level of detail of the summary based on the importance of the data. For example, the summary creation unit may describe data of high importance in detail and summarize data of low importance briefly. The summary creation unit may also prioritize including data of high importance in the summary. Furthermore, the summary creation unit can adjust the level of detail of the summary according to the importance. In this way, by adjusting the level of detail of the summary based on the importance of the data, important information can be conveyed preferentially. Some or all of the above-mentioned processing in the summary creation unit may be performed using, or without, a generation AI. For example, the summary creation unit inputs the importance of the data into the generation AI, and the generation AI adjusts the level of detail of the summary.

[0079] When creating summaries, the summary creation unit can apply different summary algorithms depending on the category of data. For example, the summary creation unit can apply a summary algorithm specialized for project management to data related to project management. The summary creation unit can also apply a summary algorithm specialized for sales to data related to sales. The summary creation unit can also apply a summary algorithm specialized for technology development to data related to technology development. This allows for the creation of more effective summaries by applying an appropriate summary algorithm depending on the data category. Some or all of the above-mentioned processing in the summary creation unit can be performed using, or without, a generation AI, for example. For example, the summary creation unit inputs the data category into the generation AI, which then applies an appropriate summary algorithm.

[0080] The summary creation unit can estimate the subordinate's emotions and adjust the length of the summary based on the estimated emotions. For example, if the subordinate is feeling stressed, the summary creation unit can create a short summary that covers the main points. Furthermore, if the subordinate is relaxed, the summary creation unit can create a longer summary that includes detailed information. Furthermore, if the subordinate is busy, the summary creation unit can prioritize important information and create a shorter summary. This allows for the creation of a more appropriate summary by adjusting the length of the summary based on the subordinate's emotions. The emotion estimation is achieved using an emotion estimation function, such as 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 summary creation unit can be performed using, for example, the generation AI. For example, the summary creation unit inputs the subordinate's emotion data into the generation AI, which then adjusts the length of the summary.

[0081] When creating summaries, the summary creation unit can determine the priority of summaries based on the time of data submission. For example, the summary creation unit can prioritize recently submitted data in the summary. The summary creation unit can also briefly summarize data that was submitted earlier and describe the most recent data in detail. Furthermore, the summary creation unit can also determine the priority of summaries based on the time of submission. In this way, by determining the priority of summaries based on the time of data submission, the most recent information can be conveyed preferentially. Some or all of the above-mentioned processing in the summary creation unit may be performed using, or without, a generation AI. For example, the summary creation unit inputs the time of data submission into the generation AI, and the generation AI determines the priority of summaries.

[0082] When creating a summary, the summary creation unit can adjust the order of summaries based on the relevance of the data. For example, the summary creation unit prioritizes highly relevant data and lists it at the beginning of the summary. The summary creation unit can also include less relevant data later in the summary. Furthermore, the summary creation unit can adjust the order of summaries based on the relevance of the data. In this way, by adjusting the order of summaries based on the relevance of the data, important information can be conveyed preferentially. Some or all of the above-mentioned processing in the summary creation unit may be performed using, or without, a generation AI. For example, the summary creation unit inputs the relevance of the data into the generation AI, and the generation AI adjusts the order of the summaries.

[0083] The reporting unit can estimate the boss's emotions and adjust the way the report is presented based on the estimated boss' emotions. For example, if the boss is stressed, the reporting unit can provide a concise and to-the-point report. Furthermore, if the boss is relaxed, the reporting unit can provide a report that includes detailed information. Furthermore, if the boss is busy, the reporting unit can prioritize important information in the report. This allows for more appropriate reporting by adjusting the way the report is presented based on the boss' emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reporting unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reporting unit inputs the boss's emotion data into the generation AI, which then adjusts the way the report is presented.

[0084] The reporting unit can adjust the level of detail of the report based on the importance of the summary when reporting. For example, the reporting unit reports summaries with high importance in detail and summaries with low importance in brief. The reporting unit can also prioritize reporting summaries with high importance. Furthermore, the reporting unit can adjust the level of detail of the report according to the importance. In this way, by adjusting the level of detail of the report based on the importance of the summary, important information can be conveyed preferentially. Some or all of the above-mentioned processing in the reporting unit may be performed using, or without, the generation AI. For example, the reporting unit inputs the importance of the summary into the generation AI, and the generation AI adjusts the level of detail of the report.

[0085] The reporting department can apply different reporting algorithms depending on the category of the summary when reporting. For example, the reporting department can apply a reporting algorithm specialized for project management to a summary related to project management. The reporting department can also apply a reporting algorithm specialized for sales to a summary related to sales. Furthermore, the reporting department can apply a reporting algorithm specialized for technology development to a summary related to technology development. This enables more effective reporting by applying an appropriate reporting algorithm depending on the category of the summary. Some or all of the above-mentioned processing in the reporting department can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reporting department inputs the category of the summary into the generation AI, which then applies an appropriate reporting algorithm.

[0086] The reporting unit can estimate the boss's emotions and adjust the length of the report based on the estimated boss' emotions. For example, if the boss is stressed, the reporting unit can provide a short, to-the-point report. Furthermore, if the boss is relaxed, the reporting unit can provide a longer report with detailed information. Furthermore, if the boss is busy, the reporting unit can prioritize important information and provide a shorter report. This allows for more appropriate reporting by adjusting the length of the report according to the boss' emotions. The 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 these examples. Some or all of the above-described processing in the reporting unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reporting unit inputs the boss's emotion data into the generation AI, which then adjusts the length of the report.

[0087] The reporting unit can determine the priority of reports based on the time of submission of the summaries when reporting. For example, the reporting unit prioritizes the most recently submitted summaries. The reporting unit can also report older summaries briefly and the most recent summaries in detail. Furthermore, the reporting unit can also determine the priority of reports based on the time of submission. In this way, by determining the priority of reports based on the time of submission of summaries, the latest information can be conveyed preferentially. Some or all of the above-mentioned processing in the reporting unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reporting unit inputs the time of submission of summaries into the generation AI, and the generation AI determines the priority of reports.

[0088] The reporting unit can adjust the order of reports based on the relevance of summaries when reporting. For example, the reporting unit prioritizes highly relevant summaries and lists them at the beginning of the report. The reporting unit can also report less relevant summaries later. Furthermore, the reporting unit can adjust the order of reports based on the relevance of summaries. In this way, important information can be conveyed preferentially by adjusting the order of reports based on the relevance of summaries. Some or all of the above-mentioned processing in the reporting unit may be performed using, or without, a generation AI. For example, the reporting unit inputs the relevance of summaries into the generation AI, and the generation AI adjusts the order of reports.

[0089] The question reception unit can estimate the boss's emotions and adjust the question reception method based on the estimated boss' emotions. For example, if the boss is stressed, the question reception unit can provide a simple and quick question reception method. Furthermore, if the boss is relaxed, the question reception unit can also provide detailed question reception options. Furthermore, if the boss is busy, the question reception unit can prioritize voice input and quickly accept questions. This allows for more appropriate question reception by adjusting the question reception method according to the boss' emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the question reception unit can be performed using, for example, the generation AI. For example, the question reception unit inputs the boss's emotion data into the generation AI, and the generation AI adjusts the question reception method.

[0090] When accepting a question, the question acceptance unit can select the optimal acceptance method by referring to the supervisor's past question history. For example, the question acceptance unit can preferentially provide question acceptance methods that the supervisor has frequently used in the past. The question acceptance unit can also suggest the optimal question acceptance method from the supervisor's past question history. Furthermore, the question acceptance unit can analyze the supervisor's past question history and select the most efficient question acceptance method. In this way, the optimal question acceptance method can be selected by referring to the supervisor's past question history. Some or all of the above-mentioned processing in the question acceptance unit may be performed using, or without, a generation AI. For example, the question acceptance unit inputs the supervisor's past question history data into the generation AI, and the generation AI selects the optimal question acceptance method.

[0091] When receiving a question, the question reception unit can apply different reception algorithms depending on the supervisor's job content. For example, if the supervisor is in charge of project management, the question reception unit can apply a question reception algorithm specialized for project management. Furthermore, if the supervisor is in charge of sales, the question reception unit can also apply a question reception algorithm specialized for sales. Furthermore, if the supervisor is in charge of technology development, the question reception unit can also apply a question reception algorithm specialized for technology development. This enables more effective question reception by applying an appropriate reception algorithm depending on the supervisor's job content. Some or all of the above-mentioned processing in the question reception unit may be performed using, or without, a generation AI, for example. For example, the question reception unit inputs the supervisor's job content data into a generation AI, which then applies an appropriate reception algorithm.

[0092] The question reception unit can estimate the boss's emotions and determine the priority of questions based on the estimated boss' emotions. For example, if the boss is stressed, the question reception unit can prioritize questions with high importance. Furthermore, if the boss is relaxed, the question reception unit can also prioritize questions with normal priority. Furthermore, if the boss is busy, the question reception unit can prioritize questions with high urgency. Thus, by determining the priority of questions according to the boss' emotions, important questions can be prioritized. The emotion estimation is realized 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 question reception unit can be performed using, for example, the generation AI. For example, the question reception unit inputs the boss's emotion data into the generation AI, which then determines the priority of questions.

[0093] When receiving a question, the question receiving unit can prioritize receiving highly relevant questions by taking into account the geographical location information of the superior. For example, when the superior is in the office, the question receiving unit can prioritize receiving office-related questions. Furthermore, when the superior is on a business trip, the question receiving unit can also prioritize receiving questions related to the business trip destination. Furthermore, when the superior is working remotely, the question receiving unit can also prioritize receiving questions related to home or remote work. In this way, by taking the superior's geographical location information into account, highly relevant questions can be prioritized. Some or all of the above-described processing in the question receiving unit may be performed using, or without, a generation AI. For example, the question receiving unit inputs the superior's geographical location information into the generation AI, which then prioritizes receiving highly relevant questions.

[0094] The question reception unit can analyze the supervisor's social media activity when receiving a question and receive related questions. The question reception unit, for example, receives questions based on work-related information shared by the supervisor on social media. The question reception unit can also extract work-related topics from the supervisor's social media activity and receive questions. Furthermore, the question reception unit can also receive questions based on the time period during which the supervisor is active on social media. In this way, related questions can be received by analyzing the supervisor's social media activity. Some or all of the above-described processing in the question reception unit may be performed using, or without, a generation AI. For example, the question reception unit inputs the supervisor's social media data into a generation AI, which then receives related questions.

[0095] The advice providing unit can estimate the boss's emotions and adjust the way the advice is expressed based on the estimated boss' emotions. For example, if the boss is feeling stressed, the advice providing unit can provide concise and to-the-point advice. Furthermore, if the boss is relaxed, the advice providing unit can also provide advice including detailed information. Furthermore, if the boss is busy, the advice providing unit can prioritize important information when providing advice. This allows for more appropriate advice by adjusting the way the advice is expressed based on the boss' emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the advice providing unit can be performed using, for example, the generation AI. For example, the advice providing unit inputs the boss's emotion data into the generation AI, which then adjusts the way the advice is expressed.

[0096] When providing advice, the advice providing unit can adjust the level of detail of the advice based on the importance of the question. For example, the advice providing unit provides detailed advice for questions of high importance and provides concise advice for questions of low importance. The advice providing unit can also provide advice by prioritizing questions of high importance. Furthermore, the advice providing unit can adjust the level of detail of the advice according to the importance. In this way, by adjusting the level of detail of the advice based on the importance of the question, detailed advice can be provided for important questions. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without, a generation AI, for example. For example, the advice providing unit inputs the importance of the question to the generation AI, and the generation AI adjusts the level of detail of the advice.

[0097] When providing advice, the advice providing unit can apply different advice algorithms depending on the category of the question. For example, the advice providing unit applies an advice algorithm specialized for project management to a question about project management. The advice providing unit can also apply an advice algorithm specialized for sales to a question about sales. Furthermore, the advice providing unit can also apply an advice algorithm specialized for technology development to a question about technology development. This enables more effective advice by applying an appropriate advice algorithm depending on the category of the question. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without, a generation AI, for example. For example, the advice providing unit inputs the category of the question into the generation AI, which then applies an appropriate advice algorithm.

[0098] The advice providing unit can estimate the boss's emotions and adjust the length of the advice based on the estimated boss' emotions. For example, if the boss is stressed, the advice providing unit can provide short, to-the-point advice. Furthermore, if the boss is relaxed, the advice providing unit can provide longer advice including detailed information. Furthermore, if the boss is busy, the advice providing unit can prioritize important information and provide shorter advice. This allows for more appropriate advice by adjusting the length of the advice according to the boss' emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the advice providing unit can be performed using, for example, the generation AI. For example, the advice providing unit inputs the boss's emotion data into the generation AI, and the generation AI adjusts the length of the advice.

[0099] When providing advice, the advice providing unit can determine the priority of advice based on the time of submission of the question. For example, the advice providing unit can provide advice by prioritizing the most recently submitted question. The advice providing unit can also provide brief advice for older questions and detailed advice for the most recent questions. Furthermore, the advice providing unit can also determine the priority of advice based on the time of submission. In this way, by determining the priority of advice based on the time of submission of the question, advice can be quickly provided for the most recent questions. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without using, the generation AI, for example. For example, the advice providing unit inputs the time of submission of the question to the generation AI, and the generation AI determines the priority of advice.

[0100] When providing advice, the advice providing unit can adjust the order of advice based on the relevance of the questions. For example, the advice providing unit prioritizes highly relevant questions and lists them at the beginning of the advice. The advice providing unit can also provide advice for less relevant questions later. Furthermore, the advice providing unit can adjust the order of advice based on the relevance of the questions. In this way, by adjusting the order of advice based on the relevance of the questions, advice can be provided preferentially for important questions. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without using, a generation AI. For example, the advice providing unit inputs the relevance of questions to the generation AI, and the generation AI adjusts the order of advice. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, learning unit, summary creation unit, reporting unit, question receiving unit, and advice providing 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 business emails and chat data of subordinates via the control unit 46A of the smart device 14. The learning unit causes the generation AI to learn the data collected by the specific processing unit 290 of the data processing device 12. The summary creation unit causes the generation AI to summarize the activities of the subordinates based on the data learned by the specific processing unit 290 of the data processing device 12. The reporting unit reports the summary to the superior via the control unit 46A of the smart device 14. The question receiving unit receives questions from the superior via the control unit 46A of the smart device 14. The advice providing unit causes the generation AI to provide advice in response to the superior's questions via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, learning unit, summary creation unit, reporting unit, question receiving unit, and advice providing 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 business emails and chat data of subordinates via the control unit 46A of the smart glasses 214. The learning unit causes the generation AI to learn data collected by the specific processing unit 290 of the data processing device 12. The summary creation unit causes the generation AI to summarize the activities of the subordinates based on the data learned by the specific processing unit 290 of the data processing device 12. The reporting unit reports the summary to the superior via the control unit 46A of the smart glasses 214. The question receiving unit receives questions from the superior via the control unit 46A of the smart glasses 214. The advice providing unit causes the generation AI to provide advice in response to the superior's questions via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, learning unit, summary creation unit, reporting unit, question receiving unit, and advice providing 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 business emails and chat data of subordinates via the control unit 46A of the headset type terminal 314. The learning unit causes the generation AI to learn data collected by the specific processing unit 290 of the data processing device 12. The summary creation unit causes the generation AI to summarize the activities of the subordinates based on the data learned by the specific processing unit 290 of the data processing device 12. The reporting unit reports the summary to the superior via the control unit 46A of the headset type terminal 314. The question receiving unit receives questions from the superior via the control unit 46A of the headset type terminal 314. The advice providing unit causes the generation AI to provide advice in response to the superior's questions via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, learning unit, summary creation unit, reporting unit, question receiving unit, and advice providing 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 business emails and chat data of subordinates via the control unit 46A of the robot 414. The learning unit causes the generation AI to learn the data collected by the specific processing unit 290 of the data processing device 12. The summary creation unit causes the generation AI to summarize the activities of the subordinates based on the data learned by the specific processing unit 290 of the data processing device 12. The reporting unit reports the summary to the superior via the control unit 46A of the robot 414. The question receiving unit receives questions from the superior via the control unit 46A of the robot 414. The advice providing unit causes the generation AI to provide advice in response to the superior's question via the specific processing unit 290 of the data processing device 12.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The reporting system can also be equipped with a performance monitoring unit that monitors the work performance of subordinates in real time. The performance monitoring unit evaluates the work progress and quality of the deliverables of subordinates and provides this information to the unit. For example, when a subordinate reports the progress of a project, the performance monitoring unit analyzes the report in real time and issues an alert if progress is behind schedule. In addition, if the quality of the subordinate's deliverables is low, the performance monitoring unit can provide specific advice for quality improvement. Furthermore, the performance monitoring unit can regularly evaluate the work performance of subordinates and report the results to their superiors. This allows superiors to understand their subordinates' work performance in real time and provide appropriate follow-up and guidance.

[0103] The collection unit can estimate the emotions of subordinates and adjust the data collection method based on the estimated emotions of the subordinates. For example, if a subordinate is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the burden on the subordinate. Also, if the subordinate is relaxed, the collection unit can collect data using the normal collection method. Furthermore, if the subordinate is busy, the collection unit can collect data by avoiding peak work hours. This allows for more appropriate data collection by adjusting the data collection method according to the subordinates' emotions. Emotion estimation is achieved using an emotion engine or a generation AI. For example, the collection unit inputs the subordinates' emotional data into a generation AI, which then adjusts the data collection method.

[0104] The learning unit can analyze the subordinate's past work history and select optimal learning data. For example, it can prioritize learning data from projects where the subordinate was successful in the past and reproduce similar success patterns. It can also analyze data from projects where the subordinate failed in the past to identify the cause of the failure and learn improvement measures. Furthermore, it can select learning data to strengthen specific skills and knowledge based on the subordinate's work history. This enables more effective learning by utilizing the subordinate's past work history. Some or all of the above-mentioned processing in the learning unit may be performed using or without the generation AI. For example, the learning unit inputs the subordinate's work history data into the generation AI, which selects optimal learning data.

[0105] The summary creation unit can estimate the emotions of a subordinate and adjust the way the summary is presented based on the estimated emotions of the subordinate. For example, if the subordinate is feeling stressed, it can create a concise summary that covers the main points. Alternatively, if the subordinate is relaxed, it can create a summary that includes detailed information. Furthermore, if the subordinate is busy, it can prioritize the inclusion of important information in the summary. This allows for the creation of a more appropriate summary by adjusting the way the summary is presented according to the subordinate's emotions. Emotion estimation is achieved using an emotion engine or a generation AI. For example, the summary creation unit inputs the subordinate's emotional data into a generation AI, which then adjusts the way the summary is presented.

[0106] The reporting department can estimate the boss's emotions and adjust the way the report is presented based on the estimated boss' emotions. For example, if the boss is feeling stressed, the department can provide a concise and to-the-point report. If the boss is relaxed, the department can provide a report that includes detailed information. Furthermore, if the boss is busy, the department can prioritize important information in the report. This allows for more appropriate reporting by adjusting the way the report is presented based on the boss' emotions. Emotion estimation is achieved using an emotion engine or a generation AI. For example, the reporting department inputs the boss's emotion data into a generation AI, which then adjusts the way the report is presented.

[0107] The question reception unit can analyze the supervisor's past question history and select the optimal question reception method. For example, it can prioritize the question reception method that the supervisor has frequently used in the past. It can also suggest the optimal question reception method based on the supervisor's past question history. It can also analyze the supervisor's past question history and select the most efficient question reception method. In this way, the optimal question reception method can be selected by referring to the supervisor's past question history. Some or all of the above-mentioned processing in the question reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the question reception unit inputs the supervisor's past question history data into the generation AI, and the generation AI selects the optimal question reception method.

[0108] The advice providing unit can estimate the boss's emotions and adjust the way the advice is expressed based on the estimated boss' emotions. For example, if the boss is feeling stressed, it can provide concise, to-the-point advice. If the boss is relaxed, it can also provide advice that includes detailed information. Furthermore, if the boss is busy, it can provide advice that prioritizes important information. This allows for more appropriate advice by adjusting the way the advice is expressed according to the boss' emotions. Emotion estimation is achieved using an emotion engine or a generation AI. For example, the advice providing unit inputs the boss's emotion data into a generation AI, which then adjusts the way the advice is expressed.

[0109] The reporting system can further include an evaluation section that evaluates the work performance of subordinates. The evaluation section periodically evaluates the work performance of subordinates and reports the results to their superiors. For example, if a subordinate's work performance is high, the evaluation section can report the results to the superior to increase the subordinate's motivation. Also, if a subordinate's work performance is low, the evaluation section can identify areas for improvement and propose specific improvement measures to the superior. Furthermore, the evaluation section can propose training programs to help improve the subordinate's skills and knowledge based on the subordinate's work performance. This allows the superior to understand the subordinate's work performance and provide appropriate follow-up and guidance.

[0110] The reporting system can further include a prediction unit that predicts the work performance of subordinates. The prediction unit analyzes the subordinates' past work data and predicts their future work performance. For example, the prediction unit predicts the probability of success of future projects based on data on projects that the subordinates have succeeded in the past. It can also analyze data on projects that the subordinates have failed in the past and predict the risk of failure. Furthermore, the prediction unit can predict future workloads based on the subordinates' work performance and propose appropriate resource allocation. This allows managers to predict the future work performance of their subordinates and provide appropriate follow-up and guidance.

[0111] The reporting system can further include a visualization unit that visualizes the work performance of subordinates. The visualization unit displays the work performance of subordinates in visual formats such as graphs and charts, making it easy for supervisors to understand. For example, the work progress of subordinates can be displayed in a Gantt chart, allowing the progress of a project to be grasped at a glance. The work results of subordinates can also be displayed in bar graphs or pie charts, visually showing the distribution and percentage of results. Furthermore, the visualization unit can display the work performance of subordinates in chronological order, allowing the supervisor to grasp the fluctuations in work performance from the past to the present. This allows the supervisor to visually grasp the work performance of subordinates and provide appropriate follow-up and guidance.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The collection department collects data from subordinates' business emails or chats. The data from subordinates' business emails or chats includes text data, attachments, image data, etc. The collection department can also build a system to automatically collect subordinates' business emails and collect subordinates' chat data in real time. For example, the collection department extracts and collects data from subordinates' chat applications. Step 2: The learning unit trains the generation AI on the data collected by the collection unit. The generation AI learns the data using text generation AI (e.g., LLM) and has advanced natural language processing capabilities. The learning unit can also implement algorithms for the generation AI to learn the data. Step 3: In the summary creation section, the generation AI creates a summary of the subordinate's activities based on the data learned by the learning section. The summary is created based on the length of the summary and the type of information it contains. The generation AI can also implement an algorithm to summarize the content of the subordinate's work emails and chats and create a summary. Step 4: The reporting department reports the summary created by the summary creation department to the superior. The reporting department can provide the summary to the superior through a web application or a mobile application, or can send the summary to the superior by email. Step 5: The question reception unit receives questions from the superior based on the summary reported by the reporting unit. The question reception unit provides an interface for the superior to input questions about the summary, and can also send the superior's questions to the generation AI and receive answers. Step 6: The advice providing unit uses the generation AI to provide advice based on the questions received by the question receiving unit. The generation AI can also implement an algorithm to provide specific advice to superiors based on the content of subordinates' business emails and chats.

[0114] 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.

[0115] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 7, a 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] [Explanation of symbols]

[0186] 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 on business emails or chats of subordinates; A learning unit in which a generation AI learns the data collected by the collection unit; A summary creation unit in which the generation AI summarizes the activities of subordinates based on the data learned by the learning unit; a reporting unit that reports the summary created by the summary creating unit to a superior; a question receiving unit that receives questions from a superior based on the summary reported by the reporting unit; an advice providing unit that provides advice based on the question received by the question receiving unit; A system characterized by:

2. The collecting unit Estimate subordinates' emotions and adjust the timing of data collection based on the estimated emotions of subordinates 2. The system of claim 1.

3. The collecting unit Analyze subordinates' past business email or chat history and select the appropriate collection method 2. The system of claim 1.

4. The collecting unit When collecting data, filter it based on your subordinates' current projects and areas of interest.

2. The system of claim 1.

5. The collecting unit Estimate subordinates' emotions and prioritize data collection based on the estimated emotions.

2. The system of claim 1.

6. The collecting unit When collecting data, prioritize collecting the most relevant data by taking into account the geographical location of subordinates.

2. The system of claim 1.

7. The collecting unit When collecting data, analyze your subordinates' social media activity and collect relevant data.

2. The system of claim 1.

8. The learning unit Estimate the subordinate's emotions and select learning data based on the estimated emotions of the subordinate.

2. The system of claim 1.

Images