system

Abstract

The system according to this embodiment aims to objectively evaluate employees' email communications and provide feedback. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an evaluation unit, and a feedback unit. The collection unit collects email data. The analysis unit analyzes the email data collected by the collection unit. The evaluation unit generates evaluation indicators based on the data analyzed by the analysis unit. The feedback unit provides feedback based on the evaluation indicators generated by the evaluation unit.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

[0005] , , , , ,

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to objectively evaluate the quality and response speed of employees' email communication, and there is room for improvement.

[0005] The system according to the embodiment aims to objectively evaluate employees' email communication and provide feedback.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a collection unit, an analysis unit, an evaluation unit, and a feedback unit. The collection unit collects email data. The analysis unit analyzes the email data collected by the collection unit. The evaluation unit generates evaluation indicators based on the data analyzed by the analysis unit. The feedback unit provides feedback based on the evaluation indicators generated by the evaluation unit. [Effects of the Invention]

[0007] The system according to this embodiment can objectively evaluate employees' email communications and provide feedback. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that uses generative AI to analyze the content and style of employees' email communications and quantitatively evaluates the quality of communication, response speed, and professionalism. This system collects employee email data and analyzes it using generative AI. The generative AI combines natural language processing (NLP) and machine learning to analyze the content and style of emails. Specifically, it measures indicators such as reply time, language use, problem-solving ability, email structure, and accuracy of information. For example, reply time is measured by calculating the difference between the email sending time and the reply time. Language use is evaluated by analyzing the frequency of use of honorifics and polite expressions. Problem-solving ability is evaluated by extracting the problem-solving process from the email content and evaluating its effectiveness. Next, the communication ability of each employee is quantified based on the data analyzed by the generative AI. This makes it possible to objectively evaluate employee performance. For example, employees with short reply times, polite language use, and high problem-solving ability can receive high evaluations. Furthermore, the generative AI provides feedback for performance improvement based on the evaluation results of each employee. For example, employees with long reply times are told the importance of prompt responses and specific improvement measures are proposed. Employees who use inappropriate language will be instructed on how to use appropriate expressions. Employees with poor problem-solving skills will be given advice on how to improve their problem-solving processes. This system will ensure that performance evaluations are based on objective and quantitative data, rather than relying on subjective opinions. This is expected to improve employee performance. Furthermore, objective criteria for evaluating non-face-to-face communication skills will be provided, enabling appropriate evaluation even in remote work environments. The system will analyze the content and style of employees' email communications, allowing for quantitative evaluation of communication quality, response speed, and professionalism.

[0029] The system according to the embodiment comprises a collection unit, an analysis unit, an evaluation unit, and a feedback unit. The collection unit collects employee email data. The collection unit obtains email data from, for example, the employee's email server. The collection unit can also obtain email data from the employee's email client. Furthermore, the collection unit can access the employee's email account and collect email data. For example, the collection unit obtains email data from the email server using the IMAP protocol. The collection unit can also obtain email data from the email client using the POP3 protocol. The collection unit can also access the email account using OAuth authentication and collect email data. The analysis unit analyzes the email data collected by the collection unit. The analysis unit analyzes the content and style of the emails using generative AI. The analysis unit analyzes the content of the emails using, for example, natural language processing technology. Furthermore, the analysis unit can analyze the style of the emails using machine learning algorithms. Furthermore, the analysis unit can also calculate the email reply time. For example, the analysis unit analyzes the content of the emails using morphological analysis. The analysis unit can also analyze the style of the emails using classification algorithms. The analysis unit can also calculate the difference between the email sending time and the reply time. The evaluation unit generates evaluation metrics based on the data analyzed by the analysis unit. The evaluation unit generates metrics such as reply time, language use, problem-solving ability, email structure, and accuracy of information. The evaluation unit can also generate evaluation metrics using generative AI. For example, the evaluation unit can generate short reply times as an evaluation metric. The evaluation unit can also generate frequency of use of polite language as an evaluation metric. The evaluation unit can also generate problem-solving processes as evaluation metrics. The feedback unit provides feedback based on the evaluation metrics generated by the evaluation unit. The feedback unit provides specific improvement measures based on the evaluation results, for example. The feedback unit can also provide feedback using generative AI. For example, the feedback unit points out the importance of prompt responses to employees with long reply times. The feedback unit can also instruct employees with inappropriate language use on how to use appropriate expressions.The feedback department can also provide advice to employees with low problem-solving skills to improve their problem-solving processes. This allows the system to analyze the content and style of employees' email communications and quantitatively evaluate the quality of communication, response speed, and professionalism.

[0030] The collection unit collects employee email data. For example, the collection unit retrieves email data from the employee's mail server. It can also retrieve email data from the employee's email client. Furthermore, the collection unit can access the employee's email account and collect email data. For example, the collection unit retrieves email data from the mail server using the IMAP protocol. The collection unit can also retrieve email data from the email client using the POP3 protocol. The collection unit can also access the email account and collect email data using OAuth authentication. Specifically, using the IMAP protocol allows for real-time synchronization of emails on the mail server, enabling the acquisition of the latest email data. Using the POP3 protocol allows for downloading email data from the email client and saving it locally. OAuth authentication allows for secure access to the employee's email account and acquisition of the necessary email data. This enables the collection unit to collect employee email data in diverse ways, improving the overall data collection capability of the system. Furthermore, the collection unit centrally manages the collected email data, allowing the analysis and evaluation units to access it efficiently. For example, collected email data is stored in cloud storage, allowing the analysis and evaluation departments to access it in real time as needed. Furthermore, the data collection department can adjust the frequency and timing of data collection, enabling flexible responses to specific situations and conditions. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the email data collected by the collection unit. The analysis unit uses generative AI to analyze the content and style of the emails. For example, the analysis unit uses natural language processing technology to analyze the content of the emails. The analysis unit can also use machine learning algorithms to analyze the style of the emails. Furthermore, the analysis unit can calculate the email reply time. For example, the analysis unit uses morphological analysis to analyze the content of the emails. The analysis unit can also use classification algorithms to analyze the style of the emails. The analysis unit can also calculate the difference between the email sending time and the reply time. Specifically, the generative AI performs topic modeling and sentiment analysis to analyze the content of the emails. Topic modeling extracts the subject and important keywords of the emails, and sentiment analysis evaluates the emotional tone of the emails. The machine learning algorithm learns patterns of writing style and word usage to analyze the style of the emails and evaluates the format and structure of the emails. Furthermore, the analysis unit calculates the difference between the email sending time and the reply time to evaluate the speed of the replies. This allows the analytics department to comprehensively analyze collected email data and gain a detailed understanding of employees' communication styles and response speeds. Furthermore, the analytics department can utilize historical email data and statistical information to analyze long-term trends and patterns. For example, it can analyze fluctuations in response times over a specific period or changes in responses to specific topics to identify areas for improvement in employee performance and identify challenges. In addition, the analytics department can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This enables the analytics department to not only grasp the situation in real time but also to handle long-term performance evaluation and anomaly detection, improving the reliability and security of the entire system.

[0032] The evaluation department generates evaluation metrics based on data analyzed by the analysis department. For example, the evaluation department generates metrics such as response time, language use, problem-solving ability, email structure, and information accuracy. The evaluation department can also generate evaluation metrics using a generative AI. For example, the evaluation department can generate short response times as an evaluation metric. It can also generate frequency of polite language use as an evaluation metric. Furthermore, it can generate the problem-solving process as an evaluation metric. Specifically, the generative AI calculates each evaluation metric based on data provided by the analysis department, quantitatively evaluating employee performance. For example, short response times are calculated based on the difference between the email sending time and the response time, and are an important metric in tasks requiring quick responses. Evaluation of language use is based on the frequency of polite language and appropriate expressions, and is used to assess professionalism and the quality of customer service. Evaluation of problem-solving ability assesses the quality of the problem-solving process and proposals based on the content and structure of emails. This allows the evaluation department to comprehensively assess the quality of employees' email communication, response speed, and professionalism, and identify specific areas for improvement. Furthermore, the evaluation department can relatively evaluate employee performance by comparing it with past evaluation data and industry standards. For example, based on past evaluation data, they can identify areas for improvement and challenges in employee performance, and evaluate how well employees perform compared to industry standards. In addition, the evaluation department can visualize the evaluation results and provide them to employees and managers in an easy-to-understand manner. This enables the evaluation department to quantitatively and comprehensively evaluate employee performance and provide information to identify specific areas for improvement.

[0033] The Feedback Department provides feedback based on evaluation metrics generated by the Evaluation Department. For example, the Feedback Department provides specific improvement measures based on evaluation results. The Feedback Department can also provide feedback using generative AI. For example, it can point out the importance of prompt responses to employees with long response times. It can also instruct employees with inappropriate language use on appropriate expression usage. Furthermore, it can advise employees with poor problem-solving skills on improving their problem-solving processes. Specifically, the generative AI generates individual feedback for each employee based on evaluation results. For example, it might highlight the importance of prompt responses and provide specific improvement measures for employees with long response times, or show appropriate expression usage and specific examples for employees with inappropriate language use. For employees with poor problem-solving skills, it might suggest problem-solving processes and effective approaches, and provide specific improvement measures. This allows the Feedback Department to provide specific advice to improve employee performance and support employee growth. Additionally, the Feedback Department can monitor employee responses to feedback and progress on improvements, providing ongoing support. For example, it can track changes in the performance of employees who receive feedback and provide additional advice and support as needed. Furthermore, the feedback department can evaluate the effectiveness of the feedback and improve or optimize the feedback content. This allows the feedback department to provide effective support for continuously improving employee performance and enhance the overall system performance.

[0034] The collection unit can preprocess email data. For example, the collection unit can perform data cleaning of email data. For example, the collection unit can remove unnecessary information from email data and prepare it in a format suitable for analysis. The collection unit can also perform data normalization of email data. For example, the collection unit can standardize the format of email data to improve the accuracy of analysis. Furthermore, the collection unit can perform spam filtering. For example, the collection unit can exclude spam emails and select email data to be analyzed. In this way, the collection unit improves the accuracy of analysis by preprocessing the email data. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can have a generation AI perform the email data preprocessing.

[0035] The analysis unit can analyze the content and style of emails using natural language processing and machine learning. For example, the analysis unit can analyze the content of emails using morphological analysis. For example, the analysis unit can divide the email text into individual words and analyze the meaning of each word. The analysis unit can also analyze the structure of emails using grammatical analysis. For example, the analysis unit can analyze the grammatical structure of emails and understand the meaning of the text. Furthermore, the analysis unit can gain a deeper understanding of the content of emails using semantic analysis. For example, the analysis unit can analyze the context of emails and grasp the meaning of the text. As a result, the analysis unit can analyze the content and style of emails with high accuracy by using natural language processing and machine learning. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have a generative AI perform the analysis of the content and style of emails.

[0036] The evaluation unit can generate indicators for response time, language use, problem-solving ability, email structure, and information accuracy. For example, the evaluation unit can generate an indicator for response time. For instance, it can calculate the difference between the email sending time and the response time and evaluate the shortness of the response time. The evaluation unit can also generate an indicator for language use. For example, it can analyze the frequency of use of honorifics and polite expressions in emails and evaluate the appropriateness of the language use. Furthermore, the evaluation unit can also generate an indicator for problem-solving ability. For example, it can extract the problem-solving process from the content of an email and evaluate its effectiveness. In this way, the evaluation unit can generate a variety of evaluation indicators and evaluate employees' communication skills from multiple perspectives. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not. For example, the evaluation unit can have a generation AI perform the generation of evaluation indicators.

[0037] The feedback department can provide specific improvement measures based on the evaluation results. For example, the feedback department can point out the importance of prompt responses to employees who take a long time to respond. For example, the feedback department can suggest specific methods to shorten response times. The feedback department can also instruct employees who use inappropriate language on how to use appropriate expressions. For example, the feedback department can specifically explain how to use honorifics and polite expressions. Furthermore, the feedback department can provide advice to employees with poor problem-solving abilities on how to improve their problem-solving process. For example, the feedback department can specifically show the steps to problem-solving and instruct on how to implement them. In this way, the feedback department can support the improvement of employee performance by providing specific improvement measures based on the evaluation results. Some or all of the above processes in the feedback department may be performed using AI, for example, or not using AI. For example, the feedback department can have a generating AI perform the provision of feedback based on the evaluation results.

[0038] The data collection unit can analyze a user's past email sending history and select an appropriate collection method. For example, the data collection unit can identify the time periods when a user frequently sends emails and collect email data during those times. For example, the data collection unit can analyze a user's email sending history to identify the time periods when emails are sent most frequently. The data collection unit can also concentrate data collection on specific days of the week if a user sends many emails on those days. For example, the data collection unit can analyze a user's email sending patterns to identify a tendency to send many emails on specific days of the week. Furthermore, the data collection unit can analyze a user's email sending patterns and propose the most efficient collection method. For example, the data collection unit can select the optimal collection method based on the user's email sending history. This allows the data collection unit to select the optimal collection method by analyzing a user's past email sending history, enabling efficient data collection. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generative AI perform the analysis of the email sending history.

[0039] The collection unit can filter email data based on the user's current work situation and areas of interest. For example, if a user is working on an important project, the collection unit will prioritize collecting email data related to that project. For example, the collection unit will analyze the user's work situation and filter email data related to important projects. The collection unit can also select and collect highly relevant email data based on the user's areas of interest. For example, the collection unit will analyze the user's areas of interest and prioritize collecting relevant email data. Furthermore, the collection unit can grasp the user's work situation in real time and collect email data at the appropriate time. For example, the collection unit will analyze the user's work situation in real time and determine the optimal collection timing. This allows the collection unit to efficiently collect highly relevant data by filtering based on the user's work situation and areas of interest. Some or all of the above processing in the collection unit may be performed using AI, for example, or not. For example, the collection unit can have a generative AI perform the analysis of work situation and areas of interest.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting email data. For example, if the user is in a specific region, the data collection unit will prioritize the collection of email data related to that region. For example, the data collection unit will analyze the user's geographical location and filter the email data related to that region. The data collection unit can also collect email data related to the user's business trip destination if the user is on a business trip. For example, the data collection unit will analyze the user's geographical location and prioritize the collection of email data related to the business trip destination. Furthermore, the data collection unit can select and collect the most relevant email data based on the user's geographical location. For example, the data collection unit will analyze the user's geographical location and prioritize the collection of highly relevant email data. In this way, the data collection unit can efficiently collect highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generating AI perform the analysis of geographical location information.

[0041] The data collection unit can analyze a user's social media activity and collect relevant data when collecting email data. For example, the data collection unit can collect email data related to topics mentioned by the user on social media. For example, the data collection unit can analyze a user's social media activity and filter email data based on relevant topics. The data collection unit can also analyze a user's social media activity and select and collect highly relevant email data. For example, the data collection unit can analyze the content of a user's social media posts and prioritize the collection of relevant email data. Furthermore, the data collection unit can collect email data related to accounts that the user follows on social media. For example, the data collection unit can analyze the accounts that a user follows and prioritize the collection of relevant email data. In this way, the data collection unit can efficiently collect highly relevant data by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generative AI perform the analysis of social media activity.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the emails during the analysis. For example, the analysis unit performs a detailed analysis on emails of high importance. For example, the analysis unit analyzes the importance of emails and performs a detailed analysis on high-importance emails. The analysis unit can also perform a simplified analysis on emails of low importance. For example, the analysis unit analyzes the importance of emails and performs a simplified analysis on emails of low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the emails. For example, the analysis unit analyzes the importance of emails in real time and dynamically adjusts the level of detail of the analysis. This allows the analysis unit to perform a detailed analysis on important emails by adjusting the level of detail of the analysis based on the importance of the emails. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have a generating AI perform the analysis of email importance.

[0043] The analysis unit can apply different analysis algorithms depending on the email category during analysis. For example, the analysis unit can apply a business-specific analysis algorithm to business emails. For instance, the analysis unit analyzes the content of business emails and applies a business-specific algorithm. The analysis unit can also apply a private-specific analysis algorithm to private emails. For example, the analysis unit analyzes the content of private emails and applies a private-specific algorithm. Furthermore, the analysis unit can select and apply the most suitable analysis algorithm depending on the email category. For example, the analysis unit analyzes the email category and selects the most suitable analysis algorithm. This improves the accuracy of the analysis by applying the most suitable analysis algorithm according to the email category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have a generating AI perform the analysis of email categories.

[0044] The analysis unit can determine the priority of analysis based on the email sending time during analysis. For example, the analysis unit may prioritize the analysis of recently sent emails. For example, the analysis unit may analyze the email sending time and prioritize the analysis of recently sent emails. The analysis unit may also prioritize the analysis of emails sent at important times. For example, the analysis unit may analyze the email sending time and prioritize the analysis of emails sent at important times. Furthermore, the analysis unit may dynamically adjust the analysis priority based on the email sending time. For example, the analysis unit may analyze the email sending time in real time and dynamically adjust the analysis priority. This allows the analysis unit to prioritize the analysis of emails sent at important times by determining the analysis priority based on the email sending time. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may have a generating AI perform the analysis of email sending time.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the emails during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant emails. For example, the analysis unit can analyze the content of the emails and prioritize the analysis of highly relevant emails. The analysis unit can also postpone the analysis of less relevant emails. For example, the analysis unit can analyze the content of the emails and postpone the analysis of less relevant emails. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the emails. For example, the analysis unit can analyze the relevance of the emails in real time and dynamically adjust the order of analysis. This allows the analysis unit to prioritize the analysis of highly relevant emails by adjusting the order of analysis based on the relevance of the emails. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have a generating AI perform the analysis of email relevance.

[0046] The evaluation unit can select an appropriate evaluation method by referring to the user's past evaluation data when generating evaluation metrics. For example, the evaluation unit can select the optimal evaluation method based on the user's past evaluation data. For example, the evaluation unit can analyze the user's past evaluation data and select the optimal evaluation method. The evaluation unit can also analyze the user's past evaluation data and customize the evaluation method. For example, the evaluation unit can analyze the user's past evaluation data and customize the evaluation method. Furthermore, the evaluation unit can dynamically adjust the evaluation metrics by referring to the user's past evaluation data. For example, the evaluation unit can analyze the user's past evaluation data in real time and dynamically adjust the evaluation metrics. As a result, the evaluation unit can select the optimal evaluation method by referring to the user's past evaluation data, improving the accuracy of the evaluation. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can have the generation AI perform the analysis of past evaluation data.

[0047] The evaluation unit can customize evaluation metrics based on the user's current work situation when generating them. For example, if the user is working on an important project, the evaluation unit will generate evaluation metrics related to that project. For example, the evaluation unit will analyze the user's work situation and generate evaluation metrics related to important projects. The evaluation unit can also grasp the user's work situation in real time and generate appropriate evaluation metrics. For example, the evaluation unit will analyze the user's work situation in real time and generate appropriate evaluation metrics. Furthermore, the evaluation unit can dynamically customize evaluation metrics based on the user's work situation. For example, the evaluation unit will analyze the user's work situation in real time and dynamically customize evaluation metrics. This allows the evaluation unit to perform more appropriate evaluations by customizing evaluation metrics based on the user's current work situation. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can have the generation AI perform the analysis of the work situation.

[0048] The evaluation unit can select an appropriate evaluation method when generating evaluation metrics, taking into account the user's geographical location information. For example, if the user is in a specific region, the evaluation unit can generate evaluation metrics related to that region. For example, the evaluation unit can analyze the user's geographical location information and generate evaluation metrics related to that region. The evaluation unit can also generate evaluation metrics related to the destination of the business trip if the user is on a business trip. For example, the evaluation unit can analyze the user's geographical location information and generate evaluation metrics related to the business trip destination. Furthermore, the evaluation unit can select the most relevant evaluation metrics based on the user's geographical location information. For example, the evaluation unit can analyze the user's geographical location information and select the most relevant evaluation metrics. As a result, the evaluation unit can select the optimal evaluation method by considering the user's geographical location information, thereby improving the accuracy of the evaluation. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can have the generation AI perform the analysis of geographical location information.

[0049] The evaluation unit can generate evaluation metrics by analyzing the user's social media activity. For example, the evaluation unit can generate evaluation metrics related to topics mentioned by the user on social media. For example, the evaluation unit can analyze the user's social media activity and generate evaluation metrics based on relevant topics. The evaluation unit can also analyze the user's social media activity and generate highly relevant evaluation metrics. For example, the evaluation unit can analyze the content of the user's social media posts and generate relevant evaluation metrics. Furthermore, the evaluation unit can generate evaluation metrics related to accounts that the user follows on social media. For example, the evaluation unit can analyze the accounts that the user follows and generate relevant evaluation metrics. In this way, the evaluation unit can generate highly relevant evaluation metrics by analyzing the user's social media activity. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can have a generation AI perform the analysis of social media activity.

[0050] The feedback unit can provide optimal feedback by referring to the user's past feedback history when providing feedback. For example, the feedback unit can provide feedback that highlights areas for improvement based on feedback the user has received in the past. For example, the feedback unit can analyze the user's past feedback history and provide feedback that highlights areas for improvement. The feedback unit can also analyze the user's past feedback history and select the most effective feedback method. For example, the feedback unit analyzes the user's past feedback history and selects the most effective feedback method. Furthermore, the feedback unit can customize the content of the feedback by referring to the user's past feedback history. For example, the feedback unit analyzes the user's past feedback history and customizes the content of the feedback. In this way, the feedback unit can provide optimal feedback and highlight areas for improvement by referring to the user's past feedback history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can have a generating AI perform the analysis of past feedback history.

[0051] The feedback unit can customize the content of feedback based on the user's current work situation when providing feedback. For example, if the user is working on an important project, the feedback unit will provide feedback related to that project. For example, the feedback unit will analyze the user's work situation and provide feedback related to important projects. The feedback unit can also grasp the user's work situation in real time and provide appropriate feedback. For example, the feedback unit will analyze the user's work situation in real time and provide appropriate feedback. Furthermore, the feedback unit can dynamically customize the content of feedback based on the user's work situation. For example, the feedback unit will analyze the user's work situation in real time and dynamically customize the content of feedback. This allows the feedback unit to provide more appropriate feedback by customizing the content of feedback based on the user's current work situation. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can have a generating AI perform the analysis of the work situation.

[0052] The feedback unit can provide appropriate feedback by considering the user's geographical location information when providing feedback. For example, if the user is in a specific region, the feedback unit can provide feedback relevant to that region. For example, the feedback unit can analyze the user's geographical location information and provide feedback relevant to that region. The feedback unit can also provide feedback relevant to the user's business trip destination if the user is on a business trip. For example, the feedback unit can analyze the user's geographical location information and provide feedback relevant to the business trip destination. Furthermore, the feedback unit can select the most relevant feedback based on the user's geographical location information. For example, the feedback unit can analyze the user's geographical location information and select the most relevant feedback. In this way, the feedback unit can provide optimal and highly relevant feedback by considering the user's geographical location information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can have a generating AI perform the analysis of geographical location information.

[0053] The feedback unit can analyze the user's social media activity and suggest feedback content when providing feedback. For example, the feedback unit can provide feedback related to topics mentioned by the user on social media. For example, the feedback unit can analyze the user's social media activity and provide feedback based on relevant topics. The feedback unit can also analyze the user's social media activity and provide highly relevant feedback. For example, the feedback unit can analyze the content of the user's social media posts and provide relevant feedback. Furthermore, the feedback unit can provide feedback related to accounts that the user follows on social media. For example, the feedback unit can analyze the accounts that the user follows and provide relevant feedback. In this way, the feedback unit can provide highly relevant feedback by analyzing the user's social media activity. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can have a generative AI perform the analysis of social media activity.

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

[0055] The data collection unit can analyze a user's past email sending history and select an appropriate collection method. For example, it can identify the times of day when a user frequently sends emails and collect email data during those times. If a user sends many emails on a particular day of the week, it can concentrate data collection on that day. Furthermore, it can analyze the user's email sending patterns and suggest the most efficient collection method. This allows the data collection unit to select the optimal collection method by analyzing the user's past email sending history, enabling efficient data collection. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can have a generative AI perform the analysis of the email sending history.

[0056] The analysis unit can adjust the level of detail in its analysis based on the importance of the emails. For example, it can perform a detailed analysis on high-importance emails and a simplified analysis on low-importance emails. Furthermore, it can dynamically adjust the level of detail in its analysis according to the importance of the emails. This allows the analysis unit to perform detailed analysis on important emails by adjusting the level of detail based on the importance of the emails. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can have a generating AI perform the analysis of email importance.

[0057] The collection unit can filter email data based on the user's current work situation and areas of interest. For example, if a user is working on an important project, it will prioritize collecting email data related to that project. It can also select and collect highly relevant email data based on the user's areas of interest. Furthermore, it can grasp the user's work situation in real time and collect email data at the appropriate time. As a result, the collection unit can efficiently collect highly relevant data by filtering based on the user's work situation and areas of interest. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can have a generative AI perform the analysis of the work situation and areas of interest.

[0058] The evaluation unit can select an appropriate evaluation method by referring to the user's past evaluation data when generating evaluation metrics. For example, it can select the optimal evaluation method based on the user's past evaluation data. It can also analyze the user's past evaluation data and customize the evaluation method. Furthermore, it can dynamically adjust the evaluation metrics by referring to the user's past evaluation data. As a result, the evaluation unit can select the optimal evaluation method by referring to the user's past evaluation data, thereby improving the accuracy of the evaluation. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can have the generation AI perform the analysis of past evaluation data.

[0059] The feedback unit can provide optimal feedback by referring to the user's past feedback history when providing feedback. For example, it can provide feedback that highlights areas for improvement based on feedback the user has received in the past. It can also analyze the user's past feedback history and select the most effective feedback method. Furthermore, it can customize the content of the feedback by referring to the user's past feedback history. In this way, the feedback unit can provide optimal feedback and highlight areas for improvement by referring to the user's past feedback history. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit can have a generating AI perform the analysis of past feedback history.

[0060] The analysis unit can apply different analysis algorithms depending on the email category during analysis. For example, a business-specific analysis algorithm can be applied to business emails. A private-specific analysis algorithm can also be applied to private emails. Furthermore, the analysis unit can select and apply the most suitable analysis algorithm depending on the email category. This improves the accuracy of the analysis by applying the most suitable analysis algorithm according to the email category. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can have a generating AI perform the analysis of email categories.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The collection unit collects employee email data. The collection unit can, for example, retrieve email data from the employee's mail server. The collection unit can also retrieve email data from the employee's email client. Furthermore, the collection unit can access the employee's email account and collect email data. For example, the collection unit can retrieve email data from the mail server using the IMAP protocol. The collection unit can also retrieve email data from the email client using the POP3 protocol. The collection unit can also access the email account using OAuth authentication and collect email data. Step 2: The analysis unit analyzes the email data collected by the collection unit. The analysis unit analyzes the content and style of the emails using generative AI. For example, the analysis unit analyzes the content of the emails using natural language processing technology. The analysis unit can also analyze the style of the emails using machine learning algorithms. Furthermore, the analysis unit can calculate the email reply time. For example, the analysis unit analyzes the content of the emails using morphological analysis. The analysis unit can also analyze the style of the emails using classification algorithms. The analysis unit can also calculate the difference between the email sending time and the reply time. Step 3: The evaluation unit generates evaluation metrics based on the data analyzed by the analysis unit. The evaluation unit generates metrics such as response time, language use, problem-solving ability, email structure, and accuracy of information. The evaluation unit can also generate evaluation metrics using generation AI. For example, the evaluation unit can generate short response times as an evaluation metric. The evaluation unit can also generate frequency of use of polite language as an evaluation metric. The evaluation unit can also generate problem-solving processes as evaluation metrics. Step 4: The Feedback Department provides feedback based on the evaluation metrics generated by the Evaluation Department. For example, the Feedback Department provides specific improvement measures based on the evaluation results. The Feedback Department can also provide feedback using AI generation. For example, the Feedback Department can point out the importance of prompt responses to employees who take a long time to respond. The Feedback Department can also instruct employees who use inappropriate language on how to use appropriate expressions. The Feedback Department can also provide advice to employees with poor problem-solving skills on how to improve their problem-solving process.

[0063] (Example of form 2) The system according to an embodiment of the present invention is a system that uses generative AI to analyze the content and style of employees' email communications and quantitatively evaluates the quality of communication, response speed, and professionalism. This system collects employee email data and analyzes it using generative AI. The generative AI combines natural language processing (NLP) and machine learning to analyze the content and style of emails. Specifically, it measures indicators such as reply time, language use, problem-solving ability, email structure, and accuracy of information. For example, reply time is measured by calculating the difference between the email sending time and the reply time. Language use is evaluated by analyzing the frequency of use of honorifics and polite expressions. Problem-solving ability is evaluated by extracting the problem-solving process from the email content and evaluating its effectiveness. Next, the communication ability of each employee is quantified based on the data analyzed by the generative AI. This makes it possible to objectively evaluate employee performance. For example, employees with short reply times, polite language use, and high problem-solving ability can receive high evaluations. Furthermore, the generative AI provides feedback for performance improvement based on the evaluation results of each employee. For example, employees with long reply times are told the importance of prompt responses and specific improvement measures are proposed. Employees who use inappropriate language will be instructed on how to use appropriate expressions. Employees with poor problem-solving skills will be given advice on how to improve their problem-solving processes. This system will ensure that performance evaluations are based on objective and quantitative data, rather than relying on subjective opinions. This is expected to improve employee performance. Furthermore, objective criteria for evaluating non-face-to-face communication skills will be provided, enabling appropriate evaluation even in remote work environments. The system will analyze the content and style of employees' email communications, allowing for quantitative evaluation of communication quality, response speed, and professionalism.

[0064] The system according to the embodiment comprises a collection unit, an analysis unit, an evaluation unit, and a feedback unit. The collection unit collects employee email data. The collection unit obtains email data from, for example, the employee's email server. The collection unit can also obtain email data from the employee's email client. Furthermore, the collection unit can access the employee's email account and collect email data. For example, the collection unit obtains email data from the email server using the IMAP protocol. The collection unit can also obtain email data from the email client using the POP3 protocol. The collection unit can also access the email account using OAuth authentication and collect email data. The analysis unit analyzes the email data collected by the collection unit. The analysis unit analyzes the content and style of the emails using generative AI. The analysis unit analyzes the content of the emails using, for example, natural language processing technology. Furthermore, the analysis unit can analyze the style of the emails using machine learning algorithms. Furthermore, the analysis unit can also calculate the email reply time. For example, the analysis unit analyzes the content of the emails using morphological analysis. The analysis unit can also analyze the style of the emails using classification algorithms. The analysis unit can also calculate the difference between the email sending time and the reply time. The evaluation unit generates evaluation metrics based on the data analyzed by the analysis unit. The evaluation unit generates metrics such as reply time, language use, problem-solving ability, email structure, and accuracy of information. The evaluation unit can also generate evaluation metrics using generative AI. For example, the evaluation unit can generate short reply times as an evaluation metric. The evaluation unit can also generate frequency of use of polite language as an evaluation metric. The evaluation unit can also generate problem-solving processes as evaluation metrics. The feedback unit provides feedback based on the evaluation metrics generated by the evaluation unit. The feedback unit provides specific improvement measures based on the evaluation results, for example. The feedback unit can also provide feedback using generative AI. For example, the feedback unit points out the importance of prompt responses to employees with long reply times. The feedback unit can also instruct employees with inappropriate language use on how to use appropriate expressions.The feedback department can also provide advice to employees with low problem-solving skills to improve their problem-solving processes. This allows the system to analyze the content and style of employees' email communications and quantitatively evaluate the quality of communication, response speed, and professionalism.

[0065] The collection unit collects employee email data. For example, the collection unit retrieves email data from the employee's mail server. It can also retrieve email data from the employee's email client. Furthermore, the collection unit can access the employee's email account and collect email data. For example, the collection unit retrieves email data from the mail server using the IMAP protocol. The collection unit can also retrieve email data from the email client using the POP3 protocol. The collection unit can also access the email account and collect email data using OAuth authentication. Specifically, using the IMAP protocol allows for real-time synchronization of emails on the mail server, enabling the acquisition of the latest email data. Using the POP3 protocol allows for downloading email data from the email client and saving it locally. OAuth authentication allows for secure access to the employee's email account and acquisition of the necessary email data. This enables the collection unit to collect employee email data in diverse ways, improving the overall data collection capability of the system. Furthermore, the collection unit centrally manages the collected email data, allowing the analysis and evaluation units to access it efficiently. For example, collected email data is stored in cloud storage, allowing the analysis and evaluation departments to access it in real time as needed. Furthermore, the data collection department can adjust the frequency and timing of data collection, enabling flexible responses to specific situations and conditions. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0066] The analysis unit analyzes the email data collected by the collection unit. The analysis unit uses generative AI to analyze the content and style of the emails. For example, the analysis unit uses natural language processing technology to analyze the content of the emails. The analysis unit can also use machine learning algorithms to analyze the style of the emails. Furthermore, the analysis unit can calculate the email reply time. For example, the analysis unit uses morphological analysis to analyze the content of the emails. The analysis unit can also use classification algorithms to analyze the style of the emails. The analysis unit can also calculate the difference between the email sending time and the reply time. Specifically, the generative AI performs topic modeling and sentiment analysis to analyze the content of the emails. Topic modeling extracts the subject and important keywords of the emails, and sentiment analysis evaluates the emotional tone of the emails. The machine learning algorithm learns patterns of writing style and word usage to analyze the style of the emails and evaluates the format and structure of the emails. Furthermore, the analysis unit calculates the difference between the email sending time and the reply time to evaluate the speed of the replies. This allows the analytics department to comprehensively analyze collected email data and gain a detailed understanding of employees' communication styles and response speeds. Furthermore, the analytics department can utilize historical email data and statistical information to analyze long-term trends and patterns. For example, it can analyze fluctuations in response times over a specific period or changes in responses to specific topics to identify areas for improvement in employee performance and identify challenges. In addition, the analytics department can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This enables the analytics department to not only grasp the situation in real time but also to handle long-term performance evaluation and anomaly detection, improving the reliability and security of the entire system.

[0067] The evaluation department generates evaluation metrics based on data analyzed by the analysis department. For example, the evaluation department generates metrics such as response time, language use, problem-solving ability, email structure, and information accuracy. The evaluation department can also generate evaluation metrics using a generative AI. For example, the evaluation department can generate short response times as an evaluation metric. It can also generate frequency of polite language use as an evaluation metric. Furthermore, it can generate the problem-solving process as an evaluation metric. Specifically, the generative AI calculates each evaluation metric based on data provided by the analysis department, quantitatively evaluating employee performance. For example, short response times are calculated based on the difference between the email sending time and the response time, and are an important metric in tasks requiring quick responses. Evaluation of language use is based on the frequency of polite language and appropriate expressions, and is used to assess professionalism and the quality of customer service. Evaluation of problem-solving ability assesses the quality of the problem-solving process and proposals based on the content and structure of emails. This allows the evaluation department to comprehensively assess the quality of employees' email communication, response speed, and professionalism, and identify specific areas for improvement. Furthermore, the evaluation department can relatively evaluate employee performance by comparing it with past evaluation data and industry standards. For example, based on past evaluation data, they can identify areas for improvement and challenges in employee performance, and evaluate how well employees perform compared to industry standards. In addition, the evaluation department can visualize the evaluation results and provide them to employees and managers in an easy-to-understand manner. This enables the evaluation department to quantitatively and comprehensively evaluate employee performance and provide information to identify specific areas for improvement.

[0068] The Feedback Department provides feedback based on evaluation metrics generated by the Evaluation Department. For example, the Feedback Department provides specific improvement measures based on evaluation results. The Feedback Department can also provide feedback using generative AI. For example, it can point out the importance of prompt responses to employees with long response times. It can also instruct employees with inappropriate language use on appropriate expression usage. Furthermore, it can advise employees with poor problem-solving skills on improving their problem-solving processes. Specifically, the generative AI generates individual feedback for each employee based on evaluation results. For example, it might highlight the importance of prompt responses and provide specific improvement measures for employees with long response times, or show appropriate expression usage and specific examples for employees with inappropriate language use. For employees with poor problem-solving skills, it might suggest problem-solving processes and effective approaches, and provide specific improvement measures. This allows the Feedback Department to provide specific advice to improve employee performance and support employee growth. Additionally, the Feedback Department can monitor employee responses to feedback and progress on improvements, providing ongoing support. For example, it can track changes in the performance of employees who receive feedback and provide additional advice and support as needed. Furthermore, the feedback department can evaluate the effectiveness of the feedback and improve or optimize the feedback content. This allows the feedback department to provide effective support for continuously improving employee performance and enhance the overall system performance.

[0069] The collection unit can preprocess email data. For example, the collection unit can perform data cleaning of email data. For example, the collection unit can remove unnecessary information from email data and prepare it in a format suitable for analysis. The collection unit can also perform data normalization of email data. For example, the collection unit can standardize the format of email data to improve the accuracy of analysis. Furthermore, the collection unit can perform spam filtering. For example, the collection unit can exclude spam emails and select email data to be analyzed. In this way, the collection unit improves the accuracy of analysis by preprocessing the email data. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can have a generation AI perform the email data preprocessing.

[0070] The analysis unit can analyze the content and style of emails using natural language processing and machine learning. For example, the analysis unit can analyze the content of emails using morphological analysis. For example, the analysis unit can divide the email text into individual words and analyze the meaning of each word. The analysis unit can also analyze the structure of emails using grammatical analysis. For example, the analysis unit can analyze the grammatical structure of emails and understand the meaning of the text. Furthermore, the analysis unit can gain a deeper understanding of the content of emails using semantic analysis. For example, the analysis unit can analyze the context of emails and grasp the meaning of the text. As a result, the analysis unit can analyze the content and style of emails with high accuracy by using natural language processing and machine learning. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have a generative AI perform the analysis of the content and style of emails.

[0071] The evaluation unit can generate indicators for response time, language use, problem-solving ability, email structure, and information accuracy. For example, the evaluation unit can generate an indicator for response time. For instance, it can calculate the difference between the email sending time and the response time and evaluate the shortness of the response time. The evaluation unit can also generate an indicator for language use. For example, it can analyze the frequency of use of honorifics and polite expressions in emails and evaluate the appropriateness of the language use. Furthermore, the evaluation unit can also generate an indicator for problem-solving ability. For example, it can extract the problem-solving process from the content of an email and evaluate its effectiveness. In this way, the evaluation unit can generate a variety of evaluation indicators and evaluate employees' communication skills from multiple perspectives. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not. For example, the evaluation unit can have a generation AI perform the generation of evaluation indicators.

[0072] The feedback department can provide specific improvement measures based on the evaluation results. For example, the feedback department can point out the importance of prompt responses to employees who take a long time to respond. For example, the feedback department can suggest specific methods to shorten response times. The feedback department can also instruct employees who use inappropriate language on how to use appropriate expressions. For example, the feedback department can specifically explain how to use honorifics and polite expressions. Furthermore, the feedback department can provide advice to employees with poor problem-solving abilities on how to improve their problem-solving process. For example, the feedback department can specifically show the steps to problem-solving and instruct on how to implement them. In this way, the feedback department can support the improvement of employee performance by providing specific improvement measures based on the evaluation results. Some or all of the above processes in the feedback department may be performed using AI, for example, or not using AI. For example, the feedback department can have a generating AI perform the provision of feedback based on the evaluation results.

[0073] The data collection unit can analyze the user's emotions and adjust the timing of email data collection based on the analyzed emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to reduce the user's burden. For example, the data collection unit can analyze the user's stress level using an emotion estimation algorithm and delay collection if the stress level is high. The data collection unit can also collect email data immediately and perform rapid analysis if the user is relaxed. For example, the data collection unit can analyze the user's level of relaxation using an emotion estimation algorithm and collect data immediately if the user is relaxed. Furthermore, if the user is busy, the data collection unit can adjust the timing to collect email data after work. For example, the data collection unit can analyze the user's work situation and collect data after work. In this way, the data collection unit can reduce the user's burden and enable efficient data collection by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit may have a generative AI perform the estimation of the user's emotions.

[0074] The data collection unit can analyze a user's past email sending history and select an appropriate collection method. For example, the data collection unit can identify the time periods when a user frequently sends emails and collect email data during those times. For example, the data collection unit can analyze a user's email sending history to identify the time periods when emails are sent most frequently. The data collection unit can also concentrate data collection on specific days of the week if a user sends many emails on those days. For example, the data collection unit can analyze a user's email sending patterns to identify a tendency to send many emails on specific days of the week. Furthermore, the data collection unit can analyze a user's email sending patterns and propose the most efficient collection method. For example, the data collection unit can select the optimal collection method based on the user's email sending history. This allows the data collection unit to select the optimal collection method by analyzing a user's past email sending history, enabling efficient data collection. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generative AI perform the analysis of the email sending history.

[0075] The collection unit can filter email data based on the user's current work situation and areas of interest. For example, if a user is working on an important project, the collection unit will prioritize collecting email data related to that project. For example, the collection unit will analyze the user's work situation and filter email data related to important projects. The collection unit can also select and collect highly relevant email data based on the user's areas of interest. For example, the collection unit will analyze the user's areas of interest and prioritize collecting relevant email data. Furthermore, the collection unit can grasp the user's work situation in real time and collect email data at the appropriate time. For example, the collection unit will analyze the user's work situation in real time and determine the optimal collection timing. This allows the collection unit to efficiently collect highly relevant data by filtering based on the user's work situation and areas of interest. Some or all of the above processing in the collection unit may be performed using AI, for example, or not. For example, the collection unit can have a generative AI perform the analysis of work situation and areas of interest.

[0076] The data collection unit can analyze the user's emotions and determine the priority of email data to collect based on the analyzed emotions. For example, if the user is stressed, the data collection unit will postpone collecting less important email data. For example, the data collection unit can analyze the user's stress level using an emotion estimation algorithm and postpone collecting less important email data if the stress level is high. The data collection unit can also collect all email data equally if the user is relaxed. For example, the data collection unit can analyze the user's level of relaxation using an emotion estimation algorithm and collect all email data equally if the user is relaxed. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting high-priority email data. For example, the data collection unit can analyze the user's hurried situation and prioritize collecting high-priority email data. In this way, the data collection unit can prioritize the collection of important data by determining the priority of email data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit may have a generative AI perform emotion estimation.

[0077] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting email data. For example, if the user is in a specific region, the data collection unit will prioritize the collection of email data related to that region. For example, the data collection unit will analyze the user's geographical location and filter the email data related to that region. The data collection unit can also collect email data related to the user's business trip destination if the user is on a business trip. For example, the data collection unit will analyze the user's geographical location and prioritize the collection of email data related to the business trip destination. Furthermore, the data collection unit can select and collect the most relevant email data based on the user's geographical location. For example, the data collection unit will analyze the user's geographical location and prioritize the collection of highly relevant email data. In this way, the data collection unit can efficiently collect highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generating AI perform the analysis of geographical location information.

[0078] The data collection unit can analyze a user's social media activity and collect relevant data when collecting email data. For example, the data collection unit can collect email data related to topics mentioned by the user on social media. For example, the data collection unit can analyze a user's social media activity and filter email data based on relevant topics. The data collection unit can also analyze a user's social media activity and select and collect highly relevant email data. For example, the data collection unit can analyze the content of a user's social media posts and prioritize the collection of relevant email data. Furthermore, the data collection unit can collect email data related to accounts that the user follows on social media. For example, the data collection unit can analyze the accounts that a user follows and prioritize the collection of relevant email data. In this way, the data collection unit can efficiently collect highly relevant data by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generative AI perform the analysis of social media activity.

[0079] The analysis unit can analyze the user's emotions and adjust the presentation of the analysis based on the analyzed emotions. For example, if the user is tense, the analysis unit can provide a simple and easy-to-understand analysis result. For example, the analysis unit can analyze the user's level of tension using an emotion estimation algorithm and provide a simple analysis result if the user is tense. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, the analysis unit can analyze the user's level of relaxation using an emotion estimation algorithm and provide a detailed analysis result if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result. For example, the analysis unit can analyze the user's hurried situation and provide a concise analysis result. In this way, the analysis unit can provide an analysis result that is easy for the user to understand by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is 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 processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have a generative AI perform emotion estimation.

[0080] The analysis unit can adjust the level of detail of the analysis based on the importance of the emails during the analysis. For example, the analysis unit performs a detailed analysis on emails of high importance. For example, the analysis unit analyzes the importance of emails and performs a detailed analysis on high-importance emails. The analysis unit can also perform a simplified analysis on emails of low importance. For example, the analysis unit analyzes the importance of emails and performs a simplified analysis on emails of low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the emails. For example, the analysis unit analyzes the importance of emails in real time and dynamically adjusts the level of detail of the analysis. This allows the analysis unit to perform a detailed analysis on important emails by adjusting the level of detail of the analysis based on the importance of the emails. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have a generating AI perform the analysis of email importance.

[0081] The analysis unit can apply different analysis algorithms depending on the email category during analysis. For example, the analysis unit can apply a business-specific analysis algorithm to business emails. For instance, the analysis unit analyzes the content of business emails and applies a business-specific algorithm. The analysis unit can also apply a private-specific analysis algorithm to private emails. For example, the analysis unit analyzes the content of private emails and applies a private-specific algorithm. Furthermore, the analysis unit can select and apply the most suitable analysis algorithm depending on the email category. For example, the analysis unit analyzes the email category and selects the most suitable analysis algorithm. This improves the accuracy of the analysis by applying the most suitable analysis algorithm according to the email category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have a generating AI perform the analysis of email categories.

[0082] The analysis unit can analyze the user's emotions and adjust the length of the analysis based on the analyzed emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the user is in a hurry, the analysis unit can analyze the user's situation and provide a short, concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the analysis unit analyzes the user's level of relaxation using an emotion estimation algorithm and provides a detailed analysis result if the user is relaxed. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. For example, if the analysis unit analyzes the user's level of excitement using an emotion estimation algorithm and provides a visually stimulating analysis result. In this way, the analysis unit can provide an analysis result of an appropriate length for the user by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have a generative AI perform emotion estimation.

[0083] The analysis unit can determine the priority of analysis based on the email sending time during analysis. For example, the analysis unit may prioritize the analysis of recently sent emails. For example, the analysis unit may analyze the email sending time and prioritize the analysis of recently sent emails. The analysis unit may also prioritize the analysis of emails sent at important times. For example, the analysis unit may analyze the email sending time and prioritize the analysis of emails sent at important times. Furthermore, the analysis unit may dynamically adjust the analysis priority based on the email sending time. For example, the analysis unit may analyze the email sending time in real time and dynamically adjust the analysis priority. This allows the analysis unit to prioritize the analysis of emails sent at important times by determining the analysis priority based on the email sending time. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may have a generating AI perform the analysis of email sending time.

[0084] The analysis unit can adjust the order of analysis based on the relevance of the emails during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant emails. For example, the analysis unit can analyze the content of the emails and prioritize the analysis of highly relevant emails. The analysis unit can also postpone the analysis of less relevant emails. For example, the analysis unit can analyze the content of the emails and postpone the analysis of less relevant emails. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the emails. For example, the analysis unit can analyze the relevance of the emails in real time and dynamically adjust the order of analysis. This allows the analysis unit to prioritize the analysis of highly relevant emails by adjusting the order of analysis based on the relevance of the emails. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have a generating AI perform the analysis of email relevance.

[0085] The evaluation unit can analyze the user's emotions and adjust the method of generating evaluation metrics based on the analyzed emotions. For example, if the user is nervous, the evaluation unit can generate simple and highly visible evaluation metrics. For example, the evaluation unit can analyze the user's level of nervousness using an emotion estimation algorithm and generate simple evaluation metrics if the user is nervous. The evaluation unit can also generate detailed evaluation metrics if the user is relaxed. For example, the evaluation unit can analyze the user's level of relaxation using an emotion estimation algorithm and generate detailed evaluation metrics if the user is relaxed. Furthermore, if the user is in a hurry, the evaluation unit can generate concise evaluation metrics. For example, the evaluation unit can analyze the user's hurried situation and generate concise evaluation metrics. In this way, the evaluation unit can provide evaluation metrics that are easy for the user to understand by adjusting the method of generating evaluation metrics according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is 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 processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit may have a generative AI perform emotion estimation.

[0086] The evaluation unit can select an appropriate evaluation method by referring to the user's past evaluation data when generating evaluation metrics. For example, the evaluation unit can select the optimal evaluation method based on the user's past evaluation data. For example, the evaluation unit can analyze the user's past evaluation data and select the optimal evaluation method. The evaluation unit can also analyze the user's past evaluation data and customize the evaluation method. For example, the evaluation unit can analyze the user's past evaluation data and customize the evaluation method. Furthermore, the evaluation unit can dynamically adjust the evaluation metrics by referring to the user's past evaluation data. For example, the evaluation unit can analyze the user's past evaluation data in real time and dynamically adjust the evaluation metrics. As a result, the evaluation unit can select the optimal evaluation method by referring to the user's past evaluation data, improving the accuracy of the evaluation. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can have the generation AI perform the analysis of past evaluation data.

[0087] The evaluation unit can customize evaluation metrics based on the user's current work situation when generating them. For example, if the user is working on an important project, the evaluation unit will generate evaluation metrics related to that project. For example, the evaluation unit will analyze the user's work situation and generate evaluation metrics related to important projects. The evaluation unit can also grasp the user's work situation in real time and generate appropriate evaluation metrics. For example, the evaluation unit will analyze the user's work situation in real time and generate appropriate evaluation metrics. Furthermore, the evaluation unit can dynamically customize evaluation metrics based on the user's work situation. For example, the evaluation unit will analyze the user's work situation in real time and dynamically customize evaluation metrics. This allows the evaluation unit to perform more appropriate evaluations by customizing evaluation metrics based on the user's current work situation. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can have the generation AI perform the analysis of the work situation.

[0088] The evaluation unit can analyze the user's emotions and determine the priority of evaluation metrics based on the analyzed emotions. For example, if the user is stressed, the evaluation unit will postpone evaluation metrics of lower importance. For example, the evaluation unit can analyze the user's stress level using an emotion estimation algorithm and postpone evaluation metrics of lower importance if the stress level is high. The evaluation unit can also evaluate all evaluation metrics equally if the user is relaxed. For example, the evaluation unit can analyze the user's level of relaxation using an emotion estimation algorithm and evaluate all evaluation metrics equally if the user is relaxed. Furthermore, if the user is in a hurry, the evaluation unit can prioritize evaluation of high-importance metrics. For example, the evaluation unit can analyze the user's hurried situation and prioritize evaluation of high-importance metrics. In this way, the evaluation unit can prioritize evaluation of important evaluation metrics by determining the priority of evaluation metrics according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the processing described above in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit may have the generative AI perform emotion estimation.

[0089] The evaluation unit can select an appropriate evaluation method when generating evaluation metrics, taking into account the user's geographical location information. For example, if the user is in a specific region, the evaluation unit can generate evaluation metrics related to that region. For example, the evaluation unit can analyze the user's geographical location information and generate evaluation metrics related to that region. The evaluation unit can also generate evaluation metrics related to the destination of the business trip if the user is on a business trip. For example, the evaluation unit can analyze the user's geographical location information and generate evaluation metrics related to the business trip destination. Furthermore, the evaluation unit can select the most relevant evaluation metrics based on the user's geographical location information. For example, the evaluation unit can analyze the user's geographical location information and select the most relevant evaluation metrics. As a result, the evaluation unit can select the optimal evaluation method by considering the user's geographical location information, thereby improving the accuracy of the evaluation. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can have the generation AI perform the analysis of geographical location information.

[0090] The evaluation unit can generate evaluation metrics by analyzing the user's social media activity. For example, the evaluation unit can generate evaluation metrics related to topics mentioned by the user on social media. For example, the evaluation unit can analyze the user's social media activity and generate evaluation metrics based on relevant topics. The evaluation unit can also analyze the user's social media activity and generate highly relevant evaluation metrics. For example, the evaluation unit can analyze the content of the user's social media posts and generate relevant evaluation metrics. Furthermore, the evaluation unit can generate evaluation metrics related to accounts that the user follows on social media. For example, the evaluation unit can analyze the accounts that the user follows and generate relevant evaluation metrics. In this way, the evaluation unit can generate highly relevant evaluation metrics by analyzing the user's social media activity. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can have a generation AI perform the analysis of social media activity.

[0091] The feedback unit can analyze the user's emotions and adjust the method of providing feedback based on the analyzed emotions. For example, if the user is nervous, the feedback unit can provide feedback in gentle language. For example, the feedback unit can analyze the user's level of nervousness using an emotion estimation algorithm and provide feedback in gentle language if the user is nervous. The feedback unit can also provide detailed feedback if the user is relaxed. For example, the feedback unit can analyze the user's level of relaxation using an emotion estimation algorithm and provide detailed feedback if the user is relaxed. Furthermore, if the user is in a hurry, the feedback unit can provide concise feedback. For example, the feedback unit can analyze the user's hurried situation and provide concise feedback. In this way, the feedback unit can provide feedback that is easily accepted by the user by adjusting the method of providing feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit may have a generative AI perform emotion estimation.

[0092] The feedback unit can provide optimal feedback by referring to the user's past feedback history when providing feedback. For example, the feedback unit can provide feedback that highlights areas for improvement based on feedback the user has received in the past. For example, the feedback unit can analyze the user's past feedback history and provide feedback that highlights areas for improvement. The feedback unit can also analyze the user's past feedback history and select the most effective feedback method. For example, the feedback unit analyzes the user's past feedback history and selects the most effective feedback method. Furthermore, the feedback unit can customize the content of the feedback by referring to the user's past feedback history. For example, the feedback unit analyzes the user's past feedback history and customizes the content of the feedback. In this way, the feedback unit can provide optimal feedback and highlight areas for improvement by referring to the user's past feedback history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can have a generating AI perform the analysis of past feedback history.

[0093] The feedback unit can customize the content of feedback based on the user's current work situation when providing feedback. For example, if the user is working on an important project, the feedback unit will provide feedback related to that project. For example, the feedback unit will analyze the user's work situation and provide feedback related to important projects. The feedback unit can also grasp the user's work situation in real time and provide appropriate feedback. For example, the feedback unit will analyze the user's work situation in real time and provide appropriate feedback. Furthermore, the feedback unit can dynamically customize the content of feedback based on the user's work situation. For example, the feedback unit will analyze the user's work situation in real time and dynamically customize the content of feedback. This allows the feedback unit to provide more appropriate feedback by customizing the content of feedback based on the user's current work situation. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can have a generating AI perform the analysis of the work situation.

[0094] The feedback unit can analyze the user's emotions and prioritize feedback based on those emotions. For example, if the user is stressed, the feedback unit will postpone less important feedback. For instance, it can analyze the user's stress level using an emotion estimation algorithm and postpone less important feedback if the stress level is high. Furthermore, if the user is relaxed, the feedback unit can provide all feedback equally. For example, it can analyze the user's level of relaxation using an emotion estimation algorithm and provide all feedback equally if the user is relaxed. Additionally, if the user is in a hurry, the feedback unit can prioritize providing high-importance feedback. For example, it can analyze the user's hurried situation and prioritize providing high-importance feedback. In this way, the feedback unit can prioritize important feedback by determining its priority according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit may have a generative AI perform emotion estimation.

[0095] The feedback unit can provide appropriate feedback by considering the user's geographical location information when providing feedback. For example, if the user is in a specific region, the feedback unit can provide feedback relevant to that region. For example, the feedback unit can analyze the user's geographical location information and provide feedback relevant to that region. The feedback unit can also provide feedback relevant to the user's business trip destination if the user is on a business trip. For example, the feedback unit can analyze the user's geographical location information and provide feedback relevant to the business trip destination. Furthermore, the feedback unit can select the most relevant feedback based on the user's geographical location information. For example, the feedback unit can analyze the user's geographical location information and select the most relevant feedback. In this way, the feedback unit can provide optimal and highly relevant feedback by considering the user's geographical location information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can have a generating AI perform the analysis of geographical location information.

[0096] The feedback unit can analyze the user's social media activity and suggest feedback content when providing feedback. For example, the feedback unit can provide feedback related to topics mentioned by the user on social media. For example, the feedback unit can analyze the user's social media activity and provide feedback based on relevant topics. The feedback unit can also analyze the user's social media activity and provide highly relevant feedback. For example, the feedback unit can analyze the content of the user's social media posts and provide relevant feedback. Furthermore, the feedback unit can provide feedback related to accounts that the user follows on social media. For example, the feedback unit can analyze the accounts that the user follows and provide relevant feedback. In this way, the feedback unit can provide highly relevant feedback by analyzing the user's social media activity. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can have a generative AI perform the analysis of social media activity.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated emotions. For example, if the user is stressed, it can postpone the analysis of less important emails. If the user is relaxed, it can analyze all emails equally. Furthermore, if the user is in a hurry, it can prioritize the analysis of high-priority emails. In this way, the analysis unit can reduce the user's burden and enable efficient analysis by adjusting the analysis priority according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can have a generative AI perform emotion estimation.

[0099] The data collection unit can analyze a user's past email sending history and select an appropriate collection method. For example, it can identify the times of day when a user frequently sends emails and collect email data during those times. If a user sends many emails on a particular day of the week, it can concentrate data collection on that day. Furthermore, it can analyze the user's email sending patterns and suggest the most efficient collection method. This allows the data collection unit to select the optimal collection method by analyzing the user's past email sending history, enabling efficient data collection. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can have a generative AI perform the analysis of the email sending history.

[0100] The evaluation unit can analyze the user's emotions and adjust the method of generating evaluation metrics based on the analyzed emotions. For example, if the user is nervous, it can generate simple and highly visible evaluation metrics. If the user is relaxed, it can also generate detailed evaluation metrics. Furthermore, if the user is in a hurry, it can generate concise evaluation metrics. In this way, the evaluation unit can provide evaluation metrics that are easy for the user to understand by adjusting the method of generating evaluation metrics according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not using AI. For example, the evaluation unit can have a generative AI perform emotion estimation.

[0101] The feedback unit can analyze the user's emotions and adjust the way feedback is provided based on the analyzed emotions. For example, if the user is nervous, it can provide feedback in gentle language. If the user is relaxed, it can provide detailed feedback. Furthermore, if the user is in a hurry, it can provide concise feedback. In this way, the feedback unit can provide feedback that is easily accepted by the user by adjusting the way feedback is provided according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not using AI. For example, the feedback unit can have a generative AI perform emotion estimation.

[0102] The analysis unit can adjust the level of detail in its analysis based on the importance of the emails. For example, it can perform a detailed analysis on high-importance emails and a simplified analysis on low-importance emails. Furthermore, it can dynamically adjust the level of detail in its analysis according to the importance of the emails. This allows the analysis unit to perform detailed analysis on important emails by adjusting the level of detail based on the importance of the emails. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can have a generating AI perform the analysis of email importance.

[0103] The collection unit can filter email data based on the user's current work situation and areas of interest. For example, if a user is working on an important project, it will prioritize collecting email data related to that project. It can also select and collect highly relevant email data based on the user's areas of interest. Furthermore, it can grasp the user's work situation in real time and collect email data at the appropriate time. As a result, the collection unit can efficiently collect highly relevant data by filtering based on the user's work situation and areas of interest. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can have a generative AI perform the analysis of the work situation and areas of interest.

[0104] The evaluation unit can select an appropriate evaluation method by referring to the user's past evaluation data when generating evaluation metrics. For example, it can select the optimal evaluation method based on the user's past evaluation data. It can also analyze the user's past evaluation data and customize the evaluation method. Furthermore, it can dynamically adjust the evaluation metrics by referring to the user's past evaluation data. As a result, the evaluation unit can select the optimal evaluation method by referring to the user's past evaluation data, thereby improving the accuracy of the evaluation. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can have the generation AI perform the analysis of past evaluation data.

[0105] The feedback unit can provide optimal feedback by referring to the user's past feedback history when providing feedback. For example, it can provide feedback that highlights areas for improvement based on feedback the user has received in the past. It can also analyze the user's past feedback history and select the most effective feedback method. Furthermore, it can customize the content of the feedback by referring to the user's past feedback history. In this way, the feedback unit can provide optimal feedback and highlight areas for improvement by referring to the user's past feedback history. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit can have a generating AI perform the analysis of past feedback history.

[0106] The analysis unit can apply different analysis algorithms depending on the email category during analysis. For example, a business-specific analysis algorithm can be applied to business emails. A private-specific analysis algorithm can also be applied to private emails. Furthermore, the analysis unit can select and apply the most suitable analysis algorithm depending on the email category. This improves the accuracy of the analysis by applying the most suitable analysis algorithm according to the email category. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can have a generating AI perform the analysis of email categories.

[0107] The feedback unit can analyze the user's emotions and prioritize feedback based on those emotions. For example, if the user is stressed, it may postpone less important feedback. If the user is relaxed, it may provide all feedback equally. Furthermore, if the user is in a hurry, it may prioritize providing more important feedback. In this way, the feedback unit can prioritize important feedback by prioritizing it according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the feedback unit may be performed using AI or not. For example, the feedback unit can have a generative AI perform emotion estimation.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The collection unit collects employee email data. The collection unit can, for example, retrieve email data from the employee's mail server. The collection unit can also retrieve email data from the employee's email client. Furthermore, the collection unit can access the employee's email account and collect email data. For example, the collection unit can retrieve email data from the mail server using the IMAP protocol. The collection unit can also retrieve email data from the email client using the POP3 protocol. The collection unit can also access the email account using OAuth authentication and collect email data. Step 2: The analysis unit analyzes the email data collected by the collection unit. The analysis unit analyzes the content and style of the emails using generative AI. For example, the analysis unit analyzes the content of the emails using natural language processing technology. The analysis unit can also analyze the style of the emails using machine learning algorithms. Furthermore, the analysis unit can calculate the email reply time. For example, the analysis unit analyzes the content of the emails using morphological analysis. The analysis unit can also analyze the style of the emails using classification algorithms. The analysis unit can also calculate the difference between the email sending time and the reply time. Step 3: The evaluation unit generates evaluation metrics based on the data analyzed by the analysis unit. The evaluation unit generates metrics such as response time, language use, problem-solving ability, email structure, and accuracy of information. The evaluation unit can also generate evaluation metrics using generation AI. For example, the evaluation unit can generate short response times as an evaluation metric. The evaluation unit can also generate frequency of use of polite language as an evaluation metric. The evaluation unit can also generate problem-solving processes as evaluation metrics. Step 4: The Feedback Department provides feedback based on the evaluation metrics generated by the Evaluation Department. For example, the Feedback Department provides specific improvement measures based on the evaluation results. The Feedback Department can also provide feedback using AI generation. For example, the Feedback Department can point out the importance of prompt responses to employees who take a long time to respond. The Feedback Department can also instruct employees who use inappropriate language on how to use appropriate expressions. The Feedback Department can also provide advice to employees with poor problem-solving skills on how to improve their problem-solving process.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] For example, the data collection unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the evaluation unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the feedback unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the evaluation unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the feedback unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the evaluation unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the feedback unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] For example, the data collection unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the evaluation unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the feedback unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0163] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] 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.

[0173] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) A collection unit that collects email data, An analysis unit analyzes the email data collected by the aforementioned collection unit, An evaluation unit generates evaluation indicators based on the data analyzed by the analysis unit, The system includes a feedback unit that provides feedback based on evaluation indicators generated by the evaluation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Preprocessing of email data The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze email content and style using natural language processing and machine learning. The system described in Appendix 1, characterized by the features described herein. (Note 4) The evaluation unit, Generate metrics for response time, language use, problem-solving ability, email structure, and information accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned feedback unit is Based on the evaluation results, we will provide specific improvement measures. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We analyze user sentiment and adjust the timing of email data collection based on the analyzed user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past email sending history and select the appropriate collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting email data, filtering is performed based on the user's current work situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We analyze user sentiment and prioritize the email data to collect based on the analyzed sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting email data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting email data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system analyzes user emotions and adjusts the way the analysis is presented based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the email. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the email category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system analyzes the user's emotions and adjusts the length of the analysis based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the emails were sent. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the emails. The system described in Appendix 1, characterized by the features described herein. (Note 18) The evaluation unit, We analyze user emotions and adjust the method for generating evaluation metrics based on the analyzed user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit, When generating evaluation metrics, the system selects an appropriate evaluation method by referring to the user's past evaluation data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit, When generating performance metrics, customize them based on the user's current work situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, Analyze user emotions and determine the priority of evaluation metrics based on the analyzed user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, When generating evaluation metrics, select an appropriate evaluation method that takes into account the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, When generating evaluation metrics, the system analyzes users' social media activity to generate the metrics. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback unit is We analyze user emotions and adjust the way feedback is provided based on the analyzed user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is When providing feedback, we refer to the user's past feedback history to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is When providing feedback, customize the content of the feedback based on the user's current work situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is Analyze user emotions and prioritize feedback based on the analyzed emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is When providing feedback, we will take the user's geographical location into consideration to provide appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is When providing feedback, we analyze the user's social media activity and suggest content for the feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects email data, An analysis unit analyzes the email data collected by the aforementioned collection unit, An evaluation unit generates evaluation indicators based on the data analyzed by the analysis unit, The system includes a feedback unit that provides feedback based on evaluation indicators generated by the evaluation unit. A system characterized by the following features.

2. The aforementioned collection unit is Preprocessing of email data The system according to feature 1.

3. The aforementioned analysis unit, Analyze email content and style using natural language processing and machine learning. The system according to feature 1.

4. The evaluation unit, Generate metrics for response time, language use, problem-solving ability, email structure, and information accuracy. The system according to feature 1.

5. The aforementioned feedback unit is Based on the evaluation results, we will provide specific improvement measures. The system according to feature 1.

6. The aforementioned collection unit is We analyze user sentiment and adjust the timing of email data collection based on the analyzed user sentiment. The system according to feature 1.

7. The aforementioned collection unit is Analyze the user's past email sending history and select the appropriate collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting email data, filtering is performed based on the user's current work situation and areas of interest. The system according to feature 1.

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