system

Abstract

The system according to this embodiment aims to convert feedback into soft and concrete expressions and provide them to the person being evaluated. [Solution] The system according to the embodiment comprises an input unit, a conversion unit, a provision unit, and a management unit. The input unit receives feedback. The conversion unit converts the feedback received by the input unit into a softer, more concrete expression. The provision unit provides the feedback converted by the conversion unit to the person being evaluated. The management unit manages the feedback history.

Description

Technical Field

[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, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, there is room for improvement because the feedback lacks specificity or may hurt the other party with a harsh expression.

[0005] The system according to the embodiment aims to convert the feedback into a soft and specific expression and provide it to the evaluated person.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an input unit, a conversion unit, a provision unit, and a management unit. The input unit receives feedback. The conversion unit converts the feedback received by the input unit into a softer, more concrete expression. The provision unit provides the feedback converted by the conversion unit to the person being evaluated. The management unit manages the feedback history. [Effects of the Invention]

[0007] The system according to this embodiment can convert feedback into soft and specific language and provide it to the person being evaluated. [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 manages communication between multiple 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 FeedHarmony Co-creation Multi-Facilitation Feedback System, according to an embodiment of the present invention, is a tool that utilizes AI to improve the quality of multi-faceted feedback. This system converts evaluators' feedback into soft and specific language and presents improvement suggestions. It provides evaluators with easily understandable and specific improvement measures and manages the feedback history. This system makes feedback specific and easy to accept, promoting employee growth. Evaluators can efficiently provide high-quality feedback, and evaluators can easily understand areas for improvement. As a result, overall workplace communication improves, and motivation and productivity increase. For example, if an evaluator inputs feedback such as, "Person B is not doing their job well at all. They are causing trouble for everyone around them," the AI ​​converts it into soft and specific language such as, "In Project X, there are many situations where Person B is able to utilize their strengths and perform well, but there seem to be cases where things are not going well in regular meetings, etc. You might want to try an approach like XXX." This makes it easier for evaluators to understand specific improvement measures, and evaluators can provide high-quality feedback. In addition, the AI ​​manages the feedback history, allowing evaluators to refer to past feedback. This makes it easier for evaluators to provide consistent feedback. Furthermore, the AI ​​provides evaluators with feedback points, leading to increased self-awareness. This system addresses the challenges of quality and efficiency in multi-rater feedback faced by business leaders and HR managers, and is expected to promote employee growth and improve overall workplace communication. Thus, FeedHarmony's multi-rater feedback system can promote employee growth and improve overall workplace communication.

[0029] The FeedHarmony collaborative multifaceted feedback system according to this embodiment comprises an input unit, a conversion unit, a provision unit, and a management unit. The input unit receives feedback. For example, the evaluator can input feedback in text format into the input unit. The input unit can also receive feedback using voice input. For example, the evaluator can input feedback by voice and convert it to text using speech recognition technology. Furthermore, the input unit can also reuse feedback previously entered by the evaluator by referring to past feedback history. The conversion unit converts the feedback entered by the input unit into softer, more specific expressions. For example, the conversion unit can analyze the evaluator's feedback using AI and convert it into an appropriate expression. For example, if the evaluator inputs "Person B is not doing their job well at all," the conversion unit converts it into a softer expression such as "Person B seems to have some difficulties with a particular project." The conversion unit can also suggest specific improvement measures depending on the content of the feedback. For example, the conversion unit suggests a specific improvement measure such as "Person B is advised to take XXX training to improve their project management skills." The provision unit provides the feedback converted by the conversion unit to the person being evaluated. The service provider can, for example, send feedback to the person being evaluated via email. The service provider can also provide a dedicated web portal where the person being evaluated can view their feedback. For example, the person being evaluated can log in to the web portal and view past feedback. Furthermore, the service provider can send notifications to the person being evaluated depending on the content of the feedback. For example, the service provider can send a notification to the person being evaluated when they receive new feedback. The management department manages the feedback history. For example, the management department can save feedback previously provided by evaluators and make it available for reference as needed. The management department can set the retention period and format for feedback. For example, the management department can save feedback for a certain period and then automatically delete it. The management department can also set access permissions for feedback.For example, the management department can configure the system so that only specific evaluators can access specific feedback. This allows the FeedHarmony collaborative multifaceted feedback system, according to this embodiment, to improve the quality of feedback and promote employee growth.

[0030] The input section is where feedback is entered. For example, the input section allows evaluators to enter feedback in text format. The input section also allows feedback to be entered using voice input. For example, the evaluator can enter feedback by voice and convert it to text using speech recognition technology. Specifically, the speech recognition technology analyzes the evaluator's voice in real time and converts it into accurate text data. This makes it easy for evaluators to enter feedback and significantly reduces the effort required for input. Furthermore, the input section can also refer to past feedback history and allow evaluators to reuse feedback they have previously entered. For example, by searching for feedback entered in the past and reusing it for similar situations, the consistency of feedback can be maintained. By integrating these functions, the input section provides an environment in which evaluators can enter feedback efficiently and effectively. In addition, the input section is designed with a user interface that is easy for evaluators to operate intuitively. For example, it has an interface that allows seamless switching between voice input and text input, and a function that allows easy searching of past feedback history. This allows evaluators to enter feedback without stress and improve the quality of feedback.

[0031] The conversion unit transforms the feedback entered by the input unit into softer, more concrete expressions. For example, the conversion unit can use AI to analyze the evaluator's feedback and transform it into appropriate expressions. Specifically, it uses natural language processing technology to analyze the evaluator's feedback and understand the context and intent. For example, if an evaluator inputs, "Person B is not doing their job well at all," the conversion unit analyzes the context and transforms it into a softer expression. The conversion unit transforms it into a softer expression such as, "It seems that Person B is having trouble with certain aspects of the project." The conversion unit can also suggest specific improvement measures depending on the content of the feedback. For example, the conversion unit might suggest, "We recommend that Person B take XXX training to improve their project management skills." In this way, the conversion unit can transform the evaluator's feedback into something more constructive and positive, and present it in a way that is more easily accepted by the person being evaluated. Furthermore, the conversion unit can analyze the content of the feedback and extract common issues and trends. For example, it can analyze feedback from multiple evaluators and identify common issues regarding specific skills or behaviors. This allows for an understanding of issues across the entire organization and the implementation of effective countermeasures. Through these functions, the conversion unit can improve the quality of feedback and promote the growth of those being evaluated.

[0032] The service provider delivers the feedback converted by the conversion unit to the person being evaluated. For example, the service provider can send the feedback to the person being evaluated via email. Specifically, the service provider automatically converts the converted feedback into email format and sends it to the person being evaluated's email address. The service provider can also provide a dedicated web portal where the person being evaluated can view their feedback. For example, the person being evaluated can log in to the web portal and view past feedback. The web portal has a user-friendly interface, allowing the person being evaluated to easily search for and view feedback. Furthermore, the service provider can send notifications to the person being evaluated depending on the content of the feedback. For example, the service provider can send a notification to the person being evaluated when they receive new feedback. This allows the person being evaluated to respond quickly without missing important feedback. Through these functions, the service provider provides an environment in which the person being evaluated can effectively receive and utilize feedback. Furthermore, the service provider can customize the method of providing feedback. For example, the method of providing feedback can be selected according to the person being evaluated's preferences. This allows the service provider to provide feedback in the form that is most acceptable to the person being evaluated, maximizing the effectiveness of the feedback.

[0033] The management department manages the feedback history. For example, the management department stores feedback previously provided by evaluators and makes it available for reference as needed. Specifically, the management department stores feedback in a database, making it easy for evaluators and those being evaluated to search for past feedback. The management department can set the retention period and storage format for feedback. For example, the management department can store feedback for a certain period and then automatically delete it. The management department can also set access permissions for feedback. For example, the management department can set it so that only specific evaluators can access specific feedback. This ensures the privacy and security of feedback. Furthermore, the management department can generate feedback statistics and understand feedback trends across the organization. For example, the management department can analyze the content and frequency of feedback to identify challenges and areas for improvement across the organization. This allows the management department to provide data to improve the quality of feedback and promote overall organizational growth. Through these functions, the management department can effectively manage and utilize feedback and improve the overall performance of the organization.

[0034] The service provider can provide specific improvement measures that are easy for the person being evaluated to understand. For example, the service provider might present specific improvement measures to the person being evaluated. For example, the service provider might present specific improvement measures such as, "We recommend that Mr. / Ms. B take the XXX training to improve their project management skills." The service provider can also provide resources for the person being evaluated to implement the improvement measures. For example, the service provider might provide the person being evaluated with a link to a training program. Furthermore, the service provider can record the results of the person being evaluated implementing the improvement measures as feedback. For example, the service provider might record the results as feedback when the person being evaluated completes the training program. This makes it easier for the person being evaluated to understand and implement the specific improvement measures. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can use AI to analyze the content of the feedback and propose appropriate improvement measures in order to present specific improvement measures to the person being evaluated.

[0035] The management department can enable evaluators to refer to past feedback. For example, the management department can save feedback previously provided by evaluators and make it available for reference as needed. The management department can set the retention period and format of the feedback. For example, the management department can save feedback for a certain period and then automatically delete it. The management department can also set access permissions for the feedback. For example, the management department can set it so that only specific evaluators can access specific feedback. Furthermore, the management department can provide a search function for feedback when evaluators refer to past feedback. For example, the management department can enable evaluators to search past feedback by entering specific keywords. This makes it easier for evaluators to provide consistent feedback. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can analyze past feedback using AI and provide it in a format that is easy for evaluators to refer to.

[0036] The conversion unit can transform the evaluator's feedback into softer, more specific language. For example, the conversion unit can use AI to analyze the evaluator's feedback and convert it into appropriate language. For instance, if the evaluator inputs "Person B is not doing their job well at all," the conversion unit will convert it into a softer expression such as "Person B seems to be having some difficulties with a particular project." The conversion unit can also suggest specific improvement measures depending on the content of the feedback. For example, the conversion unit might suggest a specific improvement measure such as, "We recommend that Person B take XXX training to improve their project management skills." This makes the feedback more specific and easier to accept. Some or all of the above processing in the conversion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the conversion unit can input the evaluator's feedback into a generative AI and have the generative AI perform the process of converting it into softer, more specific language.

[0037] The service provider can present feedback points to the evaluator. For example, the service provider might present key points of feedback to the evaluator. For example, it might present a point such as, "Feedback on B's project management skills is important." The service provider can also highlight key points when the evaluator enters feedback. For example, the service provider highlights key points on the feedback entry screen. Furthermore, the service provider can provide a function that allows the evaluator to refer to past feedback history when entering feedback. For example, the service provider displays feedback previously provided by the evaluator, making it easier to provide consistent feedback. This makes it easier for evaluators to provide high-quality feedback. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can use AI to analyze the content of feedback and extract key points in order to present feedback points to the evaluator.

[0038] The management department can manage the history to maintain the consistency of feedback. For example, the management department can save feedback previously provided by evaluators and make it available for reference as needed. The management department can set the retention period and format of the feedback. For example, the management department can save feedback for a certain period and then automatically delete it. The management department can also set access permissions for the feedback. For example, the management department can set it so that only specific evaluators can access specific feedback. Furthermore, the management department can provide a feedback search function when evaluators refer to past feedback. For example, the management department can allow evaluators to search past feedback by entering specific keywords. This ensures the consistency of feedback. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can analyze past feedback using AI and provide it in a format that is easy for evaluators to refer to.

[0039] The input unit can analyze past feedback history and select the optimal input method. For example, the input unit can prioritize suggesting input methods (voice, text, etc.) that the user has preferred in the past. For example, if the user has preferred voice input in the past, the input unit will prioritize suggesting voice input. The input unit can also analyze the format of feedback previously entered by the user and provide the optimal template. For example, the input unit can analyze feedback templates previously used by the user and provide the optimal template. Furthermore, the input unit can suggest the optimal input method for a specific time period based on the user's past feedback history. For example, the input unit can analyze the user's history of feedback entered during a specific time period and suggest the optimal input method for that time period. This allows the user to be provided with the most suitable input method. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can analyze past feedback history using AI and select the optimal input method.

[0040] The input unit can filter feedback input based on the user's current projects and areas of interest. For example, the input unit can prioritize inputting feedback related to the user's current projects. The input unit can also filter and input relevant feedback based on the user's areas of interest. The input unit can also select and input appropriate feedback according to the user's current work. This allows for the input of feedback tailored to the user's interests. Some or all of the above processing in the input unit may be performed using AI, or not. For example, the input unit can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the feedback filtering.

[0041] The input unit can prioritize inputting highly relevant feedback when inputting feedback, taking into account the user's geographical location information. For example, if the user is in the office, the input unit can prioritize inputting work-related feedback. For example, if the user is on a business trip, the input unit can prioritize inputting feedback related to the destination. For example, if the user is on a business trip, the input unit can prioritize inputting feedback related to the destination. For example, if the user is at home, the input unit can prioritize inputting feedback related to remote work. This allows for the input of appropriate feedback based on the user's location information. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's geographical location information into a generating AI and cause the generating AI to perform the process of prioritizing the input of highly relevant feedback.

[0042] The input unit can analyze the user's social media activity and input relevant feedback when feedback is entered. For example, the input unit can input feedback related to projects the user has shared on social media. The input unit can also input relevant feedback based on the user's interests on social media. For example, the input unit can input relevant feedback based on the user's interests on social media. The input unit can also select and input appropriate feedback from the user's social media activity. For example, the input unit can select and input appropriate feedback from the user's social media activity. This allows for the input of feedback based on the user's social media activity. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's social media activity data into a generating AI and have the generating AI perform the process of inputting relevant feedback.

[0043] The transformation unit can adjust the level of detail in the transformation based on the importance of the feedback. For example, in the case of important feedback, the transformation unit can perform a transformation that includes a detailed explanation. For example, in the case of important feedback, the transformation unit can perform a transformation that includes a detailed explanation. The transformation unit can also perform a transformation that is concise in the case of minor feedback. For example, in the case of minor feedback, the transformation unit can perform a transformation with an appropriate level of detail in the case of moderately important feedback. For example, in the case of moderately important feedback, the transformation unit can perform a transformation with an appropriate level of detail according to the importance of the feedback. Some or all of the above processing in the transformation unit may be performed using AI, for example, or without AI. For example, the transformation unit can evaluate the importance of the feedback using AI and adjust the level of detail in the transformation.

[0044] The transformation unit can apply different transformation algorithms depending on the category of the feedback when transforming it. For example, in the case of performance-related feedback, the transformation unit can perform a transformation that includes specific improvement measures. For example, in the case of communication-related feedback, the transformation unit can perform a transformation that includes specific improvement measures. For example, in the case of skill-related feedback, the transformation unit can perform a transformation that includes specific training suggestions. This allows for appropriate transformation according to the category of the feedback. Some or all of the above processing in the transformation unit may be performed using AI, for example, or without AI. For example, the transformation unit can classify the categories of feedback using AI and apply an appropriate transformation algorithm.

[0045] The conversion unit can determine the priority of conversions based on the timing of feedback submission when converting feedback. For example, the conversion unit can perform conversions with the highest priority for urgent feedback. For example, the conversion unit can perform conversions with the normal priority for regular feedback. For example, the conversion unit can perform conversions with the normal priority for regular feedback. For example, the conversion unit can postpone the conversion of past feedback. For example, the conversion unit can postpone the conversion of past feedback. This allows for conversions to be performed with appropriate priority according to the timing of feedback submission. Some or all of the above processing in the conversion unit may be performed using AI, or not. For example, the conversion unit can use AI to evaluate the timing of feedback submission and determine the priority of conversions.

[0046] The transformation unit can adjust the order of transformations based on the relevance of the feedback during the transformation process. For example, the transformation unit may prioritize transforming feedback with high relevance. It can also transform feedback with moderate relevance next. It can also transform feedback with low relevance last. This allows transformations to be performed in an appropriate order according to the relevance of the feedback. Some or all of the above processing in the transformation unit may be performed using AI, for example, or without AI. For example, the transformation unit can use AI to evaluate the relevance of the feedback and adjust the order of transformations.

[0047] The feedback provider can select the optimal method of providing feedback by referring to the recipient's past feedback history when providing feedback. For example, the provider may prioritize providing feedback in a format that the recipient has preferred to receive in the past. The provider can also select the optimal timing for providing feedback based on the recipient's past feedback history. The provider can also analyze the recipient's past feedback history and provide appropriate feedback content. This allows the provider to provide feedback to the recipient in the most optimal way. Some or all of the above processes in the feedback provider may be performed using AI, or not. For example, the provider can analyze the recipient's past feedback history using AI and select the optimal method of providing feedback.

[0048] The feedback provider can customize the content of the feedback based on the evaluated person's current projects and areas of interest. For example, the provider can provide feedback related to the projects the evaluated person is currently working on. The provider can also provide relevant feedback based on the evaluated person's areas of interest. The provider can also provide appropriate feedback according to the evaluated person's current work. This allows for the provision of appropriate feedback tailored to the evaluated person's interests. Some or all of the above processing in the feedback provider may be performed using AI, for example, or without AI. For example, the provider can input data on the evaluated person's current projects and areas of interest into a generating AI and have the generating AI perform the process of customizing the content of the feedback.

[0049] The feedback delivery unit can select the optimal delivery method when providing feedback, taking into account the geographical location information of the person being evaluated. For example, if the person being evaluated is in the office, the feedback delivery unit can provide work-related feedback. For example, if the person being evaluated is in the office, the feedback delivery unit can provide work-related feedback. The feedback delivery unit can also provide feedback related to the destination of the business trip if the person being evaluated is on a business trip. For example, if the person being evaluated is at home, the feedback delivery unit can provide feedback related to remote work. For example, if the person being evaluated is at home, the feedback delivery unit can provide feedback related to remote work. This allows for the provision of appropriate feedback based on the location information of the person being evaluated. Some or all of the above processing in the feedback delivery unit may be performed using AI, for example, or without AI. For example, the feedback delivery unit can input the geographical location information of the person being evaluated into a generating AI and have the generating AI perform the process of selecting the optimal delivery method.

[0050] The service provider can analyze the social media activity of the person being evaluated and customize the content of the feedback provided. For example, the service provider can provide feedback related to projects shared by the person being evaluated on social media. The service provider can also provide relevant feedback based on the person's interests on social media. The service provider can also select and provide appropriate feedback from the person's social media activity. This allows for the provision of appropriate feedback based on the person's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the person's social media activity data into a generating AI and have the generating AI perform the process of customizing the content of the feedback.

[0051] The management department can optimize its management algorithm by referring to past feedback data when managing feedback history. For example, the management department can analyze past feedback data and apply the optimal management algorithm. The management department can also optimize its algorithm for maintaining feedback consistency based on past feedback data. For example, the management department can optimize its algorithm for maintaining feedback consistency based on past feedback data. The management department can also adjust its management algorithm to improve the quality of feedback based on past feedback data. For example, the management department can adjust its management algorithm to improve the quality of feedback based on past feedback data. This allows the management department to apply the optimal management algorithm based on past feedback data. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can analyze past feedback data using AI and optimize its management algorithm.

[0052] The management department can weight historical data based on the timing of feedback submission when managing feedback history. For example, the management department can weight historical data by giving more weight to recent feedback. Alternatively, the management department can weight periodic feedback equally. Alternatively, the management department can weight feedback related to specific events. This allows for the management of historical data with appropriate weighting according to the timing of feedback submission. Some or all of the above processing in the management department may be performed using AI, for example, or not. For example, the management department can use AI to evaluate the timing of feedback submission and weight the historical data accordingly.

[0053] The management department can classify historical data based on the feedback category when managing feedback history. For example, the management department can classify performance-related feedback as one category. The management department can also classify communication-related feedback as another category. The management department can also classify skill-related feedback as yet another category. This allows for the management of historical data with appropriate classification according to the feedback category. Some or all of the above processing in the management department may be performed using AI, for example, or not. For example, the management department can classify feedback categories using AI and manage historical data appropriately.

[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 feedback delivery unit can select the optimal delivery method by referring to the recipient's past feedback history when providing feedback. For example, it can prioritize providing feedback in a format the recipient has preferred to receive in the past. It can also select the optimal timing for delivery based on the recipient's past feedback history. Furthermore, it can analyze the recipient's past feedback history and provide appropriate feedback content. This allows feedback to be delivered to the recipient in the most optimal way. Some or all of the above processes in the feedback delivery unit may be performed using AI or not. For example, the feedback delivery unit can use AI to analyze the recipient's past feedback history and select the optimal delivery method.

[0056] The management department can optimize its management algorithms by referring to past feedback data when managing feedback history. For example, it can analyze past feedback data and apply the optimal management algorithm. It can also optimize algorithms to maintain feedback consistency based on past feedback data. Furthermore, it can adjust management algorithms to improve the quality of feedback based on past feedback data. This allows for the application of the optimal management algorithm based on past feedback data. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can analyze past feedback data using AI and optimize its management algorithms.

[0057] The input unit can analyze past feedback history and select the optimal input method. For example, it can prioritize suggesting input methods that the user has previously preferred (voice, text, etc.). It can also analyze the format of feedback previously entered by the user and provide the optimal template. Furthermore, it can suggest the optimal input method for a specific time period based on the user's past feedback history. This allows the user to be provided with the most suitable input method. Some or all of the above processing in the input unit may be performed using AI or not. For example, the input unit can analyze past feedback history using AI and select the optimal input method.

[0058] The conversion unit can adjust the level of detail in the conversion of feedback based on its importance. For example, important feedback can be converted with a detailed explanation. Minor feedback can be converted concisely. Furthermore, feedback of moderate importance can be converted with an appropriate level of detail. This allows for conversion with an appropriate level of detail according to the importance of the feedback. Some or all of the above processing in the conversion unit may be performed using AI or not. For example, the conversion unit can use AI to evaluate the importance of the feedback and adjust the level of detail in the conversion.

[0059] The feedback delivery unit can customize the content of the feedback provided based on the evaluated person's current projects and areas of interest. For example, it can provide feedback related to the projects the evaluated person is currently working on. It can also provide relevant feedback based on the evaluated person's areas of interest. Furthermore, it can provide appropriate feedback according to the evaluated person's current work content. This ensures that appropriate feedback is provided that is tailored to the evaluated person's interests. Some or all of the above processing in the feedback delivery unit may be performed using AI or not. For example, the feedback delivery unit can input data on the evaluated person's current projects and areas of interest into a generating AI and have the generating AI perform the process of customizing the content of the feedback.

[0060] The transformation unit can apply different transformation algorithms depending on the category of the feedback when transforming it. For example, in the case of performance-related feedback, it can perform transformations that include specific improvement measures. In the case of communication-related feedback, it can perform transformations that use softer language. Furthermore, in the case of skill-related feedback, it can perform transformations that include specific training suggestions. This allows for appropriate transformations according to the category of feedback. Some or all of the above processing in the transformation unit may be performed using AI or not. For example, the transformation unit can classify the feedback categories using AI and apply an appropriate transformation algorithm.

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

[0062] Step 1: The input section allows feedback to be entered. For example, the evaluator can enter feedback in text format. The input section also allows feedback to be entered using voice input. For example, the evaluator can enter feedback by voice and it can be converted to text using speech recognition technology. Furthermore, the input section can refer to past feedback history and reuse feedback previously entered by the evaluator. Step 2: The conversion unit transforms the feedback entered by the input unit into softer, more specific language. For example, the conversion unit can use AI to analyze the evaluator's feedback and transform it into appropriate language. For instance, if the evaluator inputs "Person B is not doing their job well at all," the conversion unit will transform it into a softer expression such as "Person B seems to be having some difficulties with a particular project." The conversion unit can also suggest specific improvement measures depending on the content of the feedback. For example, the conversion unit might suggest a specific improvement measure such as, "We recommend that Person B take XXX training to improve their project management skills." Step 3: The provider unit provides the feedback converted by the conversion unit to the person being evaluated. The provider unit can, for example, send the feedback to the person being evaluated via email. The provider unit can also provide a dedicated web portal where the person being evaluated can view the feedback. For example, the person being evaluated can log in to the web portal and view past feedback. Furthermore, the provider unit can send notifications to the person being evaluated depending on the content of the feedback. For example, the provider unit can send a notification to the person being evaluated when they receive new feedback. Step 4: The management department manages the feedback history. For example, the management department saves feedback previously provided by evaluators and makes it available for reference as needed. The management department can set the retention period and format for the feedback. For example, the management department can save feedback for a certain period and then automatically delete it. The management department can also set access permissions for the feedback. For example, the management department can set it so that only specific evaluators can access specific feedback.

[0063] (Example of form 2) The FeedHarmony Co-creation Multi-Facilitation Feedback System, according to an embodiment of the present invention, is a tool that utilizes AI to improve the quality of multi-faceted feedback. This system converts evaluators' feedback into soft and specific language and presents improvement suggestions. It provides evaluators with easily understandable and specific improvement measures and manages the feedback history. This system makes feedback specific and easy to accept, promoting employee growth. Evaluators can efficiently provide high-quality feedback, and evaluators can easily understand areas for improvement. As a result, overall workplace communication improves, and motivation and productivity increase. For example, if an evaluator inputs feedback such as, "Person B is not doing their job well at all. They are causing trouble for everyone around them," the AI ​​converts it into soft and specific language such as, "In Project X, there are many situations where Person B is able to utilize their strengths and perform well, but there seem to be cases where things are not going well in regular meetings, etc. You might want to try an approach like XXX." This makes it easier for evaluators to understand specific improvement measures, and evaluators can provide high-quality feedback. In addition, the AI ​​manages the feedback history, allowing evaluators to refer to past feedback. This makes it easier for evaluators to provide consistent feedback. Furthermore, the AI ​​provides evaluators with feedback points, leading to increased self-awareness. This system addresses the challenges of quality and efficiency in multi-rater feedback faced by business leaders and HR managers, and is expected to promote employee growth and improve overall workplace communication. Thus, FeedHarmony's multi-rater feedback system can promote employee growth and improve overall workplace communication.

[0064] The FeedHarmony collaborative multifaceted feedback system according to this embodiment comprises an input unit, a conversion unit, a provision unit, and a management unit. The input unit receives feedback. For example, the evaluator can input feedback in text format into the input unit. The input unit can also receive feedback using voice input. For example, the evaluator can input feedback by voice and convert it to text using speech recognition technology. Furthermore, the input unit can also reuse feedback previously entered by the evaluator by referring to past feedback history. The conversion unit converts the feedback entered by the input unit into softer, more specific expressions. For example, the conversion unit can analyze the evaluator's feedback using AI and convert it into an appropriate expression. For example, if the evaluator inputs "Person B is not doing their job well at all," the conversion unit converts it into a softer expression such as "Person B seems to have some difficulties with a particular project." The conversion unit can also suggest specific improvement measures depending on the content of the feedback. For example, the conversion unit suggests a specific improvement measure such as "Person B is advised to take XXX training to improve their project management skills." The provision unit provides the feedback converted by the conversion unit to the person being evaluated. The service provider can, for example, send feedback to the person being evaluated via email. The service provider can also provide a dedicated web portal where the person being evaluated can view their feedback. For example, the person being evaluated can log in to the web portal and view past feedback. Furthermore, the service provider can send notifications to the person being evaluated depending on the content of the feedback. For example, the service provider can send a notification to the person being evaluated when they receive new feedback. The management department manages the feedback history. For example, the management department can save feedback previously provided by evaluators and make it available for reference as needed. The management department can set the retention period and format for feedback. For example, the management department can save feedback for a certain period and then automatically delete it. The management department can also set access permissions for feedback.For example, the management department can configure the system so that only specific evaluators can access specific feedback. This allows the FeedHarmony collaborative multifaceted feedback system, according to this embodiment, to improve the quality of feedback and promote employee growth.

[0065] The input section is where feedback is entered. For example, the input section allows evaluators to enter feedback in text format. The input section also allows feedback to be entered using voice input. For example, the evaluator can enter feedback by voice and convert it to text using speech recognition technology. Specifically, the speech recognition technology analyzes the evaluator's voice in real time and converts it into accurate text data. This makes it easy for evaluators to enter feedback and significantly reduces the effort required for input. Furthermore, the input section can also refer to past feedback history and allow evaluators to reuse feedback they have previously entered. For example, by searching for feedback entered in the past and reusing it for similar situations, the consistency of feedback can be maintained. By integrating these functions, the input section provides an environment in which evaluators can enter feedback efficiently and effectively. In addition, the input section is designed with a user interface that is easy for evaluators to operate intuitively. For example, it has an interface that allows seamless switching between voice input and text input, and a function that allows easy searching of past feedback history. This allows evaluators to enter feedback without stress and improve the quality of feedback.

[0066] The conversion unit transforms the feedback entered by the input unit into softer, more concrete expressions. For example, the conversion unit can use AI to analyze the evaluator's feedback and transform it into appropriate expressions. Specifically, it uses natural language processing technology to analyze the evaluator's feedback and understand the context and intent. For example, if an evaluator inputs, "Person B is not doing their job well at all," the conversion unit analyzes the context and transforms it into a softer expression. The conversion unit transforms it into a softer expression such as, "It seems that Person B is having trouble with certain aspects of the project." The conversion unit can also suggest specific improvement measures depending on the content of the feedback. For example, the conversion unit might suggest, "We recommend that Person B take XXX training to improve their project management skills." In this way, the conversion unit can transform the evaluator's feedback into something more constructive and positive, and present it in a way that is more easily accepted by the person being evaluated. Furthermore, the conversion unit can analyze the content of the feedback and extract common issues and trends. For example, it can analyze feedback from multiple evaluators and identify common issues regarding specific skills or behaviors. This allows for an understanding of issues across the entire organization and the implementation of effective countermeasures. Through these functions, the conversion unit can improve the quality of feedback and promote the growth of those being evaluated.

[0067] The service provider delivers the feedback converted by the conversion unit to the person being evaluated. For example, the service provider can send the feedback to the person being evaluated via email. Specifically, the service provider automatically converts the converted feedback into email format and sends it to the person being evaluated's email address. The service provider can also provide a dedicated web portal where the person being evaluated can view their feedback. For example, the person being evaluated can log in to the web portal and view past feedback. The web portal has a user-friendly interface, allowing the person being evaluated to easily search for and view feedback. Furthermore, the service provider can send notifications to the person being evaluated depending on the content of the feedback. For example, the service provider can send a notification to the person being evaluated when they receive new feedback. This allows the person being evaluated to respond quickly without missing important feedback. Through these functions, the service provider provides an environment in which the person being evaluated can effectively receive and utilize feedback. Furthermore, the service provider can customize the method of providing feedback. For example, the method of providing feedback can be selected according to the person being evaluated's preferences. This allows the service provider to provide feedback in the form that is most acceptable to the person being evaluated, maximizing the effectiveness of the feedback.

[0068] The management department manages the feedback history. For example, the management department stores feedback previously provided by evaluators and makes it available for reference as needed. Specifically, the management department stores feedback in a database, making it easy for evaluators and those being evaluated to search for past feedback. The management department can set the retention period and storage format for feedback. For example, the management department can store feedback for a certain period and then automatically delete it. The management department can also set access permissions for feedback. For example, the management department can set it so that only specific evaluators can access specific feedback. This ensures the privacy and security of feedback. Furthermore, the management department can generate feedback statistics and understand feedback trends across the organization. For example, the management department can analyze the content and frequency of feedback to identify challenges and areas for improvement across the organization. This allows the management department to provide data to improve the quality of feedback and promote overall organizational growth. Through these functions, the management department can effectively manage and utilize feedback and improve the overall performance of the organization.

[0069] The service provider can provide specific improvement measures that are easy for the person being evaluated to understand. For example, the service provider might present specific improvement measures to the person being evaluated. For example, the service provider might present specific improvement measures such as, "We recommend that Mr. / Ms. B take the XXX training to improve their project management skills." The service provider can also provide resources for the person being evaluated to implement the improvement measures. For example, the service provider might provide the person being evaluated with a link to a training program. Furthermore, the service provider can record the results of the person being evaluated implementing the improvement measures as feedback. For example, the service provider might record the results as feedback when the person being evaluated completes the training program. This makes it easier for the person being evaluated to understand and implement the specific improvement measures. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can use AI to analyze the content of the feedback and propose appropriate improvement measures in order to present specific improvement measures to the person being evaluated.

[0070] The management department can enable evaluators to refer to past feedback. For example, the management department can save feedback previously provided by evaluators and make it available for reference as needed. The management department can set the retention period and format of the feedback. For example, the management department can save feedback for a certain period and then automatically delete it. The management department can also set access permissions for the feedback. For example, the management department can set it so that only specific evaluators can access specific feedback. Furthermore, the management department can provide a search function for feedback when evaluators refer to past feedback. For example, the management department can enable evaluators to search past feedback by entering specific keywords. This makes it easier for evaluators to provide consistent feedback. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can analyze past feedback using AI and provide it in a format that is easy for evaluators to refer to.

[0071] The conversion unit can transform the evaluator's feedback into softer, more specific language. For example, the conversion unit can use AI to analyze the evaluator's feedback and convert it into appropriate language. For instance, if the evaluator inputs "Person B is not doing their job well at all," the conversion unit will convert it into a softer expression such as "Person B seems to be having some difficulties with a particular project." The conversion unit can also suggest specific improvement measures depending on the content of the feedback. For example, the conversion unit might suggest a specific improvement measure such as, "We recommend that Person B take XXX training to improve their project management skills." This makes the feedback more specific and easier to accept. Some or all of the above processing in the conversion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the conversion unit can input the evaluator's feedback into a generative AI and have the generative AI perform the process of converting it into softer, more specific language.

[0072] The service provider can present feedback points to the evaluator. For example, the service provider might present key points of feedback to the evaluator. For example, it might present a point such as, "Feedback on B's project management skills is important." The service provider can also highlight key points when the evaluator enters feedback. For example, the service provider highlights key points on the feedback entry screen. Furthermore, the service provider can provide a function that allows the evaluator to refer to past feedback history when entering feedback. For example, the service provider displays feedback previously provided by the evaluator, making it easier to provide consistent feedback. This makes it easier for evaluators to provide high-quality feedback. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can use AI to analyze the content of feedback and extract key points in order to present feedback points to the evaluator.

[0073] The management department can manage the history to maintain the consistency of feedback. For example, the management department can save feedback previously provided by evaluators and make it available for reference as needed. The management department can set the retention period and format of the feedback. For example, the management department can save feedback for a certain period and then automatically delete it. The management department can also set access permissions for the feedback. For example, the management department can set it so that only specific evaluators can access specific feedback. Furthermore, the management department can provide a feedback search function when evaluators refer to past feedback. For example, the management department can allow evaluators to search past feedback by entering specific keywords. This ensures the consistency of feedback. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can analyze past feedback using AI and provide it in a format that is easy for evaluators to refer to.

[0074] The input unit can estimate the user's emotions and adjust the timing of feedback input based on the estimated emotions. For example, if the user is feeling stressed, the input unit can prompt for feedback input during a time when the user is relaxed. For example, the input unit can detect when the user is relaxed and prompt for feedback input during that time. The input unit can also leverage the user's concentration to prompt for feedback input when the user is focused. For example, the input unit can detect when the user is focused and prompt for feedback input during that time. The input unit can also prompt for feedback input after a break if the user is tired. For example, the input unit can detect when the user is tired and prompt for feedback input after a break. This allows feedback to be input at an appropriate time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 input unit may be performed using AI, for example, or without AI. For example, the input unit can input user emotion data into a generating AI, allowing the AI ​​to perform emotion estimation.

[0075] The input unit can analyze past feedback history and select the optimal input method. For example, the input unit can prioritize suggesting input methods (voice, text, etc.) that the user has preferred in the past. For example, if the user has preferred voice input in the past, the input unit will prioritize suggesting voice input. The input unit can also analyze the format of feedback previously entered by the user and provide the optimal template. For example, the input unit can analyze feedback templates previously used by the user and provide the optimal template. Furthermore, the input unit can suggest the optimal input method for a specific time period based on the user's past feedback history. For example, the input unit can analyze the user's history of feedback entered during a specific time period and suggest the optimal input method for that time period. This allows the user to be provided with the most suitable input method. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can analyze past feedback history using AI and select the optimal input method.

[0076] The input unit can filter feedback input based on the user's current projects and areas of interest. For example, the input unit can prioritize inputting feedback related to the user's current projects. The input unit can also filter and input relevant feedback based on the user's areas of interest. The input unit can also select and input appropriate feedback according to the user's current work. This allows for the input of feedback tailored to the user's interests. Some or all of the above processing in the input unit may be performed using AI, or not. For example, the input unit can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the feedback filtering.

[0077] The input unit can estimate the user's emotions and determine the priority of the feedback to input based on the estimated emotions. For example, if the user is stressed, the input unit will prioritize positive feedback. For example, if the user is relaxed, the input unit will prioritize feedback on areas for improvement. For example, if the user is relaxed, the input unit will prioritize feedback on areas for improvement. For example, if the user is in a hurry, the input unit will prioritize important feedback. For example, if the user is in a hurry, the input unit will prioritize important feedback. This allows feedback to be input with priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 input unit may be performed using AI, for example, or without AI. For example, the input unit can input user emotion data into a generating AI, allowing the AI ​​to perform emotion estimation.

[0078] The input unit can prioritize inputting highly relevant feedback when inputting feedback, taking into account the user's geographical location information. For example, if the user is in the office, the input unit can prioritize inputting work-related feedback. For example, if the user is on a business trip, the input unit can prioritize inputting feedback related to the destination. For example, if the user is on a business trip, the input unit can prioritize inputting feedback related to the destination. For example, if the user is at home, the input unit can prioritize inputting feedback related to remote work. This allows for the input of appropriate feedback based on the user's location information. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's geographical location information into a generating AI and cause the generating AI to perform the process of prioritizing the input of highly relevant feedback.

[0079] The input unit can analyze the user's social media activity and input relevant feedback when feedback is entered. For example, the input unit can input feedback related to projects the user has shared on social media. The input unit can also input relevant feedback based on the user's interests on social media. For example, the input unit can input relevant feedback based on the user's interests on social media. The input unit can also select and input appropriate feedback from the user's social media activity. For example, the input unit can select and input appropriate feedback from the user's social media activity. This allows for the input of feedback based on the user's social media activity. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's social media activity data into a generating AI and have the generating AI perform the process of inputting relevant feedback.

[0080] The transformation unit can estimate the user's emotions and adjust the way feedback is expressed based on the estimated emotions. For example, if the user is relaxed, the transformation unit can provide feedback in a gentle manner. For example, if the user is relaxed, the transformation unit can provide feedback in a gentle manner. The transformation unit can also provide feedback in a positive manner if the user is stressed. For example, if the user is stressed, the transformation unit can provide feedback in a positive manner. The transformation unit can also provide feedback in a concise and specific manner if the user is in a hurry. For example, if the user is in a hurry, the transformation unit can provide feedback in a concise and specific manner. This allows for the provision of feedback in an appropriate manner according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the transformation unit may be performed using AI, for example, or without AI. For example, the conversion unit can input user emotion data into the generating AI and have the generating AI perform a process to adjust how the feedback is expressed.

[0081] The transformation unit can adjust the level of detail in the transformation based on the importance of the feedback. For example, in the case of important feedback, the transformation unit can perform a transformation that includes a detailed explanation. For example, in the case of important feedback, the transformation unit can perform a transformation that includes a detailed explanation. The transformation unit can also perform a transformation that is concise in the case of minor feedback. For example, in the case of minor feedback, the transformation unit can perform a transformation with an appropriate level of detail in the case of moderately important feedback. For example, in the case of moderately important feedback, the transformation unit can perform a transformation with an appropriate level of detail according to the importance of the feedback. Some or all of the above processing in the transformation unit may be performed using AI, for example, or without AI. For example, the transformation unit can evaluate the importance of the feedback using AI and adjust the level of detail in the transformation.

[0082] The transformation unit can apply different transformation algorithms depending on the category of the feedback when transforming it. For example, in the case of performance-related feedback, the transformation unit can perform a transformation that includes specific improvement measures. For example, in the case of communication-related feedback, the transformation unit can perform a transformation that includes specific improvement measures. For example, in the case of skill-related feedback, the transformation unit can perform a transformation that includes specific training suggestions. This allows for appropriate transformation according to the category of the feedback. Some or all of the above processing in the transformation unit may be performed using AI, for example, or without AI. For example, the transformation unit can classify the categories of feedback using AI and apply an appropriate transformation algorithm.

[0083] The transformation unit can estimate the user's emotions and adjust the length of the feedback based on the estimated emotions. For example, if the user is relaxed, the transformation unit can provide detailed feedback. For example, if the user is relaxed, the transformation unit can provide detailed feedback. For example, if the user is stressed, the transformation unit can provide concise feedback. For example, if the user is in a hurry, the transformation unit can provide short, to the point, feedback. For example, if the user is in a hurry, the transformation unit can provide short, to the point, feedback. This allows for the provision of feedback of an appropriate length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the transformation unit may be performed using AI, for example, or without AI. For example, the transformation unit can input user emotion data into the generative AI and have the generative AI perform the process of adjusting the length of the feedback.

[0084] The conversion unit can determine the priority of conversions based on the timing of feedback submission when converting feedback. For example, the conversion unit can perform conversions with the highest priority for urgent feedback. For example, the conversion unit can perform conversions with the normal priority for regular feedback. For example, the conversion unit can perform conversions with the normal priority for regular feedback. For example, the conversion unit can postpone the conversion of past feedback. For example, the conversion unit can postpone the conversion of past feedback. This allows for conversions to be performed with appropriate priority according to the timing of feedback submission. Some or all of the above processing in the conversion unit may be performed using AI, or not. For example, the conversion unit can use AI to evaluate the timing of feedback submission and determine the priority of conversions.

[0085] The transformation unit can adjust the order of transformations based on the relevance of the feedback during the transformation process. For example, the transformation unit may prioritize transforming feedback with high relevance. It can also transform feedback with moderate relevance next. It can also transform feedback with low relevance last. This allows transformations to be performed in an appropriate order according to the relevance of the feedback. Some or all of the above processing in the transformation unit may be performed using AI, for example, or without AI. For example, the transformation unit can use AI to evaluate the relevance of the feedback and adjust the order of transformations.

[0086] The service provider can estimate the user's emotions and adjust the method of providing feedback based on the estimated emotions. For example, if the user is relaxed, the service provider can provide detailed feedback. For example, if the user is relaxed, the service provider can provide detailed feedback. The service provider can also provide positive feedback if the user is stressed. For example, if the user is stressed, the service provider can provide positive feedback. The service provider can also provide concise feedback if the user is in a hurry. For example, if the user is in a hurry, the service provider can provide concise feedback. This allows for the provision of feedback in an appropriate manner according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the process of adjusting the method of providing feedback.

[0087] The feedback provider can select the optimal method of providing feedback by referring to the recipient's past feedback history when providing feedback. For example, the provider may prioritize providing feedback in a format that the recipient has preferred to receive in the past. The provider can also select the optimal timing for providing feedback based on the recipient's past feedback history. The provider can also analyze the recipient's past feedback history and provide appropriate feedback content. This allows the provider to provide feedback to the recipient in the most optimal way. Some or all of the above processes in the feedback provider may be performed using AI, or not. For example, the provider can analyze the recipient's past feedback history using AI and select the optimal method of providing feedback.

[0088] The feedback provider can customize the content of the feedback based on the evaluated person's current projects and areas of interest. For example, the provider can provide feedback related to the projects the evaluated person is currently working on. The provider can also provide relevant feedback based on the evaluated person's areas of interest. The provider can also provide appropriate feedback according to the evaluated person's current work. This allows for the provision of appropriate feedback tailored to the evaluated person's interests. Some or all of the above processing in the feedback provider may be performed using AI, for example, or without AI. For example, the provider can input data on the evaluated person's current projects and areas of interest into a generating AI and have the generating AI perform the process of customizing the content of the feedback.

[0089] The service provider can estimate the user's emotions and determine the order in which to provide feedback based on the estimated emotions. For example, if the user is relaxed, the service provider may prioritize providing detailed feedback. For example, if the user is relaxed, the service provider may prioritize providing detailed feedback. The service provider may also prioritize providing positive feedback if the user is stressed. For example, if the user is stressed, the service provider may prioritize providing positive feedback. The service provider may also prioritize providing concise feedback if the user is in a hurry. For example, if the service provider is in a hurry, the service provider may prioritize providing concise feedback. This allows feedback to be provided in an appropriate order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generating AI and have the AI ​​perform the process of determining the order in which feedback is provided.

[0090] The feedback delivery unit can select the optimal delivery method when providing feedback, taking into account the geographical location information of the person being evaluated. For example, if the person being evaluated is in the office, the feedback delivery unit can provide work-related feedback. For example, if the person being evaluated is in the office, the feedback delivery unit can provide work-related feedback. The feedback delivery unit can also provide feedback related to the destination of the business trip if the person being evaluated is on a business trip. For example, if the person being evaluated is at home, the feedback delivery unit can provide feedback related to remote work. For example, if the person being evaluated is at home, the feedback delivery unit can provide feedback related to remote work. This allows for the provision of appropriate feedback based on the location information of the person being evaluated. Some or all of the above processing in the feedback delivery unit may be performed using AI, for example, or without AI. For example, the feedback delivery unit can input the geographical location information of the person being evaluated into a generating AI and have the generating AI perform the process of selecting the optimal delivery method.

[0091] The service provider can analyze the social media activity of the person being evaluated and customize the content of the feedback provided. For example, the service provider can provide feedback related to projects shared by the person being evaluated on social media. The service provider can also provide relevant feedback based on the person being evaluated's interests on social media. The service provider can also select and provide appropriate feedback from the person being evaluated's social media activities. This allows for the provision of appropriate feedback based on the person being evaluated's social media activities. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the person being evaluated's social media activity data into a generating AI and have the generating AI perform the process of customizing the content of the feedback.

[0092] The management unit can estimate the user's emotions and adjust how the feedback history is managed based on the estimated emotions. For example, if the user is relaxed, the management unit can display a detailed feedback history. For example, if the user is relaxed, the management unit can display a detailed feedback history. The management unit can also prioritize displaying positive feedback history if the user is stressed. For example, if the user is in a hurry, the management unit can display a concise feedback history. For example, if the user is in a hurry, the management unit can display a concise feedback history. This allows the feedback history to be managed in an appropriate manner according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 management unit may be performed using AI, for example, or without AI. For example, the management department can input user emotion data into a generating AI and have the AI ​​perform a process to adjust the management method of the feedback history.

[0093] The management department can optimize its management algorithm by referring to past feedback data when managing feedback history. For example, the management department can analyze past feedback data and apply the optimal management algorithm. The management department can also optimize its algorithm for maintaining feedback consistency based on past feedback data. For example, the management department can optimize its algorithm for maintaining feedback consistency based on past feedback data. The management department can also adjust its management algorithm to improve the quality of feedback based on past feedback data. For example, the management department can adjust its management algorithm to improve the quality of feedback based on past feedback data. This allows the management department to apply the optimal management algorithm based on past feedback data. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can analyze past feedback data using AI and optimize its management algorithm.

[0094] The management unit can estimate the user's emotions and adjust how the feedback history is displayed based on the estimated emotions. For example, if the user is relaxed, the management unit can display a detailed feedback history. For example, if the user is relaxed, the management unit can display a detailed feedback history. The management unit can also prioritize displaying positive feedback history if the user is stressed. For example, if the user is in a hurry, the management unit can display a concise feedback history. For example, if the user is in a hurry, the management unit can display a concise feedback history. This allows the feedback history to be displayed in an appropriate manner according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 management unit may be performed using AI, for example, or without AI. For example, the management department can input user emotion data into a generating AI and have the AI ​​perform a process to adjust how the feedback history is displayed.

[0095] The management department can weight historical data based on the timing of feedback submission when managing feedback history. For example, the management department can weight historical data by giving more weight to recent feedback. Alternatively, the management department can weight periodic feedback equally. Alternatively, the management department can weight feedback related to specific events. This allows for the management of historical data with appropriate weighting according to the timing of feedback submission. Some or all of the above processing in the management department may be performed using AI, for example, or not. For example, the management department can use AI to evaluate the timing of feedback submission and weight the historical data accordingly.

[0096] The management department can classify historical data based on the feedback category when managing feedback history. For example, the management department can classify performance-related feedback as one category. The management department can also classify communication-related feedback as another category. The management department can also classify skill-related feedback as yet another category. This allows for the management of historical data with appropriate classification according to the feedback category. Some or all of the above processing in the management department may be performed using AI, for example, or not. For example, the management department can classify feedback categories using AI and manage historical data appropriately.

[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 input unit can estimate the user's emotions and adjust the timing of feedback input based on the estimated emotions. For example, if the user is feeling stressed, feedback input can be prompted during a time when they can relax. If the user is concentrating, feedback input can be prompted to take advantage of that concentration. Furthermore, if the user is tired, feedback input can be prompted after a break. This allows feedback to be input at an appropriate time 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 above processing in the input unit may be performed using AI or not. For example, the input unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0099] The feedback delivery unit can select the optimal delivery method by referring to the recipient's past feedback history when providing feedback. For example, it can prioritize providing feedback in a format the recipient has preferred to receive in the past. It can also select the optimal timing for delivery based on the recipient's past feedback history. Furthermore, it can analyze the recipient's past feedback history and provide appropriate feedback content. This allows feedback to be delivered to the recipient in the most optimal way. Some or all of the above processes in the feedback delivery unit may be performed using AI or not. For example, the feedback delivery unit can use AI to analyze the recipient's past feedback history and select the optimal delivery method.

[0100] The transformation unit can estimate the user's emotions and adjust the way feedback is expressed based on the estimated emotions. For example, if the user is relaxed, feedback can be provided in a gentle manner. If the user is stressed, feedback can be provided in a positive manner. Furthermore, if the user is in a hurry, feedback can be provided in a concise and specific manner. This allows for the provision of feedback in an appropriate manner 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 above processing in the transformation unit may be performed using AI or not. For example, the transformation unit can input user emotion data into the generative AI and have the generative AI perform the process of adjusting the way feedback is expressed.

[0101] The management department can optimize its management algorithms by referring to past feedback data when managing feedback history. For example, it can analyze past feedback data and apply the optimal management algorithm. It can also optimize algorithms to maintain feedback consistency based on past feedback data. Furthermore, it can adjust management algorithms to improve the quality of feedback based on past feedback data. This allows for the application of the optimal management algorithm based on past feedback data. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can analyze past feedback data using AI and optimize its management algorithms.

[0102] The service provider can estimate the user's emotions and adjust the method of providing feedback based on the estimated emotions. For example, if the user is relaxed, detailed feedback can be provided. If the user is stressed, positive feedback can be provided. Furthermore, if the user is in a hurry, concise feedback can be provided. This allows for the provision of feedback in an appropriate manner according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the process of adjusting the method of providing feedback.

[0103] The input unit can analyze past feedback history and select the optimal input method. For example, it can prioritize suggesting input methods that the user has previously preferred (voice, text, etc.). It can also analyze the format of feedback previously entered by the user and provide the optimal template. Furthermore, it can suggest the optimal input method for a specific time period based on the user's past feedback history. This allows the user to be provided with the most suitable input method. Some or all of the above processing in the input unit may be performed using AI or not. For example, the input unit can analyze past feedback history using AI and select the optimal input method.

[0104] The conversion unit can adjust the level of detail in the conversion of feedback based on its importance. For example, important feedback can be converted with a detailed explanation. Minor feedback can be converted concisely. Furthermore, feedback of moderate importance can be converted with an appropriate level of detail. This allows for conversion with an appropriate level of detail according to the importance of the feedback. Some or all of the above processing in the conversion unit may be performed using AI or not. For example, the conversion unit can use AI to evaluate the importance of the feedback and adjust the level of detail in the conversion.

[0105] The feedback delivery unit can customize the content of the feedback provided based on the evaluated person's current projects and areas of interest. For example, it can provide feedback related to the projects the evaluated person is currently working on. It can also provide relevant feedback based on the evaluated person's areas of interest. Furthermore, it can provide appropriate feedback according to the evaluated person's current work content. This ensures that appropriate feedback is provided that is tailored to the evaluated person's interests. Some or all of the above processing in the feedback delivery unit may be performed using AI or not. For example, the feedback delivery unit can input data on the evaluated person's current projects and areas of interest into a generating AI and have the generating AI perform the process of customizing the content of the feedback.

[0106] The management unit can estimate the user's emotions and adjust the management method of the feedback history based on the estimated user emotions. For example, if the user is relaxed, a detailed feedback history can be displayed. If the user is stressed, positive feedback history can be prioritized. Furthermore, if the user is in a hurry, a concise feedback history can be displayed. This allows the feedback history to be managed in an appropriate manner 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 management unit may be performed using AI or not. For example, the management unit can input user emotion data into a generative AI and have the generative AI perform the process of adjusting the management method of the feedback history.

[0107] The transformation unit can apply different transformation algorithms depending on the category of the feedback when transforming it. For example, in the case of performance-related feedback, it can perform transformations that include specific improvement measures. In the case of communication-related feedback, it can perform transformations that use softer language. Furthermore, in the case of skill-related feedback, it can perform transformations that include specific training suggestions. This allows for appropriate transformations according to the category of feedback. Some or all of the above processing in the transformation unit may be performed using AI or not. For example, the transformation unit can classify the feedback categories using AI and apply an appropriate transformation algorithm.

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

[0109] Step 1: The input section allows feedback to be entered. For example, the evaluator can enter feedback in text format. The input section also allows feedback to be entered using voice input. For example, the evaluator can enter feedback by voice and it can be converted to text using speech recognition technology. Furthermore, the input section can refer to past feedback history and reuse feedback previously entered by the evaluator. Step 2: The conversion unit transforms the feedback entered by the input unit into softer, more specific language. For example, the conversion unit can use AI to analyze the evaluator's feedback and transform it into appropriate language. For instance, if the evaluator inputs "Person B is not doing their job well at all," the conversion unit will transform it into a softer expression such as "Person B seems to be having some difficulties with a particular project." The conversion unit can also suggest specific improvement measures depending on the content of the feedback. For example, the conversion unit might suggest a specific improvement measure such as, "We recommend that Person B take XXX training to improve their project management skills." Step 3: The provider unit provides the feedback converted by the conversion unit to the person being evaluated. The provider unit can, for example, send the feedback to the person being evaluated via email. The provider unit can also provide a dedicated web portal where the person being evaluated can view the feedback. For example, the person being evaluated can log in to the web portal and view past feedback. Furthermore, the provider unit can send notifications to the person being evaluated depending on the content of the feedback. For example, the provider unit can send a notification to the person being evaluated when they receive new feedback. Step 4: The management department manages the feedback history. For example, the management department saves feedback previously provided by evaluators and makes it available for reference as needed. The management department can set the retention period and format for the feedback. For example, the management department can save feedback for a certain period and then automatically delete it. The management department can also set access permissions for the feedback. For example, the management department can set it so that only specific evaluators can access specific feedback.

[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] Each of the multiple elements described above, including the input unit, conversion unit, provision unit, and management unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the input unit can input feedback using the receiving device 38 of the smart device 14. The conversion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the feedback using AI and converts it into an appropriate expression. The provision unit provides feedback to the person being evaluated using, for example, the output device 40 of the smart device 14. The management unit stores the feedback history in the database 24 of the data processing unit 12, for example, and makes it available for reference as needed. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[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] Each of the multiple elements described above, including the input unit, conversion unit, provision unit, and management unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the input unit can perform voice input using the microphone 238 of the smart glasses 214. The conversion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the feedback using AI and converts it into an appropriate expression. The provision unit provides feedback to the person being evaluated using, for example, the speaker 240 of the smart glasses 214. The management unit stores the feedback history in the database 24 of the data processing unit 12, for example, and makes it available for reference as needed. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes 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] Each of the multiple elements described above, including the input unit, conversion unit, provision unit, and management unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the input unit can perform voice input using the microphone 238 of the headset terminal 314. The conversion unit is implemented in the specific processing unit 290 of the data processing unit 12, which uses AI to analyze the feedback and convert it into an appropriate expression. The provision unit provides feedback to the person being evaluated using the display 343 of the headset terminal 314. The management unit stores the feedback history in the database 24 of the data processing unit 12, for example, and makes it available for reference as needed. The correspondence between each unit and the device or control unit is not limited to the example 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] Each of the multiple elements described above, including the input unit, conversion unit, provision unit, and management unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the input unit can perform voice input using the microphone 238 of the robot 414. The conversion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the feedback using AI and converts it into an appropriate expression. The provision unit provides feedback to the person being evaluated using, for example, the speaker 240 of the robot 414. The management unit stores the feedback history in the database 24 of the data processing unit 12, for example, and makes it available for reference as needed. The correspondence between each unit and the device or control unit is not limited to the example 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) An input section for receiving feedback, A conversion unit that converts the feedback input by the aforementioned input unit into a soft and concrete expression, A providing unit that provides the feedback converted by the conversion unit to the person being evaluated, It includes a management unit for managing feedback history. A system characterized by the following features. (Note 2) The aforementioned supply unit is, Provide specific improvement measures that are easy for the person being evaluated to understand. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned management department, Allow evaluators to refer to past feedback. The system described in Appendix 1, characterized by the features described herein. (Note 4) The conversion unit is Transform evaluator feedback into softer, more concrete language. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide the evaluator with points to provide feedback on. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned management department, Manage the history to maintain consistency in feedback. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned input unit is It estimates the user's emotions and adjusts the timing of feedback input based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned input unit is Analyze past feedback history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned input unit is When submitting feedback, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned input unit is It estimates the user's emotions and determines the priority of feedback input based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned input unit is When users submit feedback, the system prioritizes submitting highly relevant feedback by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned input unit is When users submit feedback, the system analyzes their social media activity and inputs relevant feedback. The system described in Appendix 1, characterized by the features described herein. (Note 13) The conversion unit is It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The conversion unit is When converting feedback, adjust the level of detail in the conversion based on the importance of the feedback. The system described in Appendix 1, characterized by the features described herein. (Note 15) The conversion unit is When transforming feedback, different transformation algorithms are applied depending on the feedback category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The conversion unit is It estimates the user's emotions and adjusts the length of the feedback based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The conversion unit is When converting feedback, the conversion priority is determined based on when the feedback was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The conversion unit is When transforming feedback, adjust the order of transformations based on the relevance of the feedback. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and adjusts how feedback is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing feedback, the system will refer to the recipient's past feedback history to select the most appropriate method of delivery. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing feedback, customize the content based on the evaluator's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and determines the order in which feedback is provided based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing feedback, the most suitable method of delivery will be selected, taking into account the geographical location of the person being evaluated. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing feedback, analyze the social media activity of the person being evaluated to customize the content of the feedback. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned management department, We estimate the user's emotions and adjust how feedback history is managed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned management department, When managing feedback history, refer to past feedback data to optimize the management algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned management department, It estimates the user's emotions and adjusts how the feedback history is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned management department, When managing feedback history, weight the historical data based on when the feedback was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned management department, When managing feedback history, categorize the historical data based on the feedback category. 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. An input section for receiving feedback, A conversion unit that converts the feedback input by the aforementioned input unit into a soft and concrete expression, A providing unit that provides the feedback converted by the conversion unit to the person being evaluated, It includes a management unit for managing feedback history. A system characterized by the following features.

2. The aforementioned supply unit is, Provide specific improvement measures that are easy for the person being evaluated to understand. The system according to feature 1.

3. The aforementioned management department, Allow evaluators to refer to past feedback. The system according to feature 1.

4. The conversion unit is Transform evaluator feedback into softer, more concrete language. The system according to feature 1.

5. The aforementioned supply unit is, Provide the evaluator with points to provide feedback on. The system according to feature 1.

6. The aforementioned management department, Manage the history to maintain consistency in feedback. The system according to feature 1.

7. The aforementioned input unit is It estimates the user's emotions and adjusts the timing of feedback input based on the estimated user emotions. The system according to feature 1.

8. The aforementioned input unit is Analyze past feedback history and select the optimal input method. The system according to feature 1.

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