System

Abstract

To provide a system capable of supporting a manager to provide appropriate feedback.SOLUTION: A system in an embodiment includes a collection unit, an analysis unit, a generation unit, and a providing unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates feedback content based on the analysis results obtained by the analysis unit. The providing unit provides the feedback content generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Current technology does not adequately assist managers in providing appropriate feedback, and there is room for improvement.

[0005] The system according to the embodiment aims to assist managers in providing appropriate feedback. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates feedback content based on the analysis result obtained by the analysis unit. The provision unit provides the feedback content generated by the generation unit. Effect of the Invention

[0007] The system according to the embodiment can assist managers in providing appropriate feedback. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The feedback training system according to the embodiment of the present invention is a system that has a training function for managers to provide appropriate feedback. This feedback training system is useful in MBO (management by objectives) and 1on1 meetings, and can be used to develop the capabilities of not only evaluators but also those being evaluated. For example, in MBO, the system displays recommended feedback content by inputting not only the current evaluation, but also the most recent evaluation, years of service, and the characteristics of the person being evaluated. If the person being evaluated is a new employee, specific advice on improving skills and short-term goal setting may be recommended. On the other hand, if the person being evaluated is a veteran employee with a long history of service, feedback on leadership and long-term career plans is recommended. Next, in 1on1 meetings, feedback content with a view to the next few months is displayed by inputting self-declaration, past evaluations, worries, and characteristics. If the person being evaluated is worried about his / her current work, specific advice and support to resolve the worries are recommended. In addition, feedback to increase motivation and challenging goal setting to promote growth may be proposed depending on the characteristics of the person being evaluated. In this way, the training function for managers to provide appropriate feedback is beneficial for both the evaluator and the person being evaluated. By providing feedback that is tailored to the characteristics and circumstances of the appraisee, the appraiser supports the appraisee's growth and contributes to improving the performance of the entire organization. Meanwhile, by receiving specific and practical feedback, the appraisee can take concrete steps toward developing their own capabilities and realizing their career plans. This allows the feedback training system to provide a training function for managers to provide appropriate feedback.

[0029] The feedback training system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data. The data includes, for example, text data, numerical data, image data, etc., but is not limited to such examples. The collection unit collects, for example, data from the evaluator and the evaluee. The evaluator and the evaluee include, for example, employees, students, customers, etc., but is not limited to such examples. The collection unit can also collect the self-report of the evaluee and the evaluation data of the past several times. For example, the self-report data is collected through a questionnaire or an interview, and the evaluation data for the past one year is collected. The analysis unit analyzes the collected data. The analysis includes, for example, statistical analysis and machine learning algorithms, but is not limited to such examples. The analysis unit analyzes the collected data and grasps the characteristics of the evaluator and the evaluee. The characteristics include, for example, skills, performance, behavioral characteristics, etc., but is not limited to such examples. The generation unit generates feedback content based on the analysis result. The feedback content includes, for example, text feedback and numerical evaluation, but is not limited to such examples. The generation unit generates optimal feedback content for the evaluator and the evaluee based on the analysis result. For example, if the evaluee is a new employee, specific advice on skill improvement is provided. If the evaluee is a veteran employee, feedback on leadership is provided. The provision unit provides the generated feedback content. The provision includes, for example, email, dashboard, report, etc., but is not limited to such examples. The provision unit provides the generated feedback content to the evaluator and the evaluee. For example, the feedback content is sent by email. The feedback content is displayed through a dashboard. The feedback content is provided in a report format. This allows the feedback training system according to the embodiment to efficiently collect and analyze data, generate and provide feedback content. Some or all of the above-mentioned processes in the collection unit, analysis unit, generation unit, and provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can collect data from the evaluator and the evaluee using AI.The analysis unit can analyze the collected data using AI. The generation unit can generate feedback content based on the analysis result using AI. The provision unit can provide the generated feedback content using AI.

[0030] The collection unit collects data. Examples of the data include, but are not limited to, text data, numerical data, and image data. The collection unit collects data from, for example, evaluators and evaluators. Examples of the evaluators and evaluators include, but are not limited to, employees, students, and customers. The collection unit can also collect self-reports from evaluators and the past several evaluations. For example, self-reported data is collected through questionnaires and interviews, and evaluation data from the past year is collected. The collection unit can utilize online forms and mobile applications to efficiently collect these data. For example, a system can be created in which employees enter self-evaluations using smartphones and the data is automatically sent to a cloud server. In addition, feedback and evaluation results given by evaluators to evaluators are also collected in digital format. In addition, the collection unit can obtain data from external data sources. For example, industry standard benchmark data and market research data can be collected and integrated with internal data to enable a more comprehensive evaluation. It is also important for the collection unit to carry out data verification and cleaning processes to ensure data quality. For example, duplicate data can be deleted and incomplete data can be completed so that the analysis unit can use accurate data. This enables the collection unit to efficiently collect high-quality data from a variety of data sources, improving the accuracy and reliability of the entire system.

[0031] The analysis unit analyzes the collected data. Examples of the analysis include, but are not limited to, statistical analysis and machine learning algorithms. The analysis unit analyzes the collected data to grasp the characteristics of the evaluator and the evaluee. Examples of the characteristics include, but are not limited to, skills, performance, behavioral characteristics, and the like. The analysis unit first preprocesses the data, complements missing values, and detects outliers. Next, the analysis unit grasps the basic trends and distribution of the data using statistical analysis. For example, the average value and standard deviation of the skill level of the evaluee are calculated to evaluate the overall performance. In addition, the analysis unit uses a machine learning algorithm to extract patterns of the behavioral characteristics and performance of the evaluee. For example, a clustering method is used to classify the evaluee into multiple groups, and the characteristics of each group are analyzed. Furthermore, the analysis unit can build a prediction model and predict the future performance and skill improvement of the evaluee. For example, regression analysis is used to predict future performance from the evaluee's past data, and basic information is provided for providing appropriate feedback. It is also important for the analysis unit to visualize these analysis results and provide them in a format that is easy for the evaluator and the evaluee to understand. For example, graphs and charts can be used to visually show trends and patterns in the data, facilitating understanding of the analysis results. This allows the analysis department to analyze the collected data from multiple angles and accurately grasp the characteristics of the evaluators and those being evaluated.

[0032] The generation unit generates feedback content based on the analysis result. The feedback content includes, for example, text feedback and numerical evaluation, but is not limited to such examples. The generation unit generates optimal feedback content for the evaluator and the evaluee based on the analysis result. For example, if the evaluee is a new employee, specific skill improvement advice is provided. If the evaluee is a veteran employee, feedback on leadership is provided. The generation unit can automatically generate text feedback based on the analysis result using natural language processing technology. For example, based on the skill evaluation result of the evaluee, feedback that points out specific improvements and strengths is generated. In addition, the performance of the evaluee can be quantitatively evaluated using numerical evaluation and visually shown using graphs and charts. Furthermore, the generation unit can provide customized feedback according to individual needs. For example, individual goal setting and action plans are proposed taking into account the evaluee's past evaluation history and self-reported data. The generation unit can also use pre-defined templates and guidelines to ensure consistency and quality of the feedback content. This makes it possible for the generated feedback to be beneficial to the evaluator and the evaluee and provide specific guidelines for action. The generator can provide feedback efficiently and quickly by automating the process of generating feedback contents.

[0033] The providing unit provides the generated feedback content. Examples of the provision include, but are not limited to, email, dashboard, and report. The providing unit provides the generated feedback content to the evaluator and the evaluee. For example, the feedback content is sent by email. The feedback content is displayed through a dashboard. The feedback content is provided in the form of a report. The providing unit can utilize multiple communication means to deliver the feedback content quickly and reliably. For example, not only email notifications but also push notifications of mobile apps and SMS are used in combination to deliver important feedback reliably. In addition, the providing unit can provide an interactive dashboard to display the feedback content in a visually easy-to-understand manner. For example, the evaluee can check his / her evaluation results in graphs and charts and click to display detailed feedback content. Furthermore, the providing unit can also have a function to collect confirmation of receipt of the feedback content and responses to the feedback. For example, an interface is provided that allows the evaluee to leave comments on the feedback content or send additional questions. This allows the providing unit to monitor the receipt status and effectiveness of the feedback and provide additional support as necessary. The providing unit streamlines the process of providing the feedback content, allowing the evaluator and the evaluee to receive feedback quickly.

[0034] The collection unit can collect data from the evaluator or the person being evaluated. The collection unit, for example, collects data from the evaluator or the person being evaluated. The evaluator or the person being evaluated includes, for example, employees, students, customers, etc., but is not limited to such examples. The collection unit can use questionnaires or interviews to efficiently collect data from the evaluator or the person being evaluated. For example, the collection unit collects data from the evaluator or the person being evaluated through an online questionnaire. The collection unit can also collect data from the evaluator or the person being evaluated through a face-to-face interview. This allows the collection unit to efficiently collect data from the evaluator or the person being evaluated. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can collect data from the evaluator or the person being evaluated using AI.

[0035] The analysis unit can analyze the collected data and grasp the characteristics of the evaluator or the evaluee. The analysis unit, for example, analyzes the collected data and grasps the characteristics of the evaluator or the evaluee. The characteristics include, for example, skills, performance, behavioral characteristics, etc., but are not limited to such examples. The analysis unit can analyze the data using statistical analysis or machine learning algorithms. For example, the analysis unit grasps the skills of the evaluator or the evaluee using statistical analysis. The analysis unit can also analyze the performance of the evaluator or the evaluee using machine learning algorithms. Furthermore, the analysis unit can analyze behavioral data to grasp the behavioral characteristics. For example, the analysis unit identifies the behavioral characteristics of the evaluator or the evaluee based on the behavioral data. As a result, the analysis unit can provide appropriate feedback by grasping the characteristics of the evaluator or the evaluee. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can analyze the collected data using AI.

[0036] The generation unit can generate feedback content appropriate for the evaluator or the evaluee based on the analysis result. The generation unit generates optimal feedback content for the evaluator or the evaluee based on the analysis result, for example. The feedback content includes, for example, text feedback and numerical evaluation, but is not limited to such examples. The generation unit generates appropriate feedback content for the evaluator or the evaluee based on the analysis result. For example, if the evaluee is a new employee, specific skill improvement advice is provided. If the evaluee is a veteran employee, feedback on leadership is provided. The generation unit can use a generation AI to generate optimal feedback content for the evaluator or the evaluee based on the analysis result. For example, the generation unit uses a generation AI to generate optimal feedback content for the evaluator or the evaluee. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows the generation unit to generate optimal feedback content for the evaluator or the evaluee. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can use AI to generate feedback content based on the analysis results.

[0037] The providing unit can provide the generated feedback content to the evaluator and the evaluee. The providing unit, for example, provides the generated feedback content to the evaluator and the evaluee. The provision includes, for example, email, dashboard, report, etc., but is not limited to such examples. The providing unit provides the generated feedback content to the evaluator and the evaluee. For example, the feedback content is sent by email. The feedback content is displayed through a dashboard. The feedback content is provided in a report format. The providing unit can use a generation AI to provide the generated feedback content to the evaluator and the evaluee. For example, the providing unit uses the generation AI to provide optimal feedback content to the evaluator and the evaluee. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. Thereby, the providing unit can provide the generated feedback content to the evaluator and the evaluee. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit can provide the generated feedback content using an AI.

[0038] The collection unit can collect the self-report of the assessee or the past few evaluation data. The collection unit, for example, collects the self-report of the assessee or the past few evaluation data. The self-report includes, for example, a questionnaire or an interview, but is not limited to such examples. The collection unit can collect the self-report of the assessee through a questionnaire. The collection unit can also collect the self-report of the assessee through an interview. The past few evaluation data includes, for example, evaluation data for the past year, but is not limited to such examples. The collection unit can collect the evaluation data for the past year. Thereby, the collection unit can provide more appropriate feedback by collecting the self-report of the assessee or the past few evaluation data. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can collect the self-report of the assessee using AI.

[0039] The collection unit can analyze the past feedback history of the assessee and select the optimal data collection method. The collection unit, for example, analyzes the past feedback history of the assessee and selects the optimal data collection method. The optimal data collection method includes, for example, an online questionnaire or a direct interview, but is not limited to such examples. The collection unit selects the optimal data collection method by analyzing the past feedback history of the assessee. For example, the collection unit prioritizes the feedback format that the assessee preferred in the past to collect data. The collection unit can also identify and apply an effective data collection method from the past feedback history of the assessee. Furthermore, the collection unit can adjust the frequency and timing of data collection based on the past feedback history of the assessee. In this way, the collection unit can select the optimal data collection method by analyzing the past feedback history of the assessee. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can analyze the past feedback history of the assessee using AI to select the optimal data collection method.

[0040] The collection unit can perform filtering based on the current project and field of interest of the assessee when collecting data. The collection unit performs filtering based on the current project and field of interest of the assessee when collecting data, for example. Filtering includes, but is not limited to, keyword filtering and category filtering. The collection unit preferentially collects highly relevant data based on the current project and field of interest of the assessee. For example, the collection unit preferentially collects data related to the project that the assessee is currently working on. The collection unit can also collect highly relevant data based on the field of interest of the assessee. Furthermore, the collection unit can filter and collect necessary data according to the current work content of the assessee. Thereby, the collection unit can collect highly relevant data by filtering data based on the current project and field of interest of the assessee. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can filter data using AI based on the current project and field of interest of the assessee.

[0041] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the person being evaluated at the time of data collection. For example, the collection unit preferentially collects highly relevant data in consideration of the geographical location information of the person being evaluated at the time of data collection. The geographical location information includes, for example, GPS data and address information, but is not limited to such examples. The collection unit preferentially collects highly relevant data by considering the geographical location information of the person being evaluated. For example, if the person being evaluated works in a specific area, the collection unit preferentially collects data related to that area. In addition, if the person being evaluated is on a business trip, the collection unit can also collect data related to the business trip destination. Furthermore, if the person being evaluated is working remotely, the collection unit can also collect data related to the remote work. In this way, the collection unit can preferentially collect highly relevant data by considering the geographical location information of the person being evaluated. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can preferentially collect highly relevant data by considering the geographical location information of the person being evaluated using AI.

[0042] The collection unit can analyze the social media activity of the assessee at the time of data collection and collect related data. For example, the collection unit analyzes the social media activity of the assessee at the time of data collection and collects related data. Social media activity includes, for example, the content of posts and the number of likes, but is not limited to such examples. The collection unit collects related data by analyzing the social media activity of the assessee. For example, the collection unit collects related data based on information shared by the assessee on social media. The collection unit can also collect data related to topics of interest from the social media activity of the assessee. Furthermore, the collection unit can determine the direction of data collection with reference to the feedback on social media of the assessee. In this way, the collection unit can collect related data by analyzing the social media activity of the assessee. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can analyze the social media activity of the assessee using AI and collect related data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the data during the analysis. The importance includes, for example, the degree of impact and frequency, but is not limited to such examples. The analysis unit adjusts the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. In addition, the analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data. Thereby, the analysis unit can optimally allocate resources by adjusting the level of detail of the analysis based on the importance of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can evaluate the importance of the data using AI and adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. The analysis unit, for example, applies different analysis algorithms depending on the category of data during analysis. The categories include, for example, text data, numerical data, image data, and the like, but are not limited to such examples. The analysis unit applies an appropriate analysis algorithm depending on the category of data. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply a statistical analysis algorithm to numerical data. Furthermore, the analysis unit can also apply an image analysis algorithm to image data. Thereby, the analysis unit applies an appropriate analysis algorithm depending on the category of data, thereby improving the analysis accuracy. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can apply an appropriate analysis algorithm using AI depending on the category of data.

[0045] The analysis unit can determine the analysis priority based on the collection time of the data at the time of analysis. The analysis unit, for example, determines the analysis priority based on the collection time of the data at the time of analysis. The collection time includes, for example, the most recent month or the past year, but is not limited to such examples. The analysis unit determines the analysis priority based on the collection time of the data. For example, the analysis unit prioritizes the latest data for analysis. In addition, the analysis unit can also emphasize the latest data while referring to past data. Furthermore, the analysis unit can optimally allocate analysis resources according to the collection time of the data. Thereby, the analysis unit can prioritize the latest data by determining the analysis priority based on the collection time of the data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can evaluate the collection time of the data using AI and determine the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of data during analysis. The relevance includes, for example, a common theme and related keywords, but is not limited to such examples. The analysis unit adjusts the order of analysis based on the relevance of data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also analyze less relevant data later. Furthermore, the analysis unit can optimally allocate analysis resources according to the relevance of data. Thereby, the analysis unit can prioritize analysis of highly relevant data by adjusting the order of analysis based on the relevance of data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can evaluate the relevance of data using AI and adjust the order of analysis.

[0047] The generation unit can adjust the level of detail of the feedback based on the characteristics of the assessee when generating the feedback. For example, the generation unit adjusts the level of detail of the feedback based on the characteristics of the assessee when generating the feedback. The characteristics include, for example, skills, performance, behavioral characteristics, etc., but are not limited to such examples. The generation unit adjusts the level of detail of the feedback based on the characteristics of the assessee. For example, if the assessee is a new employee, the generation unit provides specific advice on skill improvement. In addition, if the assessee is a veteran employee, the generation unit can also provide feedback on leadership. Furthermore, the generation unit can customize the content of the feedback according to the characteristics of the assessee. Thereby, the generation unit can provide more appropriate feedback by adjusting the level of detail of the feedback according to the characteristics of the assessee. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can evaluate the characteristics of the assessee using AI and adjust the level of detail of the feedback.

[0048] The generating unit can apply different feedback algorithms according to the past evaluation history of the assessee when generating feedback. The generating unit applies different feedback algorithms according to the past evaluation history of the assessee when generating feedback, for example. Feedback algorithms include, but are not limited to, rule-based and machine learning-based algorithms. The generating unit applies an optimal feedback algorithm according to the past evaluation history of the assessee. For example, the generating unit generates optimal feedback based on feedback that the assessee has received in the past. The generating unit can also identify and apply an effective feedback algorithm from the past evaluation history of the assessee. Furthermore, the generating unit can analyze the past evaluation history of the assessee and customize the content of the feedback. As a result, the generating unit can provide more effective feedback by applying an optimal feedback algorithm according to the past evaluation history of the assessee. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can analyze the past evaluation history of the assessee using AI and apply an optimal feedback algorithm.

[0049] The generation unit can determine the priority of the feedback based on the submission time of the assessee when generating the feedback. For example, the generation unit determines the priority of the feedback based on the submission time of the assessee when generating the feedback. The submission time includes, for example, early submission and late submission, but is not limited to such examples. The generation unit determines the priority of the feedback based on the submission time of the assessee. For example, the generation unit provides feedback preferentially when the assessee submits early. In addition, the generation unit can provide feedback later when the assessee submits late. Furthermore, the generation unit can optimally allocate feedback resources according to the submission time. Thereby, the generation unit can provide feedback efficiently by determining the priority of the feedback based on the submission time of the assessee. Part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can evaluate the submission time of the assessee using AI and determine the priority of the feedback.

[0050] The generation unit can adjust the order of feedback based on the relevance of the assessee when generating feedback. The generation unit, for example, adjusts the order of feedback based on the relevance of the assessee when generating feedback. Relevance includes, for example, a common theme and related keywords, but is not limited to such examples. The generation unit adjusts the order of feedback based on the relevance of the assessee. For example, the generation unit provides feedback with high relevance as a priority. The generation unit can also provide feedback with low relevance at a later date. Furthermore, the generation unit can optimally allocate feedback resources according to the relevance of the feedback. Thereby, the generation unit can provide more effective feedback by adjusting the order of feedback based on the relevance of the assessee. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can evaluate the relevance of the assessee using AI and adjust the order of feedback.

[0051] The providing unit can select the optimal providing method by referring to the past feedback history of the assessee when providing feedback. For example, the providing unit selects the optimal providing method by referring to the past feedback history of the assessee when providing feedback. The optimal providing method includes, for example, online provision and face-to-face provision, but is not limited to such examples. The providing unit selects the optimal providing method by referring to the past feedback history of the assessee. For example, the providing unit provides a feedback format that the assessee has preferred in the past with priority. In addition, the providing unit can identify and apply an effective providing method from the past feedback history of the assessee. Furthermore, the providing unit can adjust the frequency and timing of the provision based on the past feedback history of the assessee. Thereby, the providing unit can select the optimal providing method by referring to the past feedback history of the assessee. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can select the optimal providing method by referring to the past feedback history of the assessee using AI.

[0052] The providing unit can customize the providing means based on the current situation of the assessee when providing feedback. For example, the providing unit customizes the providing means based on the current situation of the assessee when providing feedback. The current situation includes, for example, the progress of the project and the workload situation, but is not limited to such examples. The providing unit selects the optimal providing means based on the current situation of the assessee. For example, when the assessee is working remotely, the providing unit prioritizes providing feedback online. In addition, when the assessee is in the office, the providing unit can also provide face-to-face feedback. Furthermore, the providing unit can select the optimal providing means according to the current work situation of the assessee. As a result, the providing unit can provide more appropriate feedback by customizing the providing means based on the current situation of the assessee. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can evaluate the current situation of the assessee using AI and customize the providing means.

[0053] The providing unit can select the optimal providing method by considering the geographical location information of the person being evaluated when providing feedback. For example, the providing unit selects the optimal providing method by considering the geographical location information of the person being evaluated when providing feedback. The geographical location information includes, for example, GPS data and address information, but is not limited to such examples. The providing unit selects the optimal providing method by considering the geographical location information of the person being evaluated. For example, if the person being evaluated works in a specific area, the providing unit provides feedback related to that area with priority. In addition, if the person being evaluated is on a business trip, the providing unit can also provide feedback related to the business trip destination. Furthermore, if the person being evaluated is working remotely, the providing unit can also provide feedback related to the remote work. As a result, the providing unit can select the optimal providing method by considering the geographical location information of the person being evaluated. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can select the optimal providing method by considering the geographical location information of the person being evaluated using AI.

[0054] The providing unit can analyze the social media activity of the person being evaluated when providing feedback and propose a means of providing the feedback. For example, the providing unit analyzes the social media activity of the person being evaluated when providing feedback and proposes a means of providing the feedback. Social media activity includes, for example, the content of posts and the number of likes, but is not limited to such examples. The providing unit proposes the optimal means of providing the feedback by analyzing the social media activity of the person being evaluated. For example, the providing unit provides relevant feedback based on information shared by the person being evaluated on social media. The providing unit can also provide feedback related to topics of interest from the social media activity of the person being evaluated. Furthermore, the providing unit can also determine the means of providing the feedback by referring to the feedback on social media of the person being evaluated. Thereby, the providing unit can propose the optimal means of providing the feedback by analyzing the social media activity of the person being evaluated. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can analyze the social media activity of the person being evaluated using AI and propose a means of providing the feedback.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The feedback training system can further include a "simulation unit." The simulation unit has a function of simulating in advance the effect of feedback provided by the evaluator. For example, when an evaluator provides advice to a new employee on how to improve their skills, it is possible to simulate how effective that advice will be. Also, when providing feedback to a veteran employee on the display of leadership, it is possible to predict how that feedback will be received. Furthermore, the simulation unit can compare the difference in effect when the content of the feedback is changed. This allows the evaluator to select the most effective feedback.

[0057] The collection unit can also collect health data of the person being evaluated. For example, data such as the stress level and sleep pattern of the person being evaluated can be collected and reflected in the content of the feedback. If the person being evaluated is feeling high stress, advice for reducing stress can be provided. Also, if the person being evaluated has a disturbed sleep pattern, feedback on healthy lifestyle habits can be provided. Furthermore, the collection unit can monitor the health data of the person being evaluated over the long term and provide feedback according to changes in the health condition.

[0058] The analysis unit can analyze the social network data of the person being evaluated. For example, it can analyze what kind of people the person being evaluated interacts with and what topics the person being evaluated talks about, and reflect this in the content of the feedback. If the person being evaluated frequently interacts with people who have a particular skill, it can provide feedback related to that skill. Also, if the person being evaluated is interested in a particular topic, it can provide feedback related to that topic. Furthermore, the analysis unit can grasp the influence and leadership characteristics of the person being evaluated based on the social network data of the person being evaluated.

[0059] When generating the feedback content, the generation unit can adjust the form of the feedback based on the learning style of the person being evaluated. For example, if the person being evaluated has a visual learning style, feedback using graphs and diagrams can be provided. If the person being evaluated has an auditory learning style, feedback using audio messages can be provided. Furthermore, if the person being evaluated has a practical learning style, feedback including a specific action plan can be provided. In this way, the generation unit can provide optimal feedback according to the learning style of the person being evaluated.

[0060] When providing the feedback content, the providing unit can adjust the way of expressing the feedback in consideration of the cultural background of the person being evaluated. For example, if the person being evaluated has a different cultural background, the feedback can be provided using an expression method suitable for that culture. If the person being evaluated is based on Japanese culture, the feedback can be provided using honorific language and polite expressions. Also, if the person being evaluated is based on American culture, the feedback can be provided using direct and clear expressions. Furthermore, the providing unit can customize the content of the feedback according to the cultural background of the person being evaluated. This allows the providing unit to provide optimal feedback according to the cultural background of the person being evaluated.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The collection unit collects data. The data includes, for example, text data, numerical data, image data, and the like. The collection unit collects data from evaluators and evaluators. The evaluators and evaluators include, for example, employees, students, and customers. The collection unit can also collect self-reporting from the evaluators and the evaluation data from the past few times. For example, the collection unit collects self-reporting data through questionnaires and interviews, and collects evaluation data from the past year. Step 2: The analysis unit analyzes the collected data. The analysis may include, for example, statistical analysis or machine learning algorithms. The analysis unit analyzes the collected data and understands the characteristics of the evaluator and the evaluee. The characteristics may include, for example, skills, performance, behavioral characteristics, etc. Step 3: The generation unit generates feedback content based on the analysis results. The feedback content includes, for example, text feedback and numerical evaluation. The generation unit generates optimal feedback content for the evaluator and the person being evaluated based on the analysis results. For example, if the person being evaluated is a new employee, the generation unit provides specific advice on how to improve skills. If the person being evaluated is a veteran employee, the generation unit provides feedback on demonstrating leadership. Step 4: The providing unit provides the generated feedback content. For example, the providing unit provides the generated feedback content by email, dashboard, report, etc. The providing unit provides the generated feedback content to the evaluator and the evaluee. For example, the providing unit sends the feedback content by email, displays the feedback content through a dashboard, or provides the feedback content in the form of a report.

[0063] (Example 2) The feedback training system according to the embodiment of the present invention is a system that has a training function for managers to provide appropriate feedback. This feedback training system is useful in MBO (management by objectives) and 1on1 meetings, and can be used to develop the capabilities of not only evaluators but also those being evaluated. For example, in MBO, the system displays recommended feedback content by inputting not only the current evaluation, but also the most recent evaluation, years of service, and the characteristics of the person being evaluated. If the person being evaluated is a new employee, specific advice on improving skills and short-term goal setting may be recommended. On the other hand, if the person being evaluated is a veteran employee with a long history of service, feedback on leadership and long-term career plans is recommended. Next, in 1on1 meetings, feedback content with a view to the next few months is displayed by inputting self-declaration, past evaluations, worries, and characteristics. If the person being evaluated is worried about his / her current work, specific advice and support to resolve the worries are recommended. In addition, feedback to increase motivation and challenging goal setting to promote growth may be proposed depending on the characteristics of the person being evaluated. In this way, the training function for managers to provide appropriate feedback is beneficial for both the evaluator and the person being evaluated. By providing feedback that is tailored to the characteristics and circumstances of the appraisee, the appraiser supports the appraisee's growth and contributes to improving the performance of the entire organization. Meanwhile, by receiving specific and practical feedback, the appraisee can take concrete steps toward developing their own capabilities and realizing their career plans. This allows the feedback training system to provide a training function for managers to provide appropriate feedback.

[0064] The feedback training system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data. The data includes, for example, text data, numerical data, image data, etc., but is not limited to such examples. The collection unit collects, for example, data from the evaluator and the evaluee. The evaluator and the evaluee include, for example, employees, students, customers, etc., but is not limited to such examples. The collection unit can also collect the self-report of the evaluee and the evaluation data of the past several times. For example, the self-report data is collected through a questionnaire or an interview, and the evaluation data for the past one year is collected. The analysis unit analyzes the collected data. The analysis includes, for example, statistical analysis and machine learning algorithms, but is not limited to such examples. The analysis unit analyzes the collected data and grasps the characteristics of the evaluator and the evaluee. The characteristics include, for example, skills, performance, behavioral characteristics, etc., but is not limited to such examples. The generation unit generates feedback content based on the analysis result. The feedback content includes, for example, text feedback and numerical evaluation, but is not limited to such examples. The generation unit generates optimal feedback content for the evaluator and the evaluee based on the analysis result. For example, if the evaluee is a new employee, specific advice on skill improvement is provided. If the evaluee is a veteran employee, feedback on leadership is provided. The provision unit provides the generated feedback content. The provision includes, for example, email, dashboard, report, etc., but is not limited to such examples. The provision unit provides the generated feedback content to the evaluator and the evaluee. For example, the feedback content is sent by email. The feedback content is displayed through a dashboard. The feedback content is provided in a report format. This allows the feedback training system according to the embodiment to efficiently collect and analyze data, generate and provide feedback content. Some or all of the above-mentioned processes in the collection unit, analysis unit, generation unit, and provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can collect data from the evaluator and the evaluee using AI.The analysis unit can analyze the collected data using AI. The generation unit can generate feedback content based on the analysis result using AI. The provision unit can provide the generated feedback content using AI.

[0065] The collection unit collects data. Examples of the data include, but are not limited to, text data, numerical data, and image data. The collection unit collects data from, for example, evaluators and evaluators. Examples of the evaluators and evaluators include, but are not limited to, employees, students, and customers. The collection unit can also collect self-reports from evaluators and the past several evaluations. For example, self-reported data is collected through questionnaires and interviews, and evaluation data from the past year is collected. The collection unit can utilize online forms and mobile applications to efficiently collect these data. For example, a system can be created in which employees enter self-evaluations using smartphones and the data is automatically sent to a cloud server. In addition, feedback and evaluation results given by evaluators to evaluators are also collected in digital format. In addition, the collection unit can obtain data from external data sources. For example, industry standard benchmark data and market research data can be collected and integrated with internal data to enable a more comprehensive evaluation. It is also important for the collection unit to carry out data verification and cleaning processes to ensure data quality. For example, duplicate data can be deleted and incomplete data can be completed so that the analysis unit can use accurate data. This enables the collection unit to efficiently collect high-quality data from a variety of data sources, improving the accuracy and reliability of the entire system.

[0066] The analysis unit analyzes the collected data. Examples of the analysis include, but are not limited to, statistical analysis and machine learning algorithms. The analysis unit analyzes the collected data to grasp the characteristics of the evaluator and the evaluee. Examples of the characteristics include, but are not limited to, skills, performance, behavioral characteristics, and the like. The analysis unit first preprocesses the data, complements missing values, and detects outliers. Next, the analysis unit grasps the basic trends and distribution of the data using statistical analysis. For example, the average value and standard deviation of the skill level of the evaluee are calculated to evaluate the overall performance. In addition, the analysis unit uses a machine learning algorithm to extract patterns of the behavioral characteristics and performance of the evaluee. For example, a clustering method is used to classify the evaluee into multiple groups, and the characteristics of each group are analyzed. Furthermore, the analysis unit can build a prediction model and predict the future performance and skill improvement of the evaluee. For example, regression analysis is used to predict future performance from the evaluee's past data, and basic information is provided for providing appropriate feedback. It is also important for the analysis unit to visualize these analysis results and provide them in a format that is easy for the evaluator and the evaluee to understand. For example, graphs and charts can be used to visually show trends and patterns in the data, facilitating understanding of the analysis results. This allows the analysis department to analyze the collected data from multiple angles and accurately grasp the characteristics of the evaluators and those being evaluated.

[0067] The generation unit generates feedback content based on the analysis result. The feedback content includes, for example, text feedback and numerical evaluation, but is not limited to such examples. The generation unit generates optimal feedback content for the evaluator and the evaluee based on the analysis result. For example, if the evaluee is a new employee, specific skill improvement advice is provided. If the evaluee is a veteran employee, feedback on leadership is provided. The generation unit can automatically generate text feedback based on the analysis result using natural language processing technology. For example, based on the skill evaluation result of the evaluee, feedback that points out specific improvements and strengths is generated. In addition, the performance of the evaluee can be quantitatively evaluated using numerical evaluation and visually shown using graphs and charts. Furthermore, the generation unit can provide customized feedback according to individual needs. For example, individual goal setting and action plans are proposed taking into account the evaluee's past evaluation history and self-reported data. The generation unit can also use pre-defined templates and guidelines to ensure consistency and quality of the feedback content. This makes it possible for the generated feedback to be beneficial to the evaluator and the evaluee and provide specific guidelines for action. The generator can provide feedback efficiently and quickly by automating the process of generating feedback contents.

[0068] The providing unit provides the generated feedback content. Examples of the provision include, but are not limited to, email, dashboard, and report. The providing unit provides the generated feedback content to the evaluator and the evaluee. For example, the feedback content is sent by email. The feedback content is displayed through a dashboard. The feedback content is provided in the form of a report. The providing unit can utilize multiple communication means to deliver the feedback content quickly and reliably. For example, not only email notifications but also push notifications of mobile apps and SMS are used in combination to deliver important feedback reliably. In addition, the providing unit can provide an interactive dashboard to display the feedback content in a visually easy-to-understand manner. For example, the evaluee can check his / her evaluation results in graphs and charts and click to display detailed feedback content. Furthermore, the providing unit can also have a function to collect confirmation of receipt of the feedback content and responses to the feedback. For example, an interface is provided that allows the evaluee to leave comments on the feedback content or send additional questions. This allows the providing unit to monitor the receipt status and effectiveness of the feedback and provide additional support as necessary. The providing unit streamlines the process of providing the feedback content, allowing the evaluator and the evaluee to receive feedback quickly.

[0069] The collection unit can collect data from the evaluator or the person being evaluated. The collection unit, for example, collects data from the evaluator or the person being evaluated. The evaluator or the person being evaluated includes, for example, employees, students, customers, etc., but is not limited to such examples. The collection unit can use questionnaires or interviews to efficiently collect data from the evaluator or the person being evaluated. For example, the collection unit collects data from the evaluator or the person being evaluated through an online questionnaire. The collection unit can also collect data from the evaluator or the person being evaluated through a face-to-face interview. This allows the collection unit to efficiently collect data from the evaluator or the person being evaluated. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can collect data from the evaluator or the person being evaluated using AI.

[0070] The analysis unit can analyze the collected data and grasp the characteristics of the evaluator or the evaluee. The analysis unit, for example, analyzes the collected data and grasps the characteristics of the evaluator or the evaluee. The characteristics include, for example, skills, performance, behavioral characteristics, etc., but are not limited to such examples. The analysis unit can analyze the data using statistical analysis or machine learning algorithms. For example, the analysis unit grasps the skills of the evaluator or the evaluee using statistical analysis. The analysis unit can also analyze the performance of the evaluator or the evaluee using machine learning algorithms. Furthermore, the analysis unit can analyze behavioral data to grasp the behavioral characteristics. For example, the analysis unit identifies the behavioral characteristics of the evaluator or the evaluee based on the behavioral data. As a result, the analysis unit can provide appropriate feedback by grasping the characteristics of the evaluator or the evaluee. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can analyze the collected data using AI.

[0071] The generation unit can generate feedback content appropriate for the evaluator or the evaluee based on the analysis result. The generation unit generates optimal feedback content for the evaluator or the evaluee based on the analysis result, for example. The feedback content includes, for example, text feedback and numerical evaluation, but is not limited to such examples. The generation unit generates appropriate feedback content for the evaluator or the evaluee based on the analysis result. For example, if the evaluee is a new employee, specific skill improvement advice is provided. If the evaluee is a veteran employee, feedback on leadership is provided. The generation unit can use a generation AI to generate optimal feedback content for the evaluator or the evaluee based on the analysis result. For example, the generation unit uses a generation AI to generate optimal feedback content for the evaluator or the evaluee. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows the generation unit to generate optimal feedback content for the evaluator or the evaluee. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can use AI to generate feedback content based on the analysis results.

[0072] The providing unit can provide the generated feedback content to the evaluator and the evaluee. The providing unit, for example, provides the generated feedback content to the evaluator and the evaluee. The provision includes, for example, email, dashboard, report, etc., but is not limited to such examples. The providing unit provides the generated feedback content to the evaluator and the evaluee. For example, the feedback content is sent by email. The feedback content is displayed through a dashboard. The feedback content is provided in a report format. The providing unit can use a generation AI to provide the generated feedback content to the evaluator and the evaluee. For example, the providing unit uses the generation AI to provide optimal feedback content to the evaluator and the evaluee. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. Thereby, the providing unit can provide the generated feedback content to the evaluator and the evaluee. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit can provide the generated feedback content using an AI.

[0073] The collection unit can collect the self-report of the assessee or the past few evaluation data. The collection unit, for example, collects the self-report of the assessee or the past few evaluation data. The self-report includes, for example, a questionnaire or an interview, but is not limited to such examples. The collection unit can collect the self-report of the assessee through a questionnaire. The collection unit can also collect the self-report of the assessee through an interview. The past few evaluation data includes, for example, evaluation data for the past year, but is not limited to such examples. The collection unit can collect the evaluation data for the past year. Thereby, the collection unit can provide more appropriate feedback by collecting the self-report of the assessee or the past few evaluation data. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can collect the self-report of the assessee using AI.

[0074] The collection unit can estimate the user's emotion and adjust the timing of data collection based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and adjusts the timing of data collection based on the estimated user's emotion. The estimation of emotion includes, for example, facial expression recognition and voice analysis, but is not limited to such examples. The collection unit can estimate the user's emotion using facial expression recognition. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The collection unit can also estimate the user's emotion using voice analysis. For example, the collection unit records the user's voice and estimates the emotion using voice analysis technology. This allows the collection unit to adjust the timing of data collection according to the user's emotion. For example, if the user is feeling stressed, the timing of data collection is delayed and collection is performed in a relaxed state. If the user is relaxed, data collection is started immediately and data in a natural state is collected. If the user is in a hurry, the necessary data is efficiently collected in a short time. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can estimate the user's emotions using AI and adjust the timing of data collection.

[0075] The collection unit can analyze the past feedback history of the assessee and select the optimal data collection method. The collection unit, for example, analyzes the past feedback history of the assessee and selects the optimal data collection method. The optimal data collection method includes, for example, an online questionnaire or a direct interview, but is not limited to such examples. The collection unit selects the optimal data collection method by analyzing the past feedback history of the assessee. For example, the collection unit prioritizes the feedback format that the assessee preferred in the past to collect data. The collection unit can also identify and apply an effective data collection method from the past feedback history of the assessee. Furthermore, the collection unit can adjust the frequency and timing of data collection based on the past feedback history of the assessee. In this way, the collection unit can select the optimal data collection method by analyzing the past feedback history of the assessee. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can analyze the past feedback history of the assessee using AI to select the optimal data collection method.

[0076] The collection unit can perform filtering based on the current project and field of interest of the assessee when collecting data. The collection unit performs filtering based on the current project and field of interest of the assessee when collecting data, for example. Filtering includes, but is not limited to, keyword filtering and category filtering. The collection unit preferentially collects highly relevant data based on the current project and field of interest of the assessee. For example, the collection unit preferentially collects data related to the project that the assessee is currently working on. The collection unit can also collect highly relevant data based on the field of interest of the assessee. Furthermore, the collection unit can filter and collect necessary data according to the current work content of the assessee. Thereby, the collection unit can collect highly relevant data by filtering data based on the current project and field of interest of the assessee. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can filter data using AI based on the current project and field of interest of the assessee.

[0077] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and determines the priority of data to be collected based on the estimated user's emotion. The estimation of emotion includes, for example, facial expression recognition and voice analysis, but is not limited to such examples. The collection unit can estimate the user's emotion using facial expression recognition. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The collection unit can also estimate the user's emotion using voice analysis. For example, the collection unit records the user's voice and estimates the emotion using voice analysis technology. This allows the collection unit to determine the priority of data to be collected according to the user's emotion. For example, when the user is feeling stressed, data of low importance is put off and important data is collected with priority. When the user is relaxed, all data is collected evenly. When the user is in a hurry, the most important data is collected with priority. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit may estimate the user's emotions using AI and determine the priority of the data to be collected.

[0078] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the person being evaluated at the time of data collection. For example, the collection unit preferentially collects highly relevant data in consideration of the geographical location information of the person being evaluated at the time of data collection. The geographical location information includes, for example, GPS data and address information, but is not limited to such examples. The collection unit preferentially collects highly relevant data by considering the geographical location information of the person being evaluated. For example, if the person being evaluated works in a specific area, the collection unit preferentially collects data related to that area. In addition, if the person being evaluated is on a business trip, the collection unit can also collect data related to the business trip destination. Furthermore, if the person being evaluated is working remotely, the collection unit can also collect data related to the remote work. In this way, the collection unit can preferentially collect highly relevant data by considering the geographical location information of the person being evaluated. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can preferentially collect highly relevant data by considering the geographical location information of the person being evaluated using AI.

[0079] The collection unit can analyze the social media activity of the assessee at the time of data collection and collect related data. For example, the collection unit analyzes the social media activity of the assessee at the time of data collection and collects related data. Social media activity includes, for example, the content of posts and the number of likes, but is not limited to such examples. The collection unit collects related data by analyzing the social media activity of the assessee. For example, the collection unit collects related data based on information shared by the assessee on social media. The collection unit can also collect data related to topics of interest from the social media activity of the assessee. Furthermore, the collection unit can determine the direction of data collection with reference to the feedback on social media of the assessee. In this way, the collection unit can collect related data by analyzing the social media activity of the assessee. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can analyze the social media activity of the assessee using AI and collect related data.

[0080] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the expression method of the analysis based on the estimated user's emotion. The estimation of emotion includes, for example, facial expression recognition and voice analysis, but is not limited to such examples. The analysis unit can estimate the user's emotion using facial expression recognition. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The analysis unit can also estimate the user's emotion using voice analysis. For example, the analysis unit records the user's voice and estimates the emotion using voice analysis technology. This allows the analysis unit to adjust the expression method of the analysis according to the user's emotion. For example, if the user is nervous, a simple and highly visible analysis result is provided. If the user is relaxed, a detailed analysis result is provided. If the user is in a hurry, a concise analysis result that covers the main points is provided. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can estimate the user's emotions using AI and adjust the way the analysis is expressed.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the data during the analysis. The importance includes, for example, the degree of impact and frequency, but is not limited to such examples. The analysis unit adjusts the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. In addition, the analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data. Thereby, the analysis unit can optimally allocate resources by adjusting the level of detail of the analysis based on the importance of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can evaluate the importance of the data using AI and adjust the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. The analysis unit, for example, applies different analysis algorithms depending on the category of data during analysis. The categories include, for example, text data, numerical data, image data, and the like, but are not limited to such examples. The analysis unit applies an appropriate analysis algorithm depending on the category of data. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply a statistical analysis algorithm to numerical data. Furthermore, the analysis unit can also apply an image analysis algorithm to image data. Thereby, the analysis unit applies an appropriate analysis algorithm depending on the category of data, thereby improving the analysis accuracy. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can apply an appropriate analysis algorithm using AI depending on the category of data.

[0083] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the length of the analysis based on the estimated user's emotion. The estimation of emotion includes, for example, facial expression recognition and voice analysis, but is not limited to such examples. The analysis unit can estimate the user's emotion using facial expression recognition. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The analysis unit can also estimate the user's emotion using voice analysis. For example, the analysis unit records the user's voice and estimates the emotion using voice analysis technology. This allows the analysis unit to adjust the length of the analysis according to the user's emotion. For example, if the user is in a hurry, a short and to the point analysis result is provided. If the user is relaxed, a detailed analysis result is provided. If the user is excited, an analysis result with a visually stimulating effect is provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit may estimate the user's emotions using AI and adjust the length of the analysis.

[0084] The analysis unit can determine the analysis priority based on the collection time of the data at the time of analysis. The analysis unit, for example, determines the analysis priority based on the collection time of the data at the time of analysis. The collection time includes, for example, the most recent month or the past year, but is not limited to such examples. The analysis unit determines the analysis priority based on the collection time of the data. For example, the analysis unit prioritizes the latest data for analysis. In addition, the analysis unit can also emphasize the latest data while referring to past data. Furthermore, the analysis unit can optimally allocate analysis resources according to the collection time of the data. Thereby, the analysis unit can prioritize the latest data by determining the analysis priority based on the collection time of the data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can evaluate the collection time of the data using AI and determine the analysis priority.

[0085] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of data during analysis. The relevance includes, for example, a common theme and related keywords, but is not limited to such examples. The analysis unit adjusts the order of analysis based on the relevance of data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also analyze less relevant data later. Furthermore, the analysis unit can optimally allocate analysis resources according to the relevance of data. Thereby, the analysis unit can prioritize analysis of highly relevant data by adjusting the order of analysis based on the relevance of data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can evaluate the relevance of data using AI and adjust the order of analysis.

[0086] The generation unit can estimate the user's emotion and adjust the expression method of the feedback content based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the expression method of the feedback content based on the estimated user's emotion. The estimation of emotion includes, for example, facial expression recognition and voice analysis, but is not limited to such examples. The generation unit can estimate the user's emotion using facial expression recognition. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The generation unit can also estimate the user's emotion using voice analysis. For example, the generation unit records the user's voice and estimates the emotion using voice analysis technology. This allows the generation unit to adjust the expression method of the feedback content according to the user's emotion. For example, if the user is relaxed, detailed feedback is provided. If the user is nervous, simple and highly visible feedback is provided. If the user is in a hurry, concise feedback that covers the main points is provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can estimate the user's emotions using AI and adjust the way the feedback content is expressed.

[0087] The generation unit can adjust the level of detail of the feedback based on the characteristics of the assessee when generating the feedback. For example, the generation unit adjusts the level of detail of the feedback based on the characteristics of the assessee when generating the feedback. The characteristics include, for example, skills, performance, behavioral characteristics, etc., but are not limited to such examples. The generation unit adjusts the level of detail of the feedback based on the characteristics of the assessee. For example, if the assessee is a new employee, the generation unit provides specific advice on skill improvement. In addition, if the assessee is a veteran employee, the generation unit can also provide feedback on leadership. Furthermore, the generation unit can customize the content of the feedback according to the characteristics of the assessee. Thereby, the generation unit can provide more appropriate feedback by adjusting the level of detail of the feedback according to the characteristics of the assessee. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can evaluate the characteristics of the assessee using AI and adjust the level of detail of the feedback.

[0088] The generating unit can apply different feedback algorithms according to the past evaluation history of the assessee when generating feedback. The generating unit applies different feedback algorithms according to the past evaluation history of the assessee when generating feedback, for example. Feedback algorithms include, but are not limited to, rule-based and machine learning-based algorithms. The generating unit applies an optimal feedback algorithm according to the past evaluation history of the assessee. For example, the generating unit generates optimal feedback based on feedback that the assessee has received in the past. The generating unit can also identify and apply an effective feedback algorithm from the past evaluation history of the assessee. Furthermore, the generating unit can analyze the past evaluation history of the assessee and customize the content of the feedback. As a result, the generating unit can provide more effective feedback by applying an optimal feedback algorithm according to the past evaluation history of the assessee. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can analyze the past evaluation history of the assessee using AI and apply an optimal feedback algorithm.

[0089] The generation unit can estimate the user's emotion and adjust the length of the feedback content based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the length of the feedback content based on the estimated user's emotion. The emotion estimation includes, for example, facial expression recognition and voice analysis, but is not limited to such examples. The generation unit can estimate the user's emotion using facial expression recognition. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The generation unit can also estimate the user's emotion using voice analysis. For example, the generation unit records the user's voice and estimates the emotion using voice analysis technology. This allows the generation unit to adjust the length of the feedback content according to the user's emotion. For example, if the user is in a hurry, short and to the point feedback is provided. If the user is relaxed, detailed feedback is provided. If the user is excited, feedback with a visually stimulating effect is provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can estimate the user's emotions using AI and adjust the length of the feedback content.

[0090] The generation unit can determine the priority of the feedback based on the submission time of the assessee when generating the feedback. For example, the generation unit determines the priority of the feedback based on the submission time of the assessee when generating the feedback. The submission time includes, for example, early submission and late submission, but is not limited to such examples. The generation unit determines the priority of the feedback based on the submission time of the assessee. For example, the generation unit provides feedback preferentially when the assessee submits early. In addition, the generation unit can provide feedback later when the assessee submits late. Furthermore, the generation unit can optimally allocate feedback resources according to the submission time. Thereby, the generation unit can provide feedback efficiently by determining the priority of the feedback based on the submission time of the assessee. Part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can evaluate the submission time of the assessee using AI and determine the priority of the feedback.

[0091] The generation unit can adjust the order of feedback based on the relevance of the assessee when generating feedback. The generation unit, for example, adjusts the order of feedback based on the relevance of the assessee when generating feedback. Relevance includes, for example, a common theme and related keywords, but is not limited to such examples. The generation unit adjusts the order of feedback based on the relevance of the assessee. For example, the generation unit provides feedback with high relevance as a priority. The generation unit can also provide feedback with low relevance at a later date. Furthermore, the generation unit can optimally allocate feedback resources according to the relevance of the feedback. Thereby, the generation unit can provide more effective feedback by adjusting the order of feedback based on the relevance of the assessee. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can evaluate the relevance of the assessee using AI and adjust the order of feedback.

[0092] The providing unit can estimate the user's emotion and adjust the feedback providing method based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the feedback providing method based on the estimated user's emotion. The emotion estimation includes, for example, facial expression recognition and voice analysis, but is not limited to such examples. The providing unit can estimate the user's emotion using facial expression recognition. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The providing unit can also estimate the user's emotion using voice analysis. For example, the providing unit records the user's voice and estimates the emotion using voice analysis technology. This allows the providing unit to adjust the feedback providing method according to the user's emotion. For example, if the user is relaxed, detailed feedback is provided. If the user is nervous, simple and highly visible feedback is provided. If the user is in a hurry, concise feedback that covers the main points is provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit may estimate the user's emotions using AI and adjust the method of providing feedback.

[0093] The providing unit can select the optimal providing method by referring to the past feedback history of the assessee when providing feedback. For example, the providing unit selects the optimal providing method by referring to the past feedback history of the assessee when providing feedback. The optimal providing method includes, for example, online provision and face-to-face provision, but is not limited to such examples. The providing unit selects the optimal providing method by referring to the past feedback history of the assessee. For example, the providing unit provides a feedback format that the assessee has preferred in the past with priority. In addition, the providing unit can identify and apply an effective providing method from the past feedback history of the assessee. Furthermore, the providing unit can adjust the frequency and timing of the provision based on the past feedback history of the assessee. Thereby, the providing unit can select the optimal providing method by referring to the past feedback history of the assessee. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can select the optimal providing method by referring to the past feedback history of the assessee using AI.

[0094] The providing unit can customize the providing means based on the current situation of the assessee when providing feedback. For example, the providing unit customizes the providing means based on the current situation of the assessee when providing feedback. The current situation includes, for example, the progress of the project and the workload situation, but is not limited to such examples. The providing unit selects the optimal providing means based on the current situation of the assessee. For example, when the assessee is working remotely, the providing unit prioritizes providing feedback online. In addition, when the assessee is in the office, the providing unit can also provide face-to-face feedback. Furthermore, the providing unit can select the optimal providing means according to the current work situation of the assessee. As a result, the providing unit can provide more appropriate feedback by customizing the providing means based on the current situation of the assessee. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can evaluate the current situation of the assessee using AI and customize the providing means.

[0095] The providing unit can estimate the user's emotion and determine the order of providing feedback based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and determines the order of providing feedback based on the estimated user's emotion. The estimation of emotion includes, for example, facial expression recognition and voice analysis, but is not limited to such examples. The providing unit can estimate the user's emotion using facial expression recognition. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The providing unit can also estimate the user's emotion using voice analysis. For example, the providing unit records the user's voice and estimates the emotion using voice analysis technology. This allows the providing unit to determine the order of providing feedback according to the user's emotion. For example, when the user is in a hurry, important feedback is provided with priority. When the user is relaxed, all feedback is provided evenly. When the user is stressed, feedback with low importance is postponed. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit may estimate the user's emotions using AI and determine the order in which feedback is provided.

[0096] The providing unit can select the optimal providing method by considering the geographical location information of the person being evaluated when providing feedback. For example, the providing unit selects the optimal providing method by considering the geographical location information of the person being evaluated when providing feedback. The geographical location information includes, for example, GPS data and address information, but is not limited to such examples. The providing unit selects the optimal providing method by considering the geographical location information of the person being evaluated. For example, if the person being evaluated works in a specific area, the providing unit provides feedback related to that area with priority. In addition, if the person being evaluated is on a business trip, the providing unit can also provide feedback related to the business trip destination. Furthermore, if the person being evaluated is working remotely, the providing unit can also provide feedback related to the remote work. As a result, the providing unit can select the optimal providing method by considering the geographical location information of the person being evaluated. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can select the optimal providing method by considering the geographical location information of the person being evaluated using AI.

[0097] The providing unit can analyze the social media activity of the person being evaluated when providing feedback and propose a means of providing the feedback. For example, the providing unit analyzes the social media activity of the person being evaluated when providing feedback and proposes a means of providing the feedback. Social media activity includes, for example, the content of posts and the number of likes, but is not limited to such examples. The providing unit proposes the optimal means of providing the feedback by analyzing the social media activity of the person being evaluated. For example, the providing unit provides relevant feedback based on information shared by the person being evaluated on social media. The providing unit can also provide feedback related to topics of interest from the social media activity of the person being evaluated. Furthermore, the providing unit can also determine the means of providing the feedback by referring to the feedback on social media of the person being evaluated. Thereby, the providing unit can propose the optimal means of providing the feedback by analyzing the social media activity of the person being evaluated. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can analyze the social media activity of the person being evaluated using AI and propose a means of providing the feedback.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The feedback training system can further include a "simulation unit." The simulation unit has a function of simulating in advance the effect of feedback provided by the evaluator. For example, when an evaluator provides advice to a new employee on how to improve their skills, it is possible to simulate how effective that advice will be. Also, when providing feedback to a veteran employee on the display of leadership, it is possible to predict how that feedback will be received. Furthermore, the simulation unit can compare the difference in effect when the content of the feedback is changed. This allows the evaluator to select the most effective feedback.

[0100] The collection unit can also collect health data of the person being evaluated. For example, data such as the stress level and sleep pattern of the person being evaluated can be collected and reflected in the content of the feedback. If the person being evaluated is feeling high stress, advice for reducing stress can be provided. Also, if the person being evaluated has a disturbed sleep pattern, feedback on healthy lifestyle habits can be provided. Furthermore, the collection unit can monitor the health data of the person being evaluated over the long term and provide feedback according to changes in the health condition.

[0101] The analysis unit can analyze the social network data of the person being evaluated. For example, it can analyze what kind of people the person being evaluated interacts with and what topics the person being evaluated talks about, and reflect this in the content of the feedback. If the person being evaluated frequently interacts with people who have a particular skill, it can provide feedback related to that skill. Also, if the person being evaluated is interested in a particular topic, it can provide feedback related to that topic. Furthermore, the analysis unit can grasp the influence and leadership characteristics of the person being evaluated based on the social network data of the person being evaluated.

[0102] When generating the feedback content, the generation unit can adjust the form of the feedback based on the learning style of the person being evaluated. For example, if the person being evaluated has a visual learning style, feedback using graphs and diagrams can be provided. If the person being evaluated has an auditory learning style, feedback using audio messages can be provided. Furthermore, if the person being evaluated has a practical learning style, feedback including a specific action plan can be provided. In this way, the generation unit can provide optimal feedback according to the learning style of the person being evaluated.

[0103] When providing the feedback content, the providing unit can adjust the way of expressing the feedback in consideration of the cultural background of the person being evaluated. For example, if the person being evaluated has a different cultural background, the feedback can be provided using an expression method suitable for that culture. If the person being evaluated is based on Japanese culture, the feedback can be provided using honorific language and polite expressions. Also, if the person being evaluated is based on American culture, the feedback can be provided using direct and clear expressions. Furthermore, the providing unit can customize the content of the feedback according to the cultural background of the person being evaluated. This allows the providing unit to provide optimal feedback according to the cultural background of the person being evaluated.

[0104] The collection unit can estimate the emotion of the person being evaluated and adjust the method of data collection based on the estimated emotion. For example, if the person being evaluated is feeling stressed, the method of data collection can be changed to collect data in a more relaxed state. If the person being evaluated is relaxed, detailed data can be collected. Also, if the person being evaluated is in a hurry, the necessary data can be collected efficiently in a short time. Furthermore, the collection unit can adjust the timing of data collection according to the emotion of the person being evaluated. In this way, the collection unit can provide an optimal data collection method according to the emotion of the person being evaluated.

[0105] The analysis unit can estimate the emotions of the person being evaluated and adjust the analysis method based on the estimated emotions. For example, if the person being evaluated is feeling stressed, the analysis method can be changed to provide a more concise and easy-to-understand analysis result. If the person being evaluated is relaxed, a detailed analysis result can be provided. Also, if the person being evaluated is in a hurry, a concise analysis result that focuses on the main points can be provided. Furthermore, the analysis unit can adjust the timing of the analysis according to the emotions of the person being evaluated. In this way, the analysis unit can provide the optimal analysis method according to the emotions of the person being evaluated.

[0106] The generation unit can estimate the emotion of the person being evaluated and adjust the tone of the feedback content based on the estimated emotion. For example, if the person being evaluated is stressed, the feedback can be provided in a gentle tone. If the person being evaluated is relaxed, detailed feedback can be provided. Also, if the person being evaluated is in a hurry, concise feedback that focuses on the main points can be provided. Furthermore, the generation unit can customize the feedback content according to the emotion of the person being evaluated. In this way, the generation unit can provide optimal feedback according to the emotion of the person being evaluated.

[0107] The providing unit can estimate the emotion of the person being evaluated and adjust the method of providing feedback based on the estimated emotion. For example, if the person being evaluated is stressed, feedback can be provided by email. If the person being evaluated is relaxed, feedback can be provided in person. Also, if the person being evaluated is in a hurry, feedback that covers the main points can be provided in a short period of time. Furthermore, the providing unit can adjust the timing of the feedback according to the emotion of the person being evaluated. In this way, the providing unit can provide the optimal feedback providing method according to the emotion of the person being evaluated.

[0108] The providing unit can estimate the emotion of the person being evaluated and adjust the content of the feedback based on the estimated emotion. For example, if the person being evaluated is stressed, positive feedback can be preferentially provided. If the person being evaluated is relaxed, detailed feedback can be provided. Also, if the person being evaluated is in a hurry, concise feedback that focuses on the main points can be provided. Furthermore, the providing unit can customize the content of the feedback according to the emotion of the person being evaluated. In this way, the providing unit can provide optimal feedback content according to the emotion of the person being evaluated.

[0109] The process flow of the second embodiment will be briefly described below.

[0110] Step 1: The collection unit collects data. The data includes, for example, text data, numerical data, image data, and the like. The collection unit collects data from evaluators and evaluators. The evaluators and evaluators include, for example, employees, students, and customers. The collection unit can also collect self-reporting from the evaluators and the evaluation data from the past few times. For example, the collection unit collects self-reporting data through questionnaires and interviews, and collects evaluation data from the past year. Step 2: The analysis unit analyzes the collected data. The analysis includes, for example, statistical analysis and machine learning algorithms. The analysis unit analyzes the collected data and understands the characteristics of the evaluator and the evaluee. The characteristics include, for example, skills, performance, behavioral characteristics, etc. Step 3: The generation unit generates feedback content based on the analysis results. The feedback content includes, for example, text feedback and numerical evaluation. The generation unit generates optimal feedback content for the evaluator and the person being evaluated based on the analysis results. For example, if the person being evaluated is a new employee, the generation unit provides specific advice on how to improve skills. If the person being evaluated is a veteran employee, the generation unit provides feedback on demonstrating leadership. Step 4: The providing unit provides the generated feedback content. For example, the providing unit provides the generated feedback content by email, dashboard, report, etc. The providing unit provides the generated feedback content to the evaluator and the evaluee. For example, the providing unit sends the feedback content by email, displays the feedback content through a dashboard, or provides the feedback content in the form of a report.

[0111] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0113] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data from the evaluator and the evaluee using the camera 42 and microphone 38B of the smart device 14. The analysis unit analyzes the data collected, for example, by the specific processing unit 290 of the data processing device 12. The generation unit generates feedback content based on the analysis result, for example, by the specific processing unit 290 of the data processing device 12. The provision unit provides the feedback content generated, for example, by the control unit 46A of the smart device 14 to the evaluator and the evaluee. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0122] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0125] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0127] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data from the evaluator and the evaluee using the camera 42 and the microphone 238 of the smart glasses 214. The analysis unit analyzes the data collected, for example, by the specific processing unit 290 of the data processing device 12. The generation unit generates feedback content based on the analysis result, for example, by the specific processing unit 290 of the data processing device 12. The provision unit provides the feedback content generated, for example, by the control unit 46A of the smart glasses 214 to the evaluator and the evaluee. The correspondence between each unit and the device and the control unit is not limited to the above-mentioned example, and various changes are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0133] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0138] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0141] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects data from the evaluator and the evaluee using the camera 42 and microphone 238 of the headset type terminal 314. The analysis unit analyzes the data collected, for example, by the specific processing unit 290 of the data processing device 12. The generation unit generates feedback content based on the analysis result, for example, by the specific processing unit 290 of the data processing device 12. The provision unit provides the feedback content generated, for example, by the control unit 46A of the headset type terminal 314 to the evaluator and the evaluee. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0155] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0156] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0158] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0159] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0163] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data from the evaluator and the evaluee using the camera 42 and microphone 238 of the robot 414. The analysis unit analyzes the data collected, for example, by the specific processing unit 290 of the data processing device 12. The generation unit generates feedback content based on the analysis result, for example, by the specific processing unit 290 of the data processing device 12. The provision unit provides the feedback content generated, for example, by the control unit 46A of the robot 414 to the evaluator and the evaluee. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0164] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0165] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0167] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0168] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0169] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0172] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0173] In addition, 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 upon request from the data processing device 12.

[0174] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0175] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0176] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0177] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration in which a processor is used that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0178] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0180] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0182] (Appendix 1) A collection unit that collects data; an analysis unit that analyzes the data collected by the collection unit; a generation unit that generates feedback content based on the analysis result obtained by the analysis unit; a providing unit that provides the feedback content generated by the generating unit; Equipped A system characterized by: (Appendix 2) The collecting unit includes: Collect data from the rater or ratee 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze the collected data to understand the characteristics of the evaluator or the person being evaluated 2. The system of claim 1, (Appendix 4) The generation unit is Based on the analysis results, generate appropriate feedback for the evaluator or the person being evaluated. 2. The system of claim 1, (Appendix 5) The providing unit is Provide generated feedback to appraisers and appraisers 2. The system of claim 1, (Appendix 6) The collecting unit includes: Collect self-reported or past evaluation data from ratees 2. The system of claim 1, (Appendix 7) The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1, (Appendix 8) The collecting unit includes: Analyze the past feedback history of ratees and choose the most appropriate data collection method 2. The system of claim 1, (Appendix 9) The collecting unit includes: During data collection, filtering is done based on the assessee's current projects and areas of interest 2. The system of claim 1, (Appendix 10) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting data, prioritize collection of relevant data by taking into account the geographic location of the assessee. 2. The system of claim 1, (Appendix 12) The collecting unit includes: During data collection, analyze the assessee's social media activity and collect relevant data 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of detail based on the importance of the data 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, apply different analysis algorithms depending on the category of data 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when the data was collected 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on data relevance 2. The system of claim 1, (Appendix 19) The generation unit is Estimate the user's emotions and adjust the way feedback is presented based on the estimated user emotions. 2. The system of claim 1, (Appendix 20) The generation unit is When generating feedback, adjust the level of detail of the feedback based on the characteristics of the ratee 2. The system of claim 1, (Appendix 21) The generation unit is When generating feedback, apply different feedback algorithms depending on the appraisee's past evaluation history 2. The system of claim 1, (Appendix 22) The generation unit is The method estimates the user's emotion and adjusts the length of the feedback content based on the estimated user's emotion. 2. The system of claim 1, (Appendix 23) The generation unit is When generating feedback, prioritize it based on when the assessee submitted it 2. The system of claim 1, (Appendix 24) The generation unit is During feedback generation, adjust the order of feedback based on the relevance of the ratee 2. The system of claim 1, (Appendix 25) The providing unit is Inferring a user's emotions and adjusting the way feedback is provided based on the inferred user emotions 2. The system of claim 1, (Appendix 26) The providing unit is When providing feedback, the appraiser's past feedback history is referenced to select the most appropriate method of providing feedback. 2. The system of claim 1, (Appendix 27) The providing unit is Customize the delivery method based on the ratee's current situation when providing feedback 2. The system of claim 1, (Appendix 28) The providing unit is A method for determining a feedback order based on a user's emotions and the user's emotions. 2. The system of claim 1, (Appendix 29) The providing unit is When providing feedback, the most appropriate method is selected based on the geographic location of the person being assessed. 2. The system of claim 1, (Appendix 30) The providing unit is When providing feedback, analyze the assessee's social media activity to suggest ways to provide it 2. The system of claim 1, [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects data; an analysis unit that analyzes the data collected by the collection unit; a generation unit that generates feedback content based on the analysis result obtained by the analysis unit; a providing unit that provides the feedback content generated by the generating unit; Equipped A system characterized by:

2. The collecting unit includes: Collect data from the rater or ratee 2. The system of claim 1 .

3. The analysis unit is Analyze the collected data to understand the characteristics of the evaluator or the person being evaluated 2. The system of claim 1 .

4. The generation unit is Based on the analysis results, generate appropriate feedback for the evaluator or the person being evaluated.

2. The system of claim 1 .

5. The providing unit is The feedback content generated by the generation unit is provided to the evaluator and the evaluee.

5. The system of claim 4.

6. The collecting unit includes: Collect self-reported or past evaluation data from ratees 2. The system of claim 1 .

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

8. The collecting unit includes: Analyze the past feedback history of ratees and choose the most appropriate data collection method 2. The system of claim 1 .

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