system

Abstract

The system of the embodiment aims to analyze 1-on-1 data and provide regular feedback on employees' strengths and advantages. [Solution] A system according to an embodiment includes a collection unit, a reading unit, an analysis unit, an extraction unit, and a feedback unit. The collection unit collects one-on-one data. The reading unit loads the data collected by the collection unit into a generation AI. The analysis unit analyzes the data loaded by the reading unit. The extraction unit extracts strengths and advantages from the data analyzed by the analysis unit. The feedback unit provides regular feedback based on the information extracted by the extraction unit.

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not effectively utilize 1-on-1 data to identify employees' strengths and merits and provide regular feedback, so there is room for improvement.

[0005] The system of the embodiment aims to analyze 1-on-1 data and provide regular feedback on employees' strengths and advantages. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a reading unit, an analysis unit, an extraction unit, and a feedback unit. The collection unit collects one-on-one data. The reading unit loads the data collected by the collection unit into a generation AI. The analysis unit analyzes the data loaded by the reading unit. The extraction unit extracts strengths and advantages from the data analyzed by the analysis unit. The feedback unit periodically provides feedback based on the information extracted by the extraction unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze 1-on-1 data and provide regular feedback on employees' strengths and advantages. [Brief explanation of the drawings]

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

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) A feedback system according to an embodiment of the present invention loads one-on-one (1-on-1) data into a generation AI and periodically provides feedback on each employee's strengths and merits. This feedback system collects one-on-one data and loads it into a generation AI. The generation AI then analyzes the data and extracts each employee's strengths and merits. Finally, feedback is provided periodically based on the extracted information. For example, when collecting one-on-one data, data such as the content of one-on-one conversations and notes between each employee and their supervisor is collected. For example, information such as the type of work each employee performs, the results they achieve, and the challenges they face is collected. The collected data is then loaded into a generation AI. The generation AI analyzes the collected data and extracts each employee's strengths and merits. For example, if an employee achieves excellent results in a specific project, the results are extracted as strengths. Similarly, if an employee excels in teamwork, the results are extracted as merits. Finally, feedback is provided periodically based on the extracted information. For example, feedback is provided to each employee once a month. This feedback includes the strengths and merits extracted by the generation AI. This allows employees to recognize their own strengths and good points and increase their motivation. This tool allows regular feedback on each employee's strengths and good points, which is expected to increase employee motivation and improve work efficiency. It also makes it easier for managers to understand employees' strengths and good points, allowing them to provide appropriate guidance and support. This means that the feedback system can provide regular feedback on employees' strengths and good points.

[0029] A feedback system according to an embodiment includes a collection unit, a reading unit, an analysis unit, an extraction unit, and a feedback unit. The collection unit collects one-on-one data. The one-on-one data includes, but is not limited to, conversation content, notes, work results, and assignments. For example, the collection unit collects conversation content and notes as text data. The collection unit can also collect data related to work results and assignments. For example, the collection unit collects reports submitted by employees and project progress. The collection unit can also collect audio data and image data. For example, the collection unit records one-on-one conversations and collects the audio data. The reading unit loads the data collected by the collection unit into a generation AI. For example, the reading unit inputs text data into the generation AI. The reading unit can also load audio data and image data into the generation AI. For example, the reading unit converts audio data into text data and inputs the text data into the generation AI. The analysis unit uses the generation AI to analyze the data loaded by the reading unit. The analysis unit analyzes data using, for example, natural language processing technology or machine learning algorithms. For example, the analysis unit analyzes text data using morphological analysis. The analysis unit can also analyze the structure of data using grammatical analysis. Furthermore, the analysis unit can analyze data using machine learning algorithms such as K-means and hierarchical clustering. The extraction unit extracts strengths and good points from the data analyzed by the analysis unit. For example, if an employee has achieved excellent results in a specific project, the extraction unit extracts those results as strengths. The extraction unit can also extract excellent teamwork as good points. For example, the extraction unit extracts strengths based on the project success rate and goals achieved. The feedback unit provides regular feedback based on the information extracted by the extraction unit. For example, the feedback unit provides feedback to each employee once a month. The feedback unit can also include advice on areas for improvement and next steps in the content of the feedback. For example, the feedback unit provides specific advice on areas for improvement and next steps in addition to the employee's strengths and good points.As a result, the feedback system according to the embodiment can periodically provide feedback on employees' strengths and good points. Some or all of the above-described processing in the feedback unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the feedback unit can provide feedback based on information extracted by the generation AI. For example, the output unit displays the feedback results to employees or their superiors via a web application or mobile application. If feedback on paper is desired, the results are printed using a printer. Sending the results via email provides quick feedback by sending the results directly to employees or their superiors. Some or all of the above-described processing in the output unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0030] The collection unit can collect data related to conversation content or notes, work results, and issues. For example, the collection unit collects conversation content and notes as text data. For example, the collection unit records one-on-one conversation content and converts the audio data into text data. The collection unit can also collect data related to work results and issues. For example, the collection unit collects reports submitted by employees and project progress. The collection unit can also collect audio data and image data. For example, the collection unit records one-on-one conversations and collects the audio data. This allows the collection unit to collect a variety of data, enabling more detailed analysis. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input audio data into a generation AI and have the generation AI convert the audio data into text data.

[0031] The analysis unit can analyze data using natural language processing technology or a machine learning algorithm. The analysis unit analyzes data using, for example, natural language processing technology. For example, the analysis unit analyzes text data using morphological analysis. The analysis unit can also analyze the structure of data using grammatical analysis. Furthermore, the analysis unit can analyze data using a machine learning algorithm. For example, the analysis unit analyzes data using a machine learning algorithm such as K-means or hierarchical clustering. This allows the analysis unit to use advanced technology, thereby improving the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can provide analysis results based on data analyzed by a generation AI.

[0032] When a significant result is achieved in a specific project, the extraction unit can extract the result as a strength. For example, when a significant result is achieved in a specific project, the extraction unit extracts the result as a strength. For example, the extraction unit extracts strengths based on the project's success rate and goals achieved. The extraction unit can also evaluate the project's progress and deliverables and extract strengths based on the evaluation results. Furthermore, the extraction unit can extract strengths based on an evaluation of the project's leadership and teamwork. For example, the extraction unit evaluates the project's leadership skills and the quality of teamwork and extracts strengths based on the evaluation results. In this way, the extraction unit extracts specific results as strengths, thereby improving the quality of feedback. Some or all of the above-mentioned processing in the extraction unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the extraction unit can extract strengths based on information extracted by a generation AI.

[0033] If teamwork is good, the extraction unit can extract that point as a good point. For example, if teamwork is good, the extraction unit extracts that point as a good point. For example, the extraction unit extracts good points based on the team's level of cooperation and the quality of communication. The extraction unit can also evaluate the team's performance and results and extract good points based on the evaluation results. Furthermore, the extraction unit can evaluate the team's leadership and cooperation system and extract good points based on the evaluation results. For example, the extraction unit evaluates the team's leadership skills and the quality of cooperation system and extracts good points based on the evaluation results. In this way, the extraction unit extracts good teamwork, making the feedback more comprehensive. Some or all of the above-mentioned processing in the extraction unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the extraction unit can extract good points based on information extracted by a generation AI.

[0034] The feedback unit can provide feedback to each employee monthly. The feedback unit can provide feedback to each employee, for example, once a month. For example, the feedback unit can set a schedule for providing regular monthly feedback. The feedback unit can also prepare the content of feedback in advance and provide feedback to each employee at an appropriate time. Furthermore, the feedback unit can evaluate the effectiveness of the feedback and reflect it in the next feedback. For example, the feedback unit can evaluate the employee's reaction and the effectiveness of the feedback, and adjust the content of the next feedback based on the evaluation results. In this way, the feedback unit can provide regular feedback, thereby improving employee motivation. Some or all of the above-mentioned processing in the feedback unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the feedback unit can provide feedback based on information extracted by a generation AI.

[0035] The feedback unit may include advice regarding areas for improvement or next steps in the feedback content. For example, the feedback unit may include advice regarding areas for improvement or next steps in the feedback content. For example, the feedback unit may provide specific advice regarding areas for improvement or next steps in addition to the employee's strengths and good points. The feedback unit may also propose a specific action plan to improve the employee's work performance. For example, the feedback unit may propose training programs or learning resources to improve the employee's skills. Furthermore, the feedback unit may provide advice regarding the employee's career path. For example, the feedback unit may propose a job title or job content as the next step based on the employee's career goals. In this way, the feedback unit's provision of specific advice promotes the employee's growth. Some or all of the above-described processing in the feedback unit may be performed using, or without, a generation AI. For example, the feedback unit may provide advice based on information extracted by a generation AI.

[0036] The feedback section may describe, as an effect of the feedback, that the feedback is expected to improve employee motivation or work efficiency. For example, the feedback section may describe, as an effect of the feedback, that the feedback is expected to improve employee motivation or work efficiency. For example, the feedback section may describe specific effects for improving employee motivation. For example, the feedback section may evaluate the effect of the feedback based on an employee satisfaction survey or work productivity indicators. The feedback section may also describe specific effects for improving work efficiency. For example, the feedback section may describe specific examples of work efficiency improvement or process improvement. Furthermore, the feedback section may set indicators for quantitatively evaluating the effect of the feedback. For example, the feedback section may evaluate the effect of the feedback based on the employee's work performance or goal achievement rate. In this way, the feedback section's explicit statement of the effect emphasizes the importance of feedback. Some or all of the above-described processing in the feedback section may be performed using, or without, a generation AI. For example, the feedback section may describe the effect based on information extracted by a generation AI.

[0037] The feedback system includes a collection unit that customizes the type of data to be collected according to the employee's job content or position. For example, for sales employees, the collection unit focuses on collecting data on conversations with customers and the progress of business negotiations. For example, the collection unit collects details of business negotiations conducted by sales employees and interactions with customers. For technical employees, the collection unit can also collect data on project progress and technical issues. For example, the collection unit collects progress reports and technical issues for projects handled by technical employees. For managerial employees, the collection unit can also collect team performance and feedback from subordinates. For example, the collection unit collects the content of team meetings conducted by managerial employees and feedback from subordinates. This allows the collection unit to customize the type of data, enabling more appropriate data collection. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can collect data based on data collection criteria customized by the generation AI.

[0038] The feedback system includes a collection unit that collects employee self-assessments and feedback from colleagues when collecting data. For example, when an employee conducts a self-assessment, the collection unit collects the content of the assessment as data. For example, the collection unit collects the content of the employee's self-assessment as digital data. The collection unit can also periodically collect feedback from colleagues to identify the employee's strengths and areas for improvement. For example, the collection unit collects feedback sheets and survey results filled out by colleagues. Furthermore, the collection unit can compare the employee's self-assessment with feedback from colleagues to collect comprehensive assessment data. For example, the collection unit integrates the employee's self-assessment and feedback from colleagues to create a comprehensive assessment report. By collecting self-assessments and feedback from colleagues, the collection unit can collect more comprehensive data. Some or all of the above-described processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can collect data based on the self-assessments and feedback from colleagues collected by the generation AI.

[0039] The feedback system includes a collection unit that prioritizes collecting highly relevant data by taking into account the employee's geographic location information when collecting data. For example, when an employee is on a business trip, the collection unit prioritizes collecting the work content and results of the business trip. For example, the collection unit collects details and results of the work performed by the employee while on the business trip. In addition, when an employee is working remotely, the collection unit can prioritize collecting work data in a remote environment. For example, the collection unit collects progress and issues of work performed by the employee while working remotely. Furthermore, when an employee works at the head office, the collection unit can prioritize collecting work data in the office. For example, the collection unit collects details and results of the work performed by the employee at the head office. This allows the collection unit to collect more relevant data by taking the geographic location information into account. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can collect data based on the geographic location information collected by the generation AI.

[0040] The feedback system includes a collection unit that analyzes employees' social media activities and collects related data during data collection. The collection unit, for example, collects work-related posts shared by employees on social media. For example, the collection unit collects work-related information and opinions posted by employees on social media. The collection unit can also collect work-related trends and opinions from employees' social media activities. For example, the collection unit analyzes topics and hashtags followed by employees on social media to collect related information. Furthermore, the collection unit can analyze employees' social media networks to collect work-related personal connection data. For example, the collection unit analyzes the professional networks to which employees are connected on social media and collects related personal connection data. This allows the collection unit to analyze social media activities and collect more comprehensive data. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can collect data based on social media data analyzed by a generation AI.

[0041] The feedback system includes a reading unit that adjusts the level of detail of reading based on the importance of data during reading. For example, the reading unit reads data with high importance in detail and reads data with low importance in a simplified manner. For example, the reading unit evaluates the importance of the data and performs a detailed analysis on the data with high importance. The reading unit can also read additional metadata for the data with high importance. For example, the reading unit collects metadata related to the data with high importance and performs a detailed analysis. Furthermore, the reading unit can also read only an outline of the data with low importance. For example, the reading unit collects an outline of the data with low importance and performs a simplified analysis. This enables efficient data reading by adjusting the level of detail of reading based on the importance of the data. Some or all of the above-described processing in the reading unit may be performed using, or without, a generation AI. For example, the reading unit can adjust the level of detail of reading based on the importance of the data evaluated by the generation AI.

[0042] The feedback system includes a reading unit that applies different reading algorithms depending on the category of data during reading. The reading unit, for example, applies a natural language processing algorithm to text data. For example, the reading unit applies natural language processing algorithms such as morphological analysis and grammatical analysis to analyze the text data. The reading unit can also apply an image recognition algorithm to image data. For example, the reading unit reads the data using image recognition technology to analyze the image data. The reading unit can also apply a voice recognition algorithm to audio data. For example, the reading unit reads the data using voice recognition technology to analyze the audio data. This enables efficient data reading by applying an appropriate algorithm depending on the category of the data. Some or all of the above-described processing in the reading unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reading unit can apply an appropriate algorithm based on the category of the data analyzed by the generation AI.

[0043] The feedback system includes a reading unit that, at the time of reading, determines a reading priority based on the time of data submission. The reading unit, for example, prioritizes reading the most recent data. For example, the reading unit evaluates the time of data submission and prioritizes reading the most recent data. The reading unit can also postpone data that was submitted earlier. For example, the reading unit postpones data that was submitted earlier and prioritizes reading the most recent data. Furthermore, the reading unit can also prioritize reading important data based on the time of submission. For example, the reading unit uses the time of submission as a criterion for prioritizing reading important data. This enables efficient data reading by determining the priority based on the time of data submission. Some or all of the above-described processing in the reading unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reading unit can determine the reading priority based on the time of data submission evaluated by the generation AI.

[0044] The feedback system includes a reading unit that adjusts the reading order based on the relevance of data during reading. The reading unit, for example, prioritizes reading highly relevant data. For example, the reading unit evaluates the relevance of data and prioritizes reading highly relevant data. The reading unit can also postpone low-relevance data. For example, the reading unit postpones low-relevance data and prioritizes reading highly relevant data. Furthermore, the reading unit can also determine an efficient reading order based on the relevance of data. For example, the reading unit determines an efficient reading order based on the relevance of data. As a result, adjusting the reading order based on the relevance of data enables efficient data reading. Some or all of the above-described processing in the reading unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reading unit can adjust the reading order based on the relevance of data evaluated by the generation AI.

[0045] The feedback system includes an analysis unit that improves the accuracy of the analysis by taking into account the interrelationships between data during analysis. The analysis unit, for example, analyzes the correlations between data to improve the accuracy. For example, the analysis unit performs a correlation analysis of data and evaluates the relationships between the data. The analysis unit can also perform analysis by taking into account the interdependence of data. For example, the analysis unit evaluates the dependency of data and performs analysis based on interrelated data. Furthermore, the analysis unit can obtain more accurate analysis results based on the interrelationships between data. For example, the analysis unit evaluates the interrelationships between data and performs analysis based on highly related data. In this way, the accuracy of the analysis is improved by taking the interrelationships between data into account. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can perform analysis based on the interrelationships between data evaluated by the generation AI.

[0046] The feedback system includes an analysis unit that performs analysis taking into account attribute information of the data submitter during analysis. The analysis unit performs analysis taking into account, for example, the submitter's job title and job content. For example, the analysis unit evaluates the submitter's job title and job content and analyzes the data based on the evaluation. The analysis unit can also perform analysis based on the submitter's past performance. For example, the analysis unit evaluates the submitter's past performance data and analyzes the data based on the evaluation. Furthermore, the analysis unit can also perform analysis taking into account the submitter's role within the team. For example, the analysis unit evaluates the submitter's role within the team and analyzes the data based on the evaluation. This enables more appropriate analysis by taking into account the attribute information of the data submitter. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can perform analysis based on the submitter's attribute information evaluated by the generation AI.

[0047] The feedback system includes an analysis unit that performs analysis taking into account the geographical distribution of data during analysis. The analysis unit, for example, performs analysis for each region based on the geographical distribution of the data. For example, the analysis unit evaluates the geographical distribution of the data and analyzes trends for each region. The analysis unit can also analyze region-specific trends taking the geographical distribution into account. For example, the analysis unit evaluates data for each region and analyzes trends specific to each region. The analysis unit can also analyze performance for each region based on the geographical distribution. For example, the analysis unit evaluates data for each region and analyzes performance for each region. This makes it possible to analyze region-specific trends by taking the geographical distribution of the data into account. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can perform analysis based on geographical distribution data evaluated by the generation AI.

[0048] The feedback system includes an analysis unit that, during analysis, refers to related literature of the data to improve the accuracy of the analysis. The analysis unit, for example, refers to related literature to improve the accuracy of the analysis. For example, the analysis unit evaluates related literature and complements the analysis results. The analysis unit can also complement the analysis results based on data from the related literature. For example, the analysis unit evaluates data from the related literature and complements the analysis results based on the evaluation. Furthermore, the analysis unit can also adjust the analysis criteria based on the related literature. For example, the analysis unit evaluates data from the related literature and adjusts the analysis criteria based on the evaluation. This improves the accuracy of the analysis by referring to the related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can perform analysis based on related literature data evaluated by the generation AI.

[0049] The feedback system includes an extraction unit that improves the accuracy of extraction by taking into account the interrelationships between data during extraction. The extraction unit extracts highly accurate information based on, for example, correlations between data. For example, the extraction unit performs a correlation analysis of data and evaluates the relationships between data. The extraction unit can also extract important information by taking into account the interdependence of data. For example, the extraction unit evaluates the dependency of data and extracts information based on interrelated data. Furthermore, the extraction unit can extract highly related information based on the interrelationships between data. For example, the extraction unit evaluates the interrelationships between data and extracts information based on highly related information. This improves the accuracy of extraction by taking into account the interrelationships between data. Some or all of the above-described processing in the extraction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the extraction unit can extract information based on the interrelationships between data evaluated by the generation AI.

[0050] The feedback system includes an extraction unit that performs extraction while taking into account attribute information of the data submitter. The extraction unit extracts important information, for example, by taking into account the submitter's job title and job content. For example, the extraction unit evaluates the submitter's job title and job content and extracts information based on the evaluation. The extraction unit can also extract highly relevant information based on the submitter's past performance. For example, the extraction unit evaluates the submitter's past performance data and extracts information based on the evaluation. Furthermore, the extraction unit can extract important information by taking into account the submitter's role within the team. For example, the extraction unit evaluates the submitter's role within the team and extracts information based on the evaluation. This enables more appropriate information extraction by taking into account the attribute information of the data submitter. Some or all of the above-described processing in the extraction unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the extraction unit can extract information based on the submitter's attribute information evaluated by the generation AI.

[0051] The feedback system includes an extraction unit that performs extraction while taking into account the geographical distribution of data. The extraction unit, for example, extracts important information for each region based on the geographical distribution of the data. For example, the extraction unit evaluates the geographical distribution of the data and extracts trends for each region. The extraction unit can also extract region-specific trends by taking the geographical distribution into account. For example, the extraction unit evaluates data for each region and extracts trends specific to each region. The extraction unit can also extract performance for each region based on the geographical distribution. For example, the extraction unit evaluates data for each region and extracts performance for each region. This makes it possible to extract region-specific trends by taking the geographical distribution of the data into account. Some or all of the above-described processing in the extraction unit may be performed using, or without, a generation AI. For example, the extraction unit can extract information based on geographical distribution data evaluated by the generation AI.

[0052] The feedback system includes an extraction unit that, during extraction, refers to related literature of the data to improve the accuracy of the extraction. The extraction unit, for example, refers to related literature to extract highly accurate information. For example, the extraction unit evaluates related literature and complements the extraction results. The extraction unit can also complement the extraction results based on data from related literature. For example, the extraction unit evaluates data from related literature and complements the extraction results based on the evaluation. Furthermore, the extraction unit can also adjust the extraction criteria based on the related literature. For example, the extraction unit evaluates data from related literature and adjusts the extraction criteria based on the evaluation. This improves the accuracy of the extraction by referring to related literature. Some or all of the above-mentioned processing in the extraction unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the extraction unit can extract information based on related literature data evaluated by the generation AI.

[0053] The feedback system includes a feedback unit that, when providing feedback, provides optimal feedback by referring to past feedback history. The feedback unit, for example, provides continuous improvement points based on the past feedback history. For example, the feedback unit evaluates the past feedback history and provides continuous improvement points. The feedback unit can also highlight areas of growth from the past feedback history. For example, the feedback unit evaluates the past feedback history and highlights areas of growth. Furthermore, the feedback unit can also provide consistent feedback by referring to the past feedback history. For example, the feedback unit evaluates the past feedback history and provides consistent feedback. This enables more consistent feedback by referring to the past feedback history. Some or all of the above-described processing in the feedback unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the feedback unit can provide feedback based on the past feedback history evaluated by the generation AI.

[0054] The feedback system includes a feedback unit that customizes the feedback method according to the employee's job title and job content. For example, the feedback unit provides a sales employee with feedback regarding customer service and sales negotiation procedures. For example, the feedback unit provides the sales employee with feedback regarding details of sales negotiations and customer service procedures. The feedback unit can also provide technical employees with feedback regarding technical skills and project progress. For example, the feedback unit provides feedback regarding the progress of projects and technical issues handled by technical employees. The feedback unit can also provide managerial employees with feedback regarding team management and leadership. For example, the feedback unit provides feedback regarding the content of team meetings held by managerial employees and their leadership skills. This enables more appropriate feedback by providing feedback according to the employee's job title and job content. Some or all of the above-described processing in the feedback unit may be performed using, or without, a generation AI. For example, the feedback unit can provide feedback based on the employee's job title and job content evaluated by the generation AI.

[0055] The feedback system includes a feedback unit that provides optimal feedback by taking into account the geographic location information of an employee. For example, the feedback unit provides feedback regarding work performed at the business trip destination to an employee who is on a business trip. For example, the feedback unit provides feedback regarding details and results of work performed by the employee who is on a business trip. The feedback unit can also provide feedback regarding work performed in a remote environment to an employee who is working remotely. For example, the feedback unit provides feedback regarding the progress and challenges of work performed by the employee who is working remotely. Furthermore, the feedback unit can also provide feedback regarding work performed in the office to an employee working at the head office. For example, the feedback unit provides feedback regarding details and results of work performed by the employee working at the head office. This enables more appropriate feedback by taking into account the geographic location information of the employee. Some or all of the above-described processing in the feedback unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the feedback unit can provide feedback based on geographic location information evaluated by the generation AI.

[0056] The feedback system includes a feedback unit that analyzes an employee's social media activity and adjusts the content of the feedback when providing feedback. The feedback unit provides feedback based on, for example, work-related posts shared by the employee on social media. For example, the feedback unit provides feedback based on work-related information and opinions posted by the employee on social media. The feedback unit can also incorporate work-related trends and opinions from the employee's social media activity into the feedback. For example, the feedback unit analyzes topics and hashtags followed by the employee on social media and incorporates related information into the feedback. The feedback unit can also analyze the employee's social media network and incorporate work-related personal connection data into the feedback. For example, the feedback unit analyzes the employee's professional network of connections on social media and incorporates related personal connection data into the feedback. This enables more appropriate feedback by analyzing the employee's social media activity. Some or all of the above-described processing in the feedback unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the feedback unit can adjust the content of the feedback based on social media data analyzed by the generation AI.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The feedback system may also include a career goal setting unit that customizes the content of feedback taking into account the employee's career goals. For example, the career goal setting unit may collect the short-term and long-term career goals set by the employee and provide feedback based on them. The career goal setting unit may also provide specific advice to strengthen the skills and experience related to the employee's career goals. Furthermore, the career goal setting unit may suggest appropriate training programs and learning resources based on the employee's career goals. This allows the system to provide feedback tailored to the employee's career goals and support their career growth.

[0059] The feedback system may also be equipped with a real-time performance monitoring unit that monitors employee performance data in real time and provides feedback according to fluctuations in performance. For example, the real-time performance monitoring unit tracks an employee's work progress and results in real time, and if performance declines, it immediately provides feedback on areas for improvement. It can also provide feedback that highlights improvements in performance if performance improves. Furthermore, the real-time performance monitoring unit can adjust the timing of feedback based on performance data. This allows timely feedback to be provided according to employee performance.

[0060] The feedback system may also include a social media analysis unit that analyzes employees' social media activities and reflects work-related trends and opinions in the feedback. For example, the social media analysis unit may analyze the topics and hashtags that employees follow on social media and reflect the related information in the feedback. The social media analysis unit may also analyze employees' social media networks and reflect work-related personal connection data in the feedback. Furthermore, the social media analysis unit may provide feedback based on work-related posts that employees have shared on social media. In this way, more appropriate feedback can be provided by analyzing employees' social media activities.

[0061] The feedback system may also include a work environment monitoring unit that monitors an employee's work environment and adjusts the content of the feedback based on the work environment. For example, if an employee is working remotely, the work environment monitoring unit may provide feedback regarding work in the remote environment. If an employee is working in the office, the work environment monitoring unit may also provide feedback regarding work in the office environment. Furthermore, if an employee is on a business trip, the work environment monitoring unit may also provide feedback regarding work performed at the business trip destination. In this way, appropriate feedback is provided according to the employee's work environment.

[0062] The feedback system may also include a performance evaluation unit that evaluates an employee's work performance and adjusts the content of the feedback based on the evaluation results. For example, the performance evaluation unit evaluates an employee's work progress and results, and if performance is high, provides feedback that highlights those results. If performance is low, it may also provide feedback that includes specific areas for improvement. Furthermore, the performance evaluation unit may adjust the timing of feedback based on the evaluation results. This allows appropriate feedback to be provided according to the employee's work performance.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The collection unit collects one-on-one data. One-on-one data includes conversation content, notes, work results, and issues. The collection unit collects conversation content and notes as text data, and also collects data related to work results and issues. In addition, audio data and image data can also be collected. For example, one-on-one conversations can be recorded and the audio data collected. Step 2: The reading unit reads the data collected by the collection unit into the generation AI. The reading unit inputs text data into the generation AI, and can also read audio data and image data into the generation AI. For example, the reading unit converts audio data into text data and inputs that text data into the generation AI. Step 3: The analysis unit uses the generative AI to analyze the data read by the reading unit. The analysis unit analyzes the data using natural language processing techniques and machine learning algorithms. For example, the analysis unit analyzes the data using morphological analysis, grammatical analysis, and machine learning algorithms such as K-means and hierarchical clustering. Step 4: The extraction unit extracts strengths and positive aspects from the data analyzed by the analysis unit. For example, if a company has achieved excellent results in a particular project, it will extract that result as a strength, and if the company has excellent teamwork, it will extract that as a positive aspect. Strengths are extracted based on the project's success rate and the goals achieved. Step 5: The feedback unit provides regular feedback based on the information extracted by the extraction unit. For example, feedback can be provided to each employee once a month, and the feedback can include advice on areas for improvement and next steps. The feedback unit can provide feedback based on the information extracted by the generation AI.

[0065] (Example 2) A feedback system according to an embodiment of the present invention loads one-on-one (1-on-1) data into a generation AI and periodically provides feedback on each employee's strengths and merits. This feedback system collects one-on-one data and loads it into a generation AI. The generation AI then analyzes the data and extracts each employee's strengths and merits. Finally, feedback is provided periodically based on the extracted information. For example, when collecting one-on-one data, data such as the content of one-on-one conversations and notes between each employee and their supervisor is collected. For example, information such as the type of work each employee performs, the results they achieve, and the challenges they face is collected. The collected data is then loaded into a generation AI. The generation AI analyzes the collected data and extracts each employee's strengths and merits. For example, if an employee achieves excellent results in a specific project, the results are extracted as strengths. Similarly, if an employee excels in teamwork, the results are extracted as merits. Finally, feedback is provided periodically based on the extracted information. For example, feedback is provided to each employee once a month. This feedback includes the strengths and merits extracted by the generation AI. This allows employees to recognize their own strengths and good points and increase their motivation. This tool allows regular feedback on each employee's strengths and good points, which is expected to increase employee motivation and improve work efficiency. It also makes it easier for managers to understand employees' strengths and good points, allowing them to provide appropriate guidance and support. This means that the feedback system can provide regular feedback on employees' strengths and good points.

[0066] A feedback system according to an embodiment includes a collection unit, a reading unit, an analysis unit, an extraction unit, and a feedback unit. The collection unit collects one-on-one data. The one-on-one data includes, but is not limited to, conversation content, notes, work results, and assignments. For example, the collection unit collects conversation content and notes as text data. The collection unit can also collect data related to work results and assignments. For example, the collection unit collects reports submitted by employees and project progress. The collection unit can also collect audio data and image data. For example, the collection unit records one-on-one conversations and collects the audio data. The reading unit loads the data collected by the collection unit into a generation AI. For example, the reading unit inputs text data into the generation AI. The reading unit can also load audio data and image data into the generation AI. For example, the reading unit converts audio data into text data and inputs the text data into the generation AI. The analysis unit uses the generation AI to analyze the data loaded by the reading unit. The analysis unit analyzes data using, for example, natural language processing technology or machine learning algorithms. For example, the analysis unit analyzes text data using morphological analysis. The analysis unit can also analyze the structure of data using grammatical analysis. Furthermore, the analysis unit can analyze data using machine learning algorithms such as K-means and hierarchical clustering. The extraction unit extracts strengths and good points from the data analyzed by the analysis unit. For example, if an employee has achieved excellent results in a specific project, the extraction unit extracts those results as strengths. The extraction unit can also extract excellent teamwork as good points. For example, the extraction unit extracts strengths based on the project success rate and goals achieved. The feedback unit provides regular feedback based on the information extracted by the extraction unit. For example, the feedback unit provides feedback to each employee once a month. The feedback unit can also include advice on areas for improvement and next steps in the content of the feedback. For example, the feedback unit provides specific advice on areas for improvement and next steps in addition to the employee's strengths and good points.As a result, the feedback system according to the embodiment can periodically provide feedback on employees' strengths and good points. Some or all of the above-described processing in the feedback unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the feedback unit can provide feedback based on information extracted by the generation AI. For example, the output unit displays the feedback results to employees or their superiors via a web application or mobile application. If feedback on paper is desired, the results are printed using a printer. Sending the results via email provides quick feedback by sending the results directly to employees or their superiors. Some or all of the above-described processing in the output unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0067] The collection unit can collect data related to conversation content or notes, work results, and issues. For example, the collection unit collects conversation content and notes as text data. For example, the collection unit records one-on-one conversation content and converts the audio data into text data. The collection unit can also collect data related to work results and issues. For example, the collection unit collects reports submitted by employees and project progress. The collection unit can also collect audio data and image data. For example, the collection unit records one-on-one conversations and collects the audio data. This allows the collection unit to collect a variety of data, enabling more detailed analysis. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input audio data into a generation AI and have the generation AI convert the audio data into text data.

[0068] The analysis unit can analyze data using natural language processing technology or a machine learning algorithm. The analysis unit analyzes data using, for example, natural language processing technology. For example, the analysis unit analyzes text data using morphological analysis. The analysis unit can also analyze the structure of data using grammatical analysis. Furthermore, the analysis unit can analyze data using a machine learning algorithm. For example, the analysis unit analyzes data using a machine learning algorithm such as K-means or hierarchical clustering. This allows the analysis unit to use advanced technology, thereby improving the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can provide analysis results based on data analyzed by a generation AI.

[0069] When a significant result is achieved in a specific project, the extraction unit can extract the result as a strength. For example, when a significant result is achieved in a specific project, the extraction unit extracts the result as a strength. For example, the extraction unit extracts strengths based on the project's success rate and goals achieved. The extraction unit can also evaluate the project's progress and deliverables and extract strengths based on the evaluation results. Furthermore, the extraction unit can extract strengths based on an evaluation of the project's leadership and teamwork. For example, the extraction unit evaluates the project's leadership skills and the quality of teamwork and extracts strengths based on the evaluation results. In this way, the extraction unit extracts specific results as strengths, thereby improving the quality of feedback. Some or all of the above-mentioned processing in the extraction unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the extraction unit can extract strengths based on information extracted by a generation AI.

[0070] If teamwork is good, the extraction unit can extract that point as a good point. For example, if teamwork is good, the extraction unit extracts that point as a good point. For example, the extraction unit extracts good points based on the team's level of cooperation and the quality of communication. The extraction unit can also evaluate the team's performance and results and extract good points based on the evaluation results. Furthermore, the extraction unit can evaluate the team's leadership and cooperation system and extract good points based on the evaluation results. For example, the extraction unit evaluates the team's leadership skills and the quality of cooperation system and extracts good points based on the evaluation results. In this way, the extraction unit extracts good teamwork, making the feedback more comprehensive. Some or all of the above-mentioned processing in the extraction unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the extraction unit can extract good points based on information extracted by a generation AI.

[0071] The feedback unit can provide feedback to each employee monthly. The feedback unit can provide feedback to each employee, for example, once a month. For example, the feedback unit can set a schedule for providing regular monthly feedback. The feedback unit can also prepare the content of feedback in advance and provide feedback to each employee at an appropriate time. Furthermore, the feedback unit can evaluate the effectiveness of the feedback and reflect it in the next feedback. For example, the feedback unit can evaluate the employee's reaction and the effectiveness of the feedback, and adjust the content of the next feedback based on the evaluation results. In this way, the feedback unit can provide regular feedback, thereby improving employee motivation. Some or all of the above-mentioned processing in the feedback unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the feedback unit can provide feedback based on information extracted by a generation AI.

[0072] The feedback unit may include advice regarding areas for improvement or next steps in the feedback content. For example, the feedback unit may include advice regarding areas for improvement or next steps in the feedback content. For example, the feedback unit may provide specific advice regarding areas for improvement or next steps in addition to the employee's strengths and good points. The feedback unit may also propose a specific action plan to improve the employee's work performance. For example, the feedback unit may propose training programs or learning resources to improve the employee's skills. Furthermore, the feedback unit may provide advice regarding the employee's career path. For example, the feedback unit may propose a job title or job content as the next step based on the employee's career goals. In this way, the feedback unit's provision of specific advice promotes the employee's growth. Some or all of the above-described processing in the feedback unit may be performed using, or without, a generation AI. For example, the feedback unit may provide advice based on information extracted by a generation AI.

[0073] The feedback section may describe, as an effect of the feedback, that the feedback is expected to improve employee motivation or work efficiency. For example, the feedback section may describe, as an effect of the feedback, that the feedback is expected to improve employee motivation or work efficiency. For example, the feedback section may describe specific effects for improving employee motivation. For example, the feedback section may evaluate the effect of the feedback based on an employee satisfaction survey or work productivity indicators. The feedback section may also describe specific effects for improving work efficiency. For example, the feedback section may describe specific examples of work efficiency improvement or process improvement. Furthermore, the feedback section may set indicators for quantitatively evaluating the effect of the feedback. For example, the feedback section may evaluate the effect of the feedback based on the employee's work performance or goal achievement rate. In this way, the feedback section's explicit statement of the effect emphasizes the importance of feedback. Some or all of the above-described processing in the feedback section may be performed using, or without, a generation AI. For example, the feedback section may describe the effect based on information extracted by a generation AI.

[0074] The feedback system includes a collection unit that estimates a user's emotions and adjusts the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit delays the timing of data collection to allow the user to relax and collect data. For example, the collection unit monitors the user's emotions in real time and postpones data collection if the user's stress level is high. The collection unit can also immediately collect data when the user is relaxed, thereby efficiently acquiring information. For example, the collection unit analyzes the user's emotional state and, if determined to be relaxed, quickly collects data. Furthermore, if the user is concentrating, the collection unit can adjust the timing of collection to take advantage of the user's concentration and collect detailed data. For example, the collection unit evaluates the user's level of concentration and, if determined to be concentrating, collects detailed data. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may adjust the collection timing based on the user's emotion data estimated by the generation AI.

[0075] The feedback system includes a collection unit that customizes the type of data to be collected according to the employee's job content or position. For example, for sales employees, the collection unit focuses on collecting data on conversations with customers and the progress of business negotiations. For example, the collection unit collects details of business negotiations conducted by sales employees and interactions with customers. For technical employees, the collection unit can also collect data on project progress and technical issues. For example, the collection unit collects progress reports and technical issues for projects handled by technical employees. For managerial employees, the collection unit can also collect team performance and feedback from subordinates. For example, the collection unit collects the content of team meetings conducted by managerial employees and feedback from subordinates. This allows the collection unit to customize the type of data, enabling more appropriate data collection. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can collect data based on data collection criteria customized by the generation AI.

[0076] The feedback system includes a collection unit that collects employee self-assessments and feedback from colleagues when collecting data. For example, when an employee conducts a self-assessment, the collection unit collects the content of the assessment as data. For example, the collection unit collects the content of the employee's self-assessment as digital data. The collection unit can also periodically collect feedback from colleagues to identify the employee's strengths and areas for improvement. For example, the collection unit collects feedback sheets and survey results filled out by colleagues. Furthermore, the collection unit can compare the employee's self-assessment with feedback from colleagues to collect comprehensive assessment data. For example, the collection unit integrates the employee's self-assessment and feedback from colleagues to create a comprehensive assessment report. By collecting self-assessments and feedback from colleagues, the collection unit can collect more comprehensive data. Some or all of the above-described processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can collect data based on the self-assessments and feedback from colleagues collected by the generation AI.

[0077] The feedback system includes a collection unit that estimates a user's emotions and prioritizes data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit prioritizes collecting data that causes stress. For example, the collection unit analyzes the user's emotional state and prioritizes collecting work data and issues that cause stress. Furthermore, if the user is relaxed, the collection unit can prioritize collecting regular work data. For example, the collection unit evaluates the user's emotional state and, if determined to be relaxed, quickly collects regular work data. Furthermore, if the user is concentrating, the collection unit can prioritize collecting detailed technical data and project progress data. For example, the collection unit evaluates the user's level of concentration and, if determined to be concentrating, collects detailed technical data and project progress data. This enables more appropriate data collection by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may determine the priority of data based on the user's emotion data estimated by the generation AI.

[0078] The feedback system includes a collection unit that prioritizes collecting highly relevant data by taking into account the employee's geographic location information when collecting data. For example, when an employee is on a business trip, the collection unit prioritizes collecting the work content and results of the business trip. For example, the collection unit collects details and results of the work performed by the employee while on the business trip. In addition, when an employee is working remotely, the collection unit can prioritize collecting work data in a remote environment. For example, the collection unit collects progress and issues of work performed by the employee while working remotely. Furthermore, when an employee works at the head office, the collection unit can prioritize collecting work data in the office. For example, the collection unit collects details and results of the work performed by the employee at the head office. This allows the collection unit to collect more relevant data by taking the geographic location information into account. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can collect data based on the geographic location information collected by the generation AI.

[0079] The feedback system includes a collection unit that analyzes employees' social media activities and collects related data during data collection. The collection unit, for example, collects work-related posts shared by employees on social media. For example, the collection unit collects work-related information and opinions posted by employees on social media. The collection unit can also collect work-related trends and opinions from employees' social media activities. For example, the collection unit analyzes topics and hashtags followed by employees on social media to collect related information. Furthermore, the collection unit can analyze employees' social media networks to collect work-related personal connection data. For example, the collection unit analyzes the professional networks to which employees are connected on social media and collects related personal connection data. This allows the collection unit to analyze social media activities and collect more comprehensive data. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can collect data based on social media data analyzed by a generation AI.

[0080] The feedback system includes a reading unit that estimates a user's emotions and adjusts the data reading order based on the estimated user emotions. For example, when the user is feeling stressed, the reading unit reads data in descending order of importance. For example, the reading unit monitors the user's emotional state in real time and prioritizes reading data with lower importance when the user's stress level is high. Furthermore, when the user is relaxed, the reading unit can also read data with higher importance. For example, the reading unit analyzes the user's emotional state and, if it is determined that the user is relaxed, quickly reads data with higher importance. Furthermore, when the user is concentrating, the reading unit can also prioritize reading detailed data. For example, the reading unit evaluates the user's level of concentration and, if it is determined that the user is concentrating, prioritizes reading detailed data. This allows for more appropriate data reading by adjusting the data reading order according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reading unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the reading unit may adjust the reading order based on the user's emotion data estimated by the generation AI.

[0081] The feedback system includes a reading unit that adjusts the level of detail of reading based on the importance of data during reading. For example, the reading unit reads data with high importance in detail and reads data with low importance in a simplified manner. For example, the reading unit evaluates the importance of the data and performs a detailed analysis on the data with high importance. The reading unit can also read additional metadata for the data with high importance. For example, the reading unit collects metadata related to the data with high importance and performs a detailed analysis. Furthermore, the reading unit can also read only an outline of the data with low importance. For example, the reading unit collects an outline of the data with low importance and performs a simplified analysis. This enables efficient data reading by adjusting the level of detail of reading based on the importance of the data. Some or all of the above-described processing in the reading unit may be performed using, or without, a generation AI. For example, the reading unit can adjust the level of detail of reading based on the importance of the data evaluated by the generation AI.

[0082] The feedback system includes a reading unit that applies different reading algorithms depending on the category of data during reading. The reading unit, for example, applies a natural language processing algorithm to text data. For example, the reading unit applies natural language processing algorithms such as morphological analysis and grammatical analysis to analyze the text data. The reading unit can also apply an image recognition algorithm to image data. For example, the reading unit reads the data using image recognition technology to analyze the image data. The reading unit can also apply a voice recognition algorithm to audio data. For example, the reading unit reads the data using voice recognition technology to analyze the audio data. This enables efficient data reading by applying an appropriate algorithm depending on the category of the data. Some or all of the above-described processing in the reading unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reading unit can apply an appropriate algorithm based on the category of the data analyzed by the generation AI.

[0083] The feedback system includes a reading unit that estimates a user's emotion and adjusts the length of the reading based on the estimated user emotion. For example, if the user is feeling stressed, the reading unit completes the reading in a short time. For example, the reading unit monitors the user's emotional state in real time and reads data in a short time if the stress level is high. Furthermore, if the user is relaxed, the reading unit can read detailed data over a long period of time. For example, the reading unit analyzes the user's emotional state and, if it is determined that the user is relaxed, reads detailed data over a long period of time. Furthermore, if the user is concentrating, the reading unit can read necessary data at an appropriate length of time. For example, the reading unit evaluates the user's level of concentration and, if it is determined that the user is concentrating, reads necessary data at an appropriate length of time. This allows for more appropriate data reading by adjusting the length of the reading according to the user's emotion. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reading unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the reading unit may adjust the length of reading based on the user's emotion data estimated by the generation AI.

[0084] The feedback system includes a reading unit that, at the time of reading, determines a reading priority based on the time of data submission. The reading unit, for example, prioritizes reading the most recent data. For example, the reading unit evaluates the time of data submission and prioritizes reading the most recent data. The reading unit can also postpone data that was submitted earlier. For example, the reading unit postpones data that was submitted earlier and prioritizes reading the most recent data. Furthermore, the reading unit can also prioritize reading important data based on the time of submission. For example, the reading unit uses the time of submission as a criterion for prioritizing reading important data. This enables efficient data reading by determining the priority based on the time of data submission. Some or all of the above-described processing in the reading unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reading unit can determine the reading priority based on the time of data submission evaluated by the generation AI.

[0085] The feedback system includes a reading unit that adjusts the reading order based on the relevance of data during reading. The reading unit, for example, prioritizes reading highly relevant data. For example, the reading unit evaluates the relevance of data and prioritizes reading highly relevant data. The reading unit can also postpone low-relevance data. For example, the reading unit postpones low-relevance data and prioritizes reading highly relevant data. Furthermore, the reading unit can also determine an efficient reading order based on the relevance of data. For example, the reading unit determines an efficient reading order based on the relevance of data. As a result, adjusting the reading order based on the relevance of data enables efficient data reading. Some or all of the above-described processing in the reading unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reading unit can adjust the reading order based on the relevance of data evaluated by the generation AI.

[0086] The feedback system includes an analysis unit that estimates a user's emotions and adjusts analysis standards based on the estimated user emotions. The analysis unit, for example, relaxes the analysis standards when the user is stressed. For example, the analysis unit monitors the user's emotional state in real time and relaxes the analysis standards when the stress level is high. The analysis unit can also tighten the analysis standards when the user is relaxed. For example, the analysis unit analyzes the user's emotional state and tightens the analysis standards when it determines that the user is relaxed. Furthermore, the analysis unit can perform a detailed analysis when the user is concentrating. For example, the analysis unit evaluates the user's concentration level and performs a detailed analysis when it determines that the user is concentrating. This enables more appropriate analysis by adjusting the analysis standards according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can adjust the analysis criteria based on the user's emotional data estimated by the generation AI.

[0087] The feedback system includes an analysis unit that improves the accuracy of the analysis by taking into account the interrelationships between data during analysis. The analysis unit, for example, analyzes the correlations between data to improve the accuracy. For example, the analysis unit performs a correlation analysis of data and evaluates the relationships between the data. The analysis unit can also perform analysis by taking into account the interdependence of data. For example, the analysis unit evaluates the dependency of data and performs analysis based on interrelated data. Furthermore, the analysis unit can obtain more accurate analysis results based on the interrelationships between data. For example, the analysis unit evaluates the interrelationships between data and performs analysis based on highly related data. In this way, the accuracy of the analysis is improved by taking the interrelationships between data into account. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can perform analysis based on the interrelationships between data evaluated by the generation AI.

[0088] The feedback system includes an analysis unit that performs analysis taking into account attribute information of the data submitter during analysis. The analysis unit performs analysis taking into account, for example, the submitter's job title and job content. For example, the analysis unit evaluates the submitter's job title and job content and analyzes the data based on the evaluation. The analysis unit can also perform analysis based on the submitter's past performance. For example, the analysis unit evaluates the submitter's past performance data and analyzes the data based on the evaluation. Furthermore, the analysis unit can also perform analysis taking into account the submitter's role within the team. For example, the analysis unit evaluates the submitter's role within the team and analyzes the data based on the evaluation. This enables more appropriate analysis by taking into account the attribute information of the data submitter. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can perform analysis based on the submitter's attribute information evaluated by the generation AI.

[0089] The feedback system includes an analysis unit that estimates a user's emotions and adjusts the display order of analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit displays results in descending order of importance. For example, the analysis unit monitors the user's emotional state in real time and prioritizes displaying results with lower importance when the user's stress level is high. The analysis unit can also display results with higher importance when the user is relaxed. For example, the analysis unit analyzes the user's emotional state and, if it determines that the user is relaxed, quickly displays results with higher importance. Furthermore, the analysis unit can prioritize displaying detailed results when the user is concentrating. For example, the analysis unit evaluates the user's level of concentration and, if it determines that the user is concentrating, prioritizes displaying detailed results. This allows for more appropriate information to be provided by adjusting the display order of analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit may adjust the display order of the analysis results based on the user's emotion data estimated by the generation AI.

[0090] The feedback system includes an analysis unit that performs analysis taking into account the geographical distribution of data during analysis. The analysis unit, for example, performs analysis for each region based on the geographical distribution of the data. For example, the analysis unit evaluates the geographical distribution of the data and analyzes trends for each region. The analysis unit can also analyze region-specific trends taking the geographical distribution into account. For example, the analysis unit evaluates data for each region and analyzes trends specific to each region. The analysis unit can also analyze performance for each region based on the geographical distribution. For example, the analysis unit evaluates data for each region and analyzes performance for each region. This makes it possible to analyze region-specific trends by taking the geographical distribution of the data into account. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can perform analysis based on geographical distribution data evaluated by the generation AI.

[0091] The feedback system includes an analysis unit that, during analysis, refers to related literature of the data to improve the accuracy of the analysis. The analysis unit, for example, refers to related literature to improve the accuracy of the analysis. For example, the analysis unit evaluates related literature and complements the analysis results. The analysis unit can also complement the analysis results based on data from the related literature. For example, the analysis unit evaluates data from the related literature and complements the analysis results based on the evaluation. Furthermore, the analysis unit can also adjust the analysis criteria based on the related literature. For example, the analysis unit evaluates data from the related literature and adjusts the analysis criteria based on the evaluation. This improves the accuracy of the analysis by referring to the related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can perform analysis based on related literature data evaluated by the generation AI.

[0092] The feedback system includes an extraction unit that estimates a user's emotions and prioritizes information to be extracted based on the estimated user emotions. For example, if the user is feeling stressed, the extraction unit prioritizes extracting positive information. For example, the extraction unit monitors the user's emotional state in real time and prioritizes extracting positive information when the user's stress level is high. The extraction unit can also prioritize extracting detailed information when the user is relaxed. For example, the extraction unit analyzes the user's emotional state and prioritizes extracting detailed information when the user is determined to be relaxed. Furthermore, the extraction unit can also prioritize extracting important information when the user is concentrating. For example, the extraction unit evaluates the user's level of concentration and prioritizes extracting important information when the user is determined to be concentrating. This enables more appropriate information extraction by prioritizing information according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the extraction unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the extraction unit may determine the priority of information based on the user's emotion data estimated by the generation AI.

[0093] The feedback system includes an extraction unit that improves the accuracy of extraction by taking into account the interrelationships between data during extraction. The extraction unit extracts highly accurate information based on, for example, correlations between data. For example, the extraction unit performs a correlation analysis of data and evaluates the relationships between data. The extraction unit can also extract important information by taking into account the interdependence of data. For example, the extraction unit evaluates the dependency of data and extracts information based on interrelated data. Furthermore, the extraction unit can extract highly related information based on the interrelationships between data. For example, the extraction unit evaluates the interrelationships between data and extracts information based on highly related information. This improves the accuracy of extraction by taking into account the interrelationships between data. Some or all of the above-described processing in the extraction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the extraction unit can extract information based on the interrelationships between data evaluated by the generation AI.

[0094] The feedback system includes an extraction unit that performs extraction while taking into account attribute information of the data submitter. The extraction unit extracts important information, for example, by taking into account the submitter's job title and job content. For example, the extraction unit evaluates the submitter's job title and job content and extracts information based on the evaluation. The extraction unit can also extract highly relevant information based on the submitter's past performance. For example, the extraction unit evaluates the submitter's past performance data and extracts information based on the evaluation. Furthermore, the extraction unit can extract important information by taking into account the submitter's role within the team. For example, the extraction unit evaluates the submitter's role within the team and extracts information based on the evaluation. This enables more appropriate information extraction by taking into account the attribute information of the data submitter. Some or all of the above-described processing in the extraction unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the extraction unit can extract information based on the submitter's attribute information evaluated by the generation AI.

[0095] The feedback system includes an extraction unit that estimates a user's emotion and adjusts the display method of extracted information based on the estimated user emotion. For example, if the user is feeling stressed, the extraction unit provides a simple, highly visible display method. For example, the extraction unit monitors the user's emotional state in real time and provides a simple, highly visible display method when the stress level is high. The extraction unit can also provide a display method including detailed information when the user is relaxed. For example, the extraction unit analyzes the user's emotional state and provides a display method including detailed information when the user is determined to be relaxed. Furthermore, the extraction unit can also provide a display method that highlights important information when the user is concentrating. For example, the extraction unit evaluates the user's level of concentration and provides a display method that highlights important information when the user is determined to be concentrating. This allows for more appropriate information to be provided by adjusting the information display method according to the user's emotion. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the extraction unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the extraction unit may adjust the display method of information based on the user's emotion data estimated by the generation AI.

[0096] The feedback system includes an extraction unit that performs extraction while taking into account the geographical distribution of data. The extraction unit, for example, extracts important information for each region based on the geographical distribution of the data. For example, the extraction unit evaluates the geographical distribution of the data and extracts trends for each region. The extraction unit can also extract region-specific trends by taking the geographical distribution into account. For example, the extraction unit evaluates data for each region and extracts trends specific to each region. The extraction unit can also extract performance for each region based on the geographical distribution. For example, the extraction unit evaluates data for each region and extracts performance for each region. This makes it possible to extract region-specific trends by taking the geographical distribution of the data into account. Some or all of the above-described processing in the extraction unit may be performed using, or without, a generation AI. For example, the extraction unit can extract information based on geographical distribution data evaluated by the generation AI.

[0097] The feedback system includes an extraction unit that, during extraction, refers to related literature of the data to improve the accuracy of the extraction. The extraction unit, for example, refers to related literature to extract highly accurate information. For example, the extraction unit evaluates related literature and complements the extraction results. The extraction unit can also complement the extraction results based on data from related literature. For example, the extraction unit evaluates data from related literature and complements the extraction results based on the evaluation. Furthermore, the extraction unit can also adjust the extraction criteria based on the related literature. For example, the extraction unit evaluates data from related literature and adjusts the extraction criteria based on the evaluation. This improves the accuracy of the extraction by referring to related literature. Some or all of the above-mentioned processing in the extraction unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the extraction unit can extract information based on related literature data evaluated by the generation AI.

[0098] The feedback system includes a feedback unit that estimates a user's emotions and adjusts the content of the feedback based on the estimated user emotions. For example, the feedback unit prioritizes providing positive feedback when the user is stressed. For example, the feedback unit monitors the user's emotional state in real time and prioritizes providing positive feedback when the stress level is high. The feedback unit can also provide detailed feedback when the user is relaxed. For example, the feedback unit analyzes the user's emotional state and provides detailed feedback when the user is determined to be relaxed. Furthermore, the feedback unit can also provide feedback including specific improvements when the user is focused. For example, the feedback unit evaluates the user's level of concentration and provides feedback including specific improvements when the user is determined to be focused. This allows for more appropriate feedback by adjusting the content of the feedback according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback unit can be performed using, for example, the generation AI, or without the generation AI. For example, the feedback unit can adjust the content of the feedback based on the user's emotional data estimated by the generation AI.

[0099] The feedback system includes a feedback unit that, when providing feedback, provides optimal feedback by referring to past feedback history. The feedback unit, for example, provides continuous improvement points based on the past feedback history. For example, the feedback unit evaluates the past feedback history and provides continuous improvement points. The feedback unit can also highlight areas of growth from the past feedback history. For example, the feedback unit evaluates the past feedback history and highlights areas of growth. Furthermore, the feedback unit can also provide consistent feedback by referring to the past feedback history. For example, the feedback unit evaluates the past feedback history and provides consistent feedback. This enables more consistent feedback by referring to the past feedback history. Some or all of the above-described processing in the feedback unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the feedback unit can provide feedback based on the past feedback history evaluated by the generation AI.

[0100] The feedback system includes a feedback unit that customizes the feedback method according to the employee's job title and job content. For example, the feedback unit provides a sales employee with feedback regarding customer service and sales negotiation procedures. For example, the feedback unit provides the sales employee with feedback regarding details of sales negotiations and customer service procedures. The feedback unit can also provide technical employees with feedback regarding technical skills and project progress. For example, the feedback unit provides feedback regarding the progress of projects and technical issues handled by technical employees. The feedback unit can also provide managerial employees with feedback regarding team management and leadership. For example, the feedback unit provides feedback regarding the content of team meetings held by managerial employees and their leadership skills. This enables more appropriate feedback by providing feedback according to the employee's job title and job content. Some or all of the above-described processing in the feedback unit may be performed using, or without, a generation AI. For example, the feedback unit can provide feedback based on the employee's job title and job content evaluated by the generation AI.

[0101] The feedback system includes a feedback unit that estimates a user's emotions and determines the priority of feedback based on the estimated user emotions. For example, the feedback unit prioritizes providing positive feedback when the user is stressed. For example, the feedback unit monitors the user's emotional state in real time and prioritizes providing positive feedback when the stress level is high. The feedback unit can also prioritize providing detailed feedback when the user is relaxed. For example, the feedback unit analyzes the user's emotional state and prioritizes providing detailed feedback when the user is determined to be relaxed. Furthermore, the feedback unit can also prioritize providing feedback including specific improvement suggestions when the user is focused. For example, the feedback unit evaluates the user's level of concentration and prioritizes providing feedback including specific improvement suggestions when the user is determined to be focused. This enables more appropriate feedback by prioritizing feedback according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the feedback unit may determine the priority of feedback based on the user's emotion data estimated by the generation AI.

[0102] The feedback system includes a feedback unit that provides optimal feedback by taking into account the geographic location information of an employee. For example, the feedback unit provides feedback regarding work performed at the business trip destination to an employee who is on a business trip. For example, the feedback unit provides feedback regarding details and results of work performed by the employee who is on a business trip. The feedback unit can also provide feedback regarding work performed in a remote environment to an employee who is working remotely. For example, the feedback unit provides feedback regarding the progress and challenges of work performed by the employee who is working remotely. Furthermore, the feedback unit can also provide feedback regarding work performed in the office to an employee working at the head office. For example, the feedback unit provides feedback regarding details and results of work performed by the employee working at the head office. This enables more appropriate feedback by taking into account the geographic location information of the employee. Some or all of the above-described processing in the feedback unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the feedback unit can provide feedback based on geographic location information evaluated by the generation AI.

[0103] The feedback system includes a feedback unit that analyzes an employee's social media activity and adjusts the content of the feedback when providing feedback. The feedback unit provides feedback based on, for example, work-related posts shared by the employee on social media. For example, the feedback unit provides feedback based on work-related information and opinions posted by the employee on social media. The feedback unit can also incorporate work-related trends and opinions from the employee's social media activity into the feedback. For example, the feedback unit analyzes topics and hashtags followed by the employee on social media and incorporates related information into the feedback. The feedback unit can also analyze the employee's social media network and incorporate work-related personal connection data into the feedback. For example, the feedback unit analyzes the employee's professional network of connections on social media and incorporates related personal connection data into the feedback. This enables more appropriate feedback by analyzing the employee's social media activity. Some or all of the above-described processing in the feedback unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the feedback unit can adjust the content of the feedback based on social media data analyzed by the generation AI. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, reading unit, analysis unit, extraction unit, and feedback unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects one-on-one conversation content and notes using the camera 42 and microphone 38B of the smart device 14. The reading unit loads the collected data into the generation AI, which is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the data using the generation AI and is realized by the specific processing unit 290. The extraction unit extracts strengths and good points from the analyzed data and is realized by the specific processing unit 290. The feedback unit provides feedback based on the extracted information and is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, reading unit, analysis unit, extraction unit, and feedback unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects one-on-one conversation content and notes using the camera 42 and microphone 238 of the smart glasses 214. The reading unit loads the collected data into the generation AI, which is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The analysis unit analyzes data using the generation AI and is realized by the specific processing unit 290. The extraction unit extracts strengths and good points from the analyzed data and is realized by the specific processing unit 290. The feedback unit provides feedback based on the extracted information and is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, reading unit, analysis unit, extraction unit, and feedback unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects one-on-one conversation content and notes using the camera 42 and microphone 238 of the headset-type terminal 314. The reading unit loads the collected data into the generation AI, which is realized by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12. The analysis unit analyzes data using the generation AI and is realized by the specific processing unit 290. The extraction unit extracts strengths and good points from the analyzed data and is realized by the specific processing unit 290. The feedback unit provides feedback based on the extracted information and is realized by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, reading unit, analysis unit, extraction unit, and feedback unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects one-on-one conversation content and notes using the camera 42 and microphone 238 of the robot 414. The reading unit loads the collected data into the generation AI, which is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the data using the generation AI and is realized by the specific processing unit 290. The extraction unit extracts strengths and good points from the analyzed data and is realized by the specific processing unit 290. The feedback unit provides feedback based on the extracted information and is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The feedback system may also include a health monitoring unit that monitors an employee's health status and adjusts the content of the feedback based on the employee's health status. For example, the health monitoring unit may monitor an employee's heart rate and sleep patterns, and if the employee's health status is poor, provide feedback to reduce stress. The health monitoring unit may also provide feedback regarding more challenging tasks if the employee's health status is good. Furthermore, the health monitoring unit may adjust the frequency of feedback based on the employee's health status. For example, if the employee's health status is deteriorating, the frequency of feedback may be reduced, and if the employee's health status is improving, the frequency of feedback may be increased. This allows appropriate feedback to be provided according to the employee's health status.

[0106] The feedback system may also include a career goal setting unit that customizes the content of feedback taking into account the employee's career goals. For example, the career goal setting unit may collect the short-term and long-term career goals set by the employee and provide feedback based on them. The career goal setting unit may also provide specific advice to strengthen the skills and experience related to the employee's career goals. Furthermore, the career goal setting unit may suggest appropriate training programs and learning resources based on the employee's career goals. This allows the system to provide feedback tailored to the employee's career goals and support their career growth.

[0107] The feedback system may also be equipped with a real-time performance monitoring unit that monitors employee performance data in real time and provides feedback according to fluctuations in performance. For example, the real-time performance monitoring unit tracks an employee's work progress and results in real time, and if performance declines, it immediately provides feedback on areas for improvement. It can also provide feedback that highlights improvements in performance if performance improves. Furthermore, the real-time performance monitoring unit can adjust the timing of feedback based on performance data. This allows timely feedback to be provided according to employee performance.

[0108] The feedback system may also include an emotional tone adjustment unit that estimates an employee's emotions and adjusts the tone of the feedback based on the estimated emotions. For example, the emotional tone adjustment unit may provide feedback in a gentler tone when the employee is stressed. Alternatively, the emotional tone adjustment unit may provide feedback in a more direct tone when the employee is relaxed. Furthermore, the emotional tone adjustment unit may provide feedback including specific suggestions for improvement when the employee is focused. In this way, feedback is provided in an appropriate tone according to the employee's emotions.

[0109] The feedback system may also include a social media analysis unit that analyzes employees' social media activities and reflects work-related trends and opinions in the feedback. For example, the social media analysis unit may analyze the topics and hashtags that employees follow on social media and reflect the related information in the feedback. The social media analysis unit may also analyze employees' social media networks and reflect work-related personal connection data in the feedback. Furthermore, the social media analysis unit may provide feedback based on work-related posts that employees have shared on social media. In this way, more appropriate feedback can be provided by analyzing employees' social media activities.

[0110] The feedback system may also include an emotion frequency adjustment unit that estimates the emotion of an employee and adjusts the frequency of feedback based on the estimated emotion. For example, the emotion frequency adjustment unit reduces the frequency of feedback when the employee is feeling stressed and increases the frequency of feedback when the employee is relaxed. The emotion frequency adjustment unit may also appropriately adjust the frequency of feedback when the employee is concentrating. This allows feedback to be provided at an appropriate frequency according to the employee's emotion.

[0111] The feedback system may also include a work environment monitoring unit that monitors an employee's work environment and adjusts the content of the feedback based on the work environment. For example, if an employee is working remotely, the work environment monitoring unit may provide feedback regarding work in the remote environment. If an employee is working in the office, the work environment monitoring unit may also provide feedback regarding work in the office environment. Furthermore, if an employee is on a business trip, the work environment monitoring unit may also provide feedback regarding work performed at the business trip destination. In this way, appropriate feedback is provided according to the employee's work environment.

[0112] The feedback system may also include an emotion customization unit that estimates an employee's emotion and customizes the content of the feedback based on the estimated emotion. For example, the emotion customization unit may provide positive feedback preferentially when the employee is feeling stressed. Alternatively, the emotion customization unit may provide detailed feedback when the employee is relaxed. Furthermore, the emotion customization unit may provide feedback including specific areas for improvement when the employee is concentrating. In this way, appropriate feedback is provided according to the employee's emotion.

[0113] The feedback system may also include a performance evaluation unit that evaluates an employee's work performance and adjusts the content of the feedback based on the evaluation results. For example, the performance evaluation unit evaluates an employee's work progress and results, and if performance is high, provides feedback that highlights those results. If performance is low, it may also provide feedback that includes specific areas for improvement. Furthermore, the performance evaluation unit may adjust the timing of feedback based on the evaluation results. This allows appropriate feedback to be provided according to the employee's work performance.

[0114] The feedback system may also include an emotion format adjustment unit that estimates an employee's emotion and adjusts the form of feedback based on the estimated emotion. For example, if the employee is feeling stressed, the emotion format adjustment unit may provide feedback in a simple, highly visible format. If the employee is relaxed, the emotion format adjustment unit may provide feedback in a format that includes detailed information. Furthermore, if the employee is concentrating, the emotion format adjustment unit may provide feedback in a format that emphasizes important information. In this way, feedback is provided in an appropriate format according to the employee's emotion.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The collection unit collects one-on-one data. One-on-one data includes conversation content, notes, work results, and issues. The collection unit collects conversation content and notes as text data, and also collects data related to work results and issues. In addition, audio data and image data can also be collected. For example, one-on-one conversations can be recorded and the audio data collected. Step 2: The reading unit reads the data collected by the collection unit into the generation AI. The reading unit inputs text data into the generation AI, and can also read audio data and image data into the generation AI. For example, the reading unit converts audio data into text data and inputs that text data into the generation AI. Step 3: The analysis unit uses the generative AI to analyze the data read by the reading unit. The analysis unit analyzes the data using natural language processing techniques and machine learning algorithms. For example, the analysis unit analyzes the data using morphological analysis, grammatical analysis, and machine learning algorithms such as K-means and hierarchical clustering. Step 4: The extraction unit extracts strengths and positive aspects from the data analyzed by the analysis unit. For example, if a company has achieved excellent results in a particular project, it will extract that result as a strength, and if the company has excellent teamwork, it will extract that as a positive aspect. Strengths are extracted based on the project's success rate and the goals achieved. Step 5: The feedback unit provides regular feedback based on the information extracted by the extraction unit. For example, feedback can be provided to each employee once a month, and the feedback can include advice on areas for improvement and next steps. The feedback unit can provide feedback based on the information extracted by the generation AI.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0119] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] 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.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] 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.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0161] 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.

[0162] 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.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0172] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0173] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0174] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0177] 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.

[0178] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0179] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0180] 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.

[0181] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0182] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0183] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0185] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0186] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0187] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0188] [Explanation of symbols]

[0189] 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 department that collects 1-on-1 data, a reading unit that reads the data collected by the collecting unit into a generating AI; an analysis unit that analyzes the data read by the reading unit; an extraction unit that extracts strengths and advantages from the data analyzed by the analysis unit; a feedback unit that periodically performs feedback based on the information extracted by the extraction unit. A system characterized by:

2. The collecting unit Collect data on conversations or notes, work outcomes, and challenges 2. The system of claim 1.

3. The analysis unit Analyze the data using natural language processing techniques or machine learning algorithms 2. The system of claim 1.

4. The extraction unit If you have achieved significant results in a specific project, extract those results as strengths.

2. The system of claim 1.

5. The extraction unit If teamwork is good, highlight that as a positive point.

2. The system of claim 1.

6. The feedback unit Provide monthly feedback to each employee 2. The system of claim 1.

7. The feedback unit Include suggestions for improvement or next steps in your feedback 2. The system of claim 1.

8. The feedback unit Describe how the feedback is expected to improve employee motivation or work efficiency.

2. The system of claim 1.

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