system

Abstract

The system according to the embodiment aims to provide quick and effective feedback to new employees' daily reports. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs daily reports of new employees. The analysis unit analyzes the daily reports input by the reception unit. The generation unit generates feedback based on the daily reports analyzed by the analysis unit. The provision unit provides the feedback generated by the generation 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 techniques do not provide quick and effective feedback on new employees' daily reports, and there is room for improvement.

[0005] The system according to the embodiment aims to provide quick and effective feedback to new employees' daily reports. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs the daily reports of new employees. The analysis unit analyzes the daily reports input by the reception unit. The generation unit generates feedback based on the daily reports analyzed by the analysis unit. The provision unit provides the feedback generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide quick and effective feedback to new employees' daily reports. [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 system according to an embodiment of the present invention uses a generation AI to provide feedback on new employees' daily reports. In this system, new employees input their daily reports, and the generation AI learns from past daily report data and elders' comments and generates appropriate feedback for the input daily reports. This accelerates the growth of new employees, provides psychological support, and reduces elders' workload. For example, a new employee inputs a daily report. The report details the job description, what they learned, and any difficulties they encountered. The generation AI learns from past daily report data and elders' comments and generates appropriate feedback for the new employee's daily report. For example, the system provides feedback such as, "For an overview of the new project, you may want to refer to the following materials. Also, for points that are difficult to understand, you may want to ask your elder specific questions." This accelerates the growth of new employees, provides psychological support, and reduces elders' workload.

[0029] A feedback system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs a new employee's daily report. The new employee's daily report may include, but is not limited to, details of work, progress, and issues. The reception unit may receive the daily report via, for example, text input, voice input, or image input. The analysis unit uses a generation AI to analyze the daily report input by the reception unit. The analysis may be performed via, for example, text analysis, sentiment analysis, or keyword extraction, but is not limited to, examples. The generation unit uses the generation AI to generate feedback based on the daily report analyzed by the analysis unit. The feedback may include, but is not limited to, advice, evaluations, and areas for improvement. The provision unit provides the generated feedback to the new employee. The provision unit may provide the feedback in the form of, for example, text, voice, or visual feedback. As a result, the feedback system according to an embodiment provides appropriate feedback to new employees' daily reports, accelerating their growth, providing psychological support, and reducing the workload of elders.

[0030] The reception unit can analyze the new employee's past daily report submission history and select the optimal input method. For example, if the new employee has preferred text input in the past, the reception unit can preferentially suggest text input. Also, if the new employee has used voice input in the past, the reception unit can preferentially suggest voice input. Also, if the new employee has used images frequently in the past, the reception unit can preferentially suggest image input. In this way, the optimal input method can be suggested based on the new employee's past history.

[0031] When entering the daily report, the reception unit can filter the data based on the new employee's current project or areas of interest. For example, the reception unit can preferentially display items related to the project the new employee is currently working on. The reception unit can also suggest topics related to the new employee's areas of interest as input items for the daily report. The reception unit can also display information related to areas in which the new employee has shown interest in the past as input items for the daily report. This makes it possible to suggest appropriate input items according to the new employee's current situation.

[0032] When entering a daily report, the reception unit can select an appropriate input means depending on the input method of the new employee. For example, if the new employee selects voice input, the reception unit inputs the daily report using voice recognition technology. Furthermore, if the new employee selects text input, the reception unit can also support keyboard input. Furthermore, if the new employee selects image input, the reception unit can also input the daily report using image recognition technology. This allows the daily report to be entered using the optimal means depending on the input method of the new employee.

[0033] When inputting the daily report, the reception unit can prioritize inputting highly relevant content in consideration of the geographical location information of the new employee. For example, if the new employee is in the office, the reception unit can prioritize inputting office-related items. Furthermore, if the new employee is on a business trip, the reception unit can also prioritize inputting items related to the business trip destination. Furthermore, if the new employee is working remotely, the reception unit can also prioritize inputting items related to remote work. This allows appropriate content to be input based on the geographical location information of the new employee.

[0034] The reception department can analyze the social media activity of new employees and enter relevant content when entering daily reports. For example, the reception department can automatically reflect the work content that the new employee has shared on social media in the daily report. The reception department can also suggest related topics in the daily report based on the new employee's social media activity. The reception department can also reflect the new employee's feedback on social media in the daily report. This makes it possible to enter appropriate content based on the new employee's social media activity.

[0035] The reception unit can customize the input method by reflecting the new employee's past feedback when inputting the daily report. For example, the reception unit customizes the input items based on the new employee's past feedback. The reception unit can also optimize the input method based on the new employee's past feedback. The reception unit can also simplify the input procedure by referring to the new employee's past feedback. This makes it possible to optimize the input method based on the new employee's past feedback.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the daily report. For example, the analysis unit performs a detailed analysis on a daily report with a high level of importance. The analysis unit can also perform a simplified analysis on a daily report with a low level of importance. The analysis unit can also adjust the depth of the analysis according to the importance. This makes it possible to adjust the level of detail of the analysis according to the importance of the daily report.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the daily report. For example, the analysis unit applies a technical analysis algorithm to a technical daily report. The analysis unit can also apply a marketing analysis algorithm to a marketing-related daily report. The analysis unit can also apply a personnel analysis algorithm to a personnel-related daily report. This allows for appropriate analysis depending on the category of the daily report.

[0038] During the analysis, the analysis unit can improve the accuracy of the analysis by referring to the past analysis results of the new employee. For example, the analysis unit adjusts the analysis algorithm based on the past analysis results of the new employee. The analysis unit can also improve the accuracy of the analysis from the past analysis results of the new employee. The analysis unit can also optimize the analysis method by referring to the past analysis results of the new employee. This makes it possible to improve the accuracy of the analysis based on the past analysis results of the new employee.

[0039] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the daily report. For example, the analysis unit prioritizes the analysis of the most recently submitted daily report. The analysis unit can also postpone the analysis of older submitted daily reports. The analysis unit can also adjust the priority of analysis based on the submission time. This makes it possible to adjust the priority of analysis based on the submission time of the daily report.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the daily reports. For example, the analysis unit prioritizes the analysis of highly relevant daily reports. The analysis unit can also postpone the analysis of less relevant daily reports. The analysis unit can also adjust the order of analysis based on the relevance. This makes it possible to adjust the order of analysis based on the relevance of the daily reports.

[0041] During the analysis, the analysis unit can adjust the use of technical terms in the analysis according to the expertise level of the new employee. For example, if the expertise level of the new employee is low, the analysis unit can provide the analysis results in simple language. Also, if the expertise level of the new employee is high, the analysis unit can provide the analysis results using a lot of technical terms. Also, the analysis unit can adjust the way the analysis results are expressed according to the expertise level of the new employee. This makes it possible to provide appropriate analysis results according to the expertise level of the new employee.

[0042] When generating feedback, the generation unit can adjust the level of detail of the feedback based on the importance of the daily report. For example, the generation unit provides detailed feedback for a daily report with high importance. The generation unit can also provide simplified feedback for a daily report with low importance. The generation unit can also adjust the level of detail of the feedback depending on the importance. This makes it possible to provide appropriate feedback depending on the importance of the daily report.

[0043] When generating feedback, the generation unit can apply different feedback algorithms depending on the category of the daily report. For example, the generation unit can apply a technical feedback algorithm to a technical daily report. The generation unit can also apply a marketing feedback algorithm to a marketing-related daily report. The generation unit can also apply a human resources feedback algorithm to a human resources-related daily report. This makes it possible to provide appropriate feedback depending on the category of the daily report.

[0044] When generating feedback, the generation unit can improve the accuracy of the feedback by referring to the past feedback results of the new employee. For example, the generation unit adjusts the feedback algorithm based on the past feedback results of the new employee. The generation unit can also improve the accuracy of the feedback from the past feedback results of the new employee. The generation unit can also optimize the feedback method by referring to the past feedback results of the new employee. This makes it possible to improve the accuracy of the feedback based on the past feedback results of the new employee.

[0045] When generating feedback, the generation unit can determine the priority of the feedback based on the submission time of the daily report. For example, the generation unit can provide feedback preferentially to a recently submitted daily report. The generation unit can also provide feedback later to a daily report that was submitted earlier. The generation unit can also adjust the priority of the feedback depending on the submission time. This makes it possible to adjust the priority of the feedback depending on the submission time of the daily report.

[0046] The generation unit can adjust the order of feedback based on the relevance of the daily report when generating the feedback. For example, the generation unit provides feedback preferentially to a highly relevant daily report. The generation unit can also provide feedback later to a less relevant daily report. The generation unit can also adjust the order of feedback based on the relevance. This makes it possible to adjust the order of feedback based on the relevance of the daily report.

[0047] When generating the feedback, the generation unit may adjust the use of technical terms in the feedback depending on the expertise level of the new employee. For example, if the expertise level of the new employee is low, the generation unit may provide the feedback in simple language. Alternatively, if the expertise level of the new employee is high, the generation unit may provide the feedback using a lot of technical terms. Alternatively, the generation unit may adjust the way the feedback is expressed depending on the expertise level of the new employee. This allows the new employee to provide appropriate feedback depending on the expertise level of the new employee.

[0048] When providing feedback, the providing unit can select the optimal feedback method by referring to the new employee's past feedback history. For example, the providing unit can preferentially provide feedback formats that the new employee has preferred in the past. The providing unit can also select the optimal feedback method from the new employee's past feedback history. The providing unit can also optimize the feedback method by referring to feedback that the new employee has received in the past. This makes it possible to select the optimal feedback method based on the new employee's past feedback history.

[0049] When providing feedback, the providing unit can customize the content of the feedback to be provided in accordance with the new employee's current task. For example, the providing unit provides feedback related to the task the new employee is currently working on. The providing unit can also customize the content of the feedback in accordance with the new employee's current task. The providing unit can also provide feedback on problems the new employee is facing in the current task. This makes it possible to provide appropriate feedback in accordance with the new employee's current task.

[0050] When providing feedback, the providing unit can analyze the new employee's reaction to the feedback and improve the method of providing the feedback. For example, if the new employee has a positive reaction to the feedback, the providing unit continues the method of providing the feedback. In addition, if the new employee has a negative reaction to the feedback, the providing unit can also change the method of providing the feedback. In addition, the providing unit can analyze the new employee's reaction to the feedback and optimize the method of providing the feedback. In this way, the method of providing the feedback can be optimized based on the new employee's reaction to the feedback.

[0051] When providing feedback, the providing unit can select the optimal providing method in consideration of the device information of the new employee. For example, if the new employee uses a smartphone, the providing unit can provide feedback optimized for the smartphone. Furthermore, if the new employee uses a tablet, the providing unit can also provide feedback optimized for the tablet. Furthermore, if the new employee uses a desktop, the providing unit can also provide feedback optimized for the desktop. In this way, optimal feedback can be provided based on the device information of the new employee.

[0052] When providing feedback, the providing unit can make the provided content multilingual in accordance with the language setting of the new employee. For example, the providing unit automatically sets the language of the feedback based on the language setting of the new employee's device. The providing unit can also provide a language switching function when the new employee speaks multiple languages. Furthermore, if the new employee selects a specific language, the providing unit can provide feedback in that language. This makes it possible to provide feedback in an appropriate language based on the new employee's language setting.

[0053] When providing feedback, the providing unit can analyze the new employee's reaction to the feedback in real time and immediately adjust the method of providing the feedback. For example, if the new employee has a positive reaction to the feedback, the providing unit continues the method of providing the feedback. In addition, if the new employee has a negative reaction to the feedback, the providing unit can also change the method of providing the feedback. In addition, the providing unit can analyze the new employee's reaction to the feedback in real time and optimize the method of providing the feedback. In this way, the method of providing the feedback can be optimized in real time based on the new employee's reaction to the feedback.

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

[0055] When new employees enter their daily reports, the reception unit can automatically complete the input content based on their past feedback history. For example, if a new employee has received feedback on a specific project in the past, it will automatically complete keywords and phrases related to that project. The reception unit can also learn expressions and phrases that new employees have frequently used in the past and use them to complete the input content. Furthermore, the reception unit can also refer to the content of daily reports that new employees have submitted in the past and automatically suggest similar content. This makes the entry of daily reports for new employees more efficient and reduces the effort required for input.

[0056] When new employees enter their daily reports, the reception department can check the consistency of the information based on past data. For example, the reception department can compare the current information entered with the work content and progress status previously recorded by the new employee to check for consistency. The reception department can also check whether the problems and issues previously recorded by the new employee have been resolved. Furthermore, the reception department can determine whether the current information entered is appropriate based on feedback the new employee has received in the past. This can improve the quality of new employees' daily reports and increase the accuracy of feedback.

[0057] When a new employee enters their daily report, the reception department can suggest what to enter by referencing the daily report data of other new employees. For example, the reception department can suggest related input items based on the daily report content of other new employees working on the same project. The reception department can also supplement the input content based on the problems and solutions faced by other new employees. Furthermore, the reception department can optimize the input content by referring to feedback received from other new employees. This promotes information sharing among new employees and improves the quality of daily reports.

[0058] The reception department can provide real-time feedback on the content entered by new employees as they enter their daily reports. For example, it can provide advice and suggestions for improvement on the content entered by the new employee on the spot. The reception department can also provide related materials and information based on the content entered by the new employee. Furthermore, the reception department can display feedback from other new employees and elders in real time on the content entered by the new employee. This makes daily report entry by new employees more effective and improves the quality of feedback.

[0059] When analyzing the contents of a new employee's daily report, the analysis unit can improve the accuracy of the analysis by referring to the daily report data of other new employees. For example, common problems and issues can be extracted based on the daily report contents of other new employees working on the same project. The analysis unit can also optimize the analysis results by referring to feedback received by other new employees. Furthermore, the analysis unit can adjust the analysis algorithm based on the daily report data of other new employees. This improves the accuracy of the analysis and enables the provision of more appropriate feedback.

[0060] When analyzing the contents of new employee daily reports, the analysis department can improve the accuracy of the analysis by referencing external data sources. For example, the analysis department can analyze the contents of daily reports based on industry trends and benchmark data. The analysis department can also optimize the analysis results by referring to the opinions and advice of external experts. Furthermore, the analysis department can also refer to external databases to provide information related to the contents of daily reports. This improves the accuracy of the analysis and enables more appropriate feedback to be provided.

[0061] When analyzing the contents of new employee daily reports, the analysis unit can automatically adjust the analysis algorithm based on past analysis results. For example, it can compare past analysis results with current analysis results to improve the accuracy of the algorithm. The analysis unit can also learn from past analysis results and optimize the analysis method. Furthermore, the analysis unit can determine analysis priorities based on past analysis results. This improves analysis accuracy and enables more appropriate feedback to be provided.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The reception department inputs the new employee's daily report. The new employee's daily report includes the job content, progress, problems, etc. The reception department can accept the daily report by text input, voice input, image input, etc. Step 2: The analysis unit uses the generation AI to analyze the daily report entered by the reception unit. The analysis is performed using methods such as text analysis, sentiment analysis, and keyword extraction. Step 3: The generator uses the generation AI to generate feedback based on the daily report analyzed by the analyzer. The feedback includes advice, evaluations, and areas for improvement. Step 4: The delivery unit provides the generated feedback to the new employee. The delivery unit can provide the feedback in the form of text, audio, visual, etc.

[0064] (Example 2) A system according to an embodiment of the present invention uses a generation AI to provide feedback on new employees' daily reports. In this system, new employees input their daily reports, and the generation AI learns from past daily report data and elders' comments and generates appropriate feedback for the input daily reports. This accelerates the growth of new employees, provides psychological support, and reduces elders' workload. For example, a new employee inputs a daily report. The report details the job description, what they learned, and any difficulties they encountered. The generation AI learns from past daily report data and elders' comments and generates appropriate feedback for the new employee's daily report. For example, the system provides feedback such as, "For an overview of the new project, you may want to refer to the following materials. Also, for points that are difficult to understand, you may want to ask your elder specific questions." This accelerates the growth of new employees, provides psychological support, and reduces elders' workload.

[0065] A feedback system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs a new employee's daily report. The new employee's daily report may include, but is not limited to, details of work, progress, and issues. The reception unit may receive the daily report via, for example, text input, voice input, or image input. The analysis unit uses a generation AI to analyze the daily report input by the reception unit. The analysis may be performed via, for example, text analysis, sentiment analysis, or keyword extraction, but is not limited to, examples. The generation unit uses the generation AI to generate feedback based on the daily report analyzed by the analysis unit. The feedback may include, but is not limited to, advice, evaluations, and areas for improvement. The provision unit provides the generated feedback to the new employee. The provision unit may provide the feedback in the form of, for example, text, voice, or visual feedback. As a result, the feedback system according to an embodiment provides appropriate feedback to new employees' daily reports, accelerating their growth, providing psychological support, and reducing the workload of elders.

[0066] The reception unit can estimate the emotions of new employees and adjust the timing of daily report input based on the estimated emotions. For example, if the new employee is feeling stressed, the reception unit can prompt the new employee to input the daily report at a time when they can relax. Furthermore, if the new employee is concentrating, the reception unit can also prompt the new employee to input the daily report at that time. Furthermore, if the new employee is tired, the reception unit can also prompt the new employee to input the daily report after a break. In this way, it is possible to prompt the new employee to input the daily report at an appropriate time depending on the new employee's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. Generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0067] The reception unit can analyze the new employee's past daily report submission history and select the optimal input method. For example, if the new employee has preferred text input in the past, the reception unit can preferentially suggest text input. Also, if the new employee has used voice input in the past, the reception unit can preferentially suggest voice input. Also, if the new employee has used images frequently in the past, the reception unit can preferentially suggest image input. In this way, the optimal input method can be suggested based on the new employee's past history.

[0068] When entering the daily report, the reception unit can filter the data based on the new employee's current project or areas of interest. For example, the reception unit can preferentially display items related to the project the new employee is currently working on. The reception unit can also suggest topics related to the new employee's areas of interest as input items for the daily report. The reception unit can also display information related to areas in which the new employee has shown interest in the past as input items for the daily report. This makes it possible to suggest appropriate input items according to the new employee's current situation.

[0069] When entering a daily report, the reception unit can select an appropriate input means depending on the input method of the new employee. For example, if the new employee selects voice input, the reception unit inputs the daily report using voice recognition technology. Furthermore, if the new employee selects text input, the reception unit can also support keyboard input. Furthermore, if the new employee selects image input, the reception unit can also input the daily report using image recognition technology. This allows the daily report to be entered using the optimal means depending on the input method of the new employee.

[0070] The reception unit can estimate the emotions of the new employee and determine the priority of the daily report to be entered based on the estimated emotions. For example, if the new employee is feeling stressed, the reception unit postpones items of low importance. Furthermore, if the new employee is relaxed, the reception unit can also prioritize input of items of high importance. Furthermore, if the new employee is concentrating, the reception unit can also prioritize input of complex items. This makes it possible to adjust the priority of the daily report according to the emotions of the new employee. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0071] When inputting the daily report, the reception unit can prioritize inputting highly relevant content in consideration of the geographical location information of the new employee. For example, if the new employee is in the office, the reception unit can prioritize inputting office-related items. Furthermore, if the new employee is on a business trip, the reception unit can also prioritize inputting items related to the business trip destination. Furthermore, if the new employee is working remotely, the reception unit can also prioritize inputting items related to remote work. This allows appropriate content to be input based on the geographical location information of the new employee.

[0072] The reception department can analyze the social media activity of new employees and enter relevant content when entering daily reports. For example, the reception department can automatically reflect the work content that the new employee has shared on social media in the daily report. The reception department can also suggest related topics in the daily report based on the new employee's social media activity. The reception department can also reflect the new employee's feedback on social media in the daily report. This makes it possible to enter appropriate content based on the new employee's social media activity.

[0073] The reception unit can customize the input method by reflecting the new employee's past feedback when inputting the daily report. For example, the reception unit customizes the input items based on the new employee's past feedback. The reception unit can also optimize the input method based on the new employee's past feedback. The reception unit can also simplify the input procedure by referring to the new employee's past feedback. This makes it possible to optimize the input method based on the new employee's past feedback.

[0074] The analysis unit can estimate the new employee's emotions and adjust the way the analysis is expressed based on the estimated emotions. For example, if the new employee is feeling stressed, the analysis unit provides a simple and easy-to-understand analysis result. The analysis unit can also provide a detailed analysis result if the new employee is relaxed. The analysis unit can also provide a complex analysis result if the new employee is concentrating. This makes it possible to provide an appropriate analysis result according to the new employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0075] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the daily report. For example, the analysis unit performs a detailed analysis on a daily report with a high level of importance. The analysis unit can also perform a simplified analysis on a daily report with a low level of importance. The analysis unit can also adjust the depth of the analysis according to the importance. This makes it possible to adjust the level of detail of the analysis according to the importance of the daily report.

[0076] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the daily report. For example, the analysis unit applies a technical analysis algorithm to a technical daily report. The analysis unit can also apply a marketing analysis algorithm to a marketing-related daily report. The analysis unit can also apply a personnel analysis algorithm to a personnel-related daily report. This allows for appropriate analysis depending on the category of the daily report.

[0077] During the analysis, the analysis unit can improve the accuracy of the analysis by referring to the past analysis results of the new employee. For example, the analysis unit adjusts the analysis algorithm based on the past analysis results of the new employee. The analysis unit can also improve the accuracy of the analysis from the past analysis results of the new employee. The analysis unit can also optimize the analysis method by referring to the past analysis results of the new employee. This makes it possible to improve the accuracy of the analysis based on the past analysis results of the new employee.

[0078] The analysis unit can estimate the new employee's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the new employee is feeling stressed, the analysis unit can provide a short, concise analysis result. If the new employee is relaxed, the analysis unit can also provide a detailed analysis result. If the new employee is concentrating, the analysis unit can also provide a long, detailed analysis result. This makes it possible to provide an analysis result of an appropriate length depending on the new employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the daily report. For example, the analysis unit prioritizes the analysis of the most recently submitted daily report. The analysis unit can also postpone the analysis of older submitted daily reports. The analysis unit can also adjust the priority of analysis based on the submission time. This makes it possible to adjust the priority of analysis based on the submission time of the daily report.

[0080] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the daily reports. For example, the analysis unit prioritizes the analysis of highly relevant daily reports. The analysis unit can also postpone the analysis of less relevant daily reports. The analysis unit can also adjust the order of analysis based on the relevance. This makes it possible to adjust the order of analysis based on the relevance of the daily reports.

[0081] During the analysis, the analysis unit can adjust the use of technical terms in the analysis according to the expertise level of the new employee. For example, if the expertise level of the new employee is low, the analysis unit can provide the analysis results in simple language. Also, if the expertise level of the new employee is high, the analysis unit can provide the analysis results using a lot of technical terms. Also, the analysis unit can adjust the way the analysis results are expressed according to the expertise level of the new employee. This makes it possible to provide appropriate analysis results according to the expertise level of the new employee.

[0082] The generation unit can estimate the new employee's emotions and adjust the way feedback is expressed based on the estimated emotions. For example, if the new employee is feeling stressed, the generation unit can provide feedback in gentle words. If the new employee is relaxed, the generation unit can also provide detailed feedback. If the new employee is concentrating, the generation unit can also provide specific feedback. This makes it possible to provide appropriate feedback according to the new employee's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0083] When generating feedback, the generation unit can adjust the level of detail of the feedback based on the importance of the daily report. For example, the generation unit provides detailed feedback for a daily report with high importance. The generation unit can also provide simplified feedback for a daily report with low importance. The generation unit can also adjust the level of detail of the feedback depending on the importance. This makes it possible to provide appropriate feedback depending on the importance of the daily report.

[0084] When generating feedback, the generation unit can apply different feedback algorithms depending on the category of the daily report. For example, the generation unit can apply a technical feedback algorithm to a technical daily report. The generation unit can also apply a marketing feedback algorithm to a marketing-related daily report. The generation unit can also apply a human resources feedback algorithm to a human resources-related daily report. This makes it possible to provide appropriate feedback depending on the category of the daily report.

[0085] When generating feedback, the generation unit can improve the accuracy of the feedback by referring to the past feedback results of the new employee. For example, the generation unit adjusts the feedback algorithm based on the past feedback results of the new employee. The generation unit can also improve the accuracy of the feedback from the past feedback results of the new employee. The generation unit can also optimize the feedback method by referring to the past feedback results of the new employee. This makes it possible to improve the accuracy of the feedback based on the past feedback results of the new employee.

[0086] The generation unit can estimate the new employee's emotions and adjust the length of the feedback based on the estimated emotions. For example, if the new employee is feeling stressed, the generation unit can provide short, to-the-point feedback. If the new employee is relaxed, the generation unit can also provide detailed feedback. If the new employee is concentrating, the generation unit can also provide long, detailed feedback. This makes it possible to provide feedback of an appropriate length depending on the new employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0087] When generating feedback, the generation unit can determine the priority of the feedback based on the submission time of the daily report. For example, the generation unit can provide feedback preferentially to a recently submitted daily report. The generation unit can also provide feedback later to a daily report that was submitted earlier. The generation unit can also adjust the priority of the feedback depending on the submission time. This makes it possible to adjust the priority of the feedback depending on the submission time of the daily report.

[0088] The generation unit can adjust the order of feedback based on the relevance of the daily report when generating the feedback. For example, the generation unit provides feedback preferentially to a highly relevant daily report. The generation unit can also provide feedback later to a less relevant daily report. The generation unit can also adjust the order of feedback based on the relevance. This makes it possible to adjust the order of feedback based on the relevance of the daily report.

[0089] When generating the feedback, the generation unit may adjust the use of technical terms in the feedback depending on the expertise level of the new employee. For example, if the expertise level of the new employee is low, the generation unit may provide the feedback in simple language. Alternatively, if the expertise level of the new employee is high, the generation unit may provide the feedback using a lot of technical terms. Alternatively, the generation unit may adjust the way the feedback is expressed depending on the expertise level of the new employee. This allows the new employee to provide appropriate feedback depending on the expertise level of the new employee.

[0090] The providing unit can estimate the new employee's emotions and adjust the method of providing feedback based on the estimated emotions. For example, if the new employee is feeling stressed, the providing unit can provide feedback in gentle words. If the new employee is relaxed, the providing unit can also provide detailed feedback. If the new employee is concentrating, the providing unit can also provide specific feedback. This makes it possible to provide appropriate feedback according to the new employee's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0091] When providing feedback, the providing unit can select the optimal feedback method by referring to the new employee's past feedback history. For example, the providing unit can preferentially provide feedback formats that the new employee has preferred in the past. The providing unit can also select the optimal feedback method from the new employee's past feedback history. The providing unit can also optimize the feedback method by referring to feedback that the new employee has received in the past. This makes it possible to select the optimal feedback method based on the new employee's past feedback history.

[0092] When providing feedback, the providing unit can customize the content of the feedback to be provided in accordance with the new employee's current task. For example, the providing unit provides feedback related to the task the new employee is currently working on. The providing unit can also customize the content of the feedback in accordance with the new employee's current task. The providing unit can also provide feedback on problems the new employee is facing in the current task. This makes it possible to provide appropriate feedback in accordance with the new employee's current task.

[0093] When providing feedback, the providing unit can analyze the new employee's reaction to the feedback and improve the method of providing the feedback. For example, if the new employee has a positive reaction to the feedback, the providing unit continues the method of providing the feedback. In addition, if the new employee has a negative reaction to the feedback, the providing unit can also change the method of providing the feedback. In addition, the providing unit can analyze the new employee's reaction to the feedback and optimize the method of providing the feedback. In this way, the method of providing the feedback can be optimized based on the new employee's reaction to the feedback.

[0094] The providing unit can estimate the new employee's emotions and adjust the feedback provision procedure based on the estimated emotions. For example, if the new employee is feeling stressed, the providing unit can provide feedback using simple procedures. Furthermore, if the new employee is relaxed, the providing unit can provide feedback using detailed procedures. Furthermore, if the new employee is concentrating, the providing unit can provide feedback using specific procedures. This makes it possible to provide feedback using appropriate procedures depending on the new employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0095] When providing feedback, the providing unit can select the optimal providing method in consideration of the device information of the new employee. For example, if the new employee uses a smartphone, the providing unit can provide feedback optimized for the smartphone. Furthermore, if the new employee uses a tablet, the providing unit can also provide feedback optimized for the tablet. Furthermore, if the new employee uses a desktop, the providing unit can also provide feedback optimized for the desktop. In this way, optimal feedback can be provided based on the device information of the new employee.

[0096] When providing feedback, the providing unit can make the provided content multilingual in accordance with the language setting of the new employee. For example, the providing unit automatically sets the language of the feedback based on the language setting of the new employee's device. The providing unit can also provide a language switching function when the new employee speaks multiple languages. Furthermore, if the new employee selects a specific language, the providing unit can provide feedback in that language. This makes it possible to provide feedback in an appropriate language based on the new employee's language setting.

[0097] When providing feedback, the providing unit can analyze the new employee's reaction to the feedback in real time and immediately adjust the method of providing the feedback. For example, if the new employee has a positive reaction to the feedback, the providing unit continues the method of providing the feedback. In addition, if the new employee has a negative reaction to the feedback, the providing unit can also change the method of providing the feedback. In addition, the providing unit can analyze the new employee's reaction to the feedback in real time and optimize the method of providing the feedback. In this way, the method of providing the feedback can be optimized in real time based on the new employee's reaction to the feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives the new employee's daily report by text input, voice input, or image input. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the daily report using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates feedback based on the analyzed daily report. The provision unit is realized by the output device 40 of the smart device 14 and provides the generated feedback to the new employee in text, voice, or visual format. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives the new employee's daily report by voice input. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the daily report using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates feedback based on the analyzed daily report. The provision unit is realized by the speaker 240 of the smart glasses 214 and provides the generated feedback to the new employee in audio format. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives the new employee's daily report by voice input. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the daily report using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates feedback based on the analyzed daily report. The provision unit is realized by the speaker 240 of the headset-type terminal 314 and provides the generated feedback to the new employee in audio format. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives the new employee's daily report by voice input. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the daily report using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates feedback based on the analyzed daily report. The provision unit is realized by the speaker 240 of the robot 414 and provides the generated feedback to the new employee in audio format.

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

[0099] When new employees enter their daily reports, the reception unit can automatically complete the input content based on their past feedback history. For example, if a new employee has received feedback on a specific project in the past, it will automatically complete keywords and phrases related to that project. The reception unit can also learn expressions and phrases that new employees have frequently used in the past and use them to complete the input content. Furthermore, the reception unit can also refer to the content of daily reports that new employees have submitted in the past and automatically suggest similar content. This makes the entry of daily reports for new employees more efficient and reduces the effort required for input.

[0100] The reception unit can estimate the emotions of new employees and customize the input content of the daily report based on the estimated emotions. For example, if a new employee is feeling stressed, the reception unit can have them input their daily report in the form of simple questions. If the new employee is relaxed, the reception unit can also suggest more detailed input items. Furthermore, if the new employee is concentrating, the reception unit can suggest input items that focus on specific tasks or problems. This makes it possible to provide appropriate input content according to the new employee's emotions and improve the quality of the daily report.

[0101] When new employees enter their daily reports, the reception department can check the consistency of the information based on past data. For example, the reception department can compare the current information entered with the work content and progress status previously recorded by the new employee to check for consistency. The reception department can also check whether the problems and issues previously recorded by the new employee have been resolved. Furthermore, the reception department can determine whether the current information entered is appropriate based on feedback the new employee has received in the past. This can improve the quality of new employees' daily reports and increase the accuracy of feedback.

[0102] When a new employee enters their daily report, the reception department can suggest what to enter by referencing the daily report data of other new employees. For example, the reception department can suggest related input items based on the daily report content of other new employees working on the same project. The reception department can also supplement the input content based on the problems and solutions faced by other new employees. Furthermore, the reception department can optimize the input content by referring to feedback received from other new employees. This promotes information sharing among new employees and improves the quality of daily reports.

[0103] The reception department can provide real-time feedback on the content entered by new employees as they enter their daily reports. For example, it can provide advice and suggestions for improvement on the content entered by the new employee on the spot. The reception department can also provide related materials and information based on the content entered by the new employee. Furthermore, the reception department can display feedback from other new employees and elders in real time on the content entered by the new employee. This makes daily report entry by new employees more effective and improves the quality of feedback.

[0104] The analysis unit can estimate the emotions of new employees and adjust the way in which the analysis results are presented based on the estimated emotions. For example, if a new employee is feeling stressed, the analysis results can be presented in a concise summary. The analysis unit can also provide detailed analysis results if the new employee is relaxed. Furthermore, if the new employee is concentrating, the analysis unit can present the analysis results in a visually easy-to-understand manner. This makes it possible to provide appropriate analysis results according to the new employee's emotions and promote understanding.

[0105] When analyzing the contents of a new employee's daily report, the analysis unit can improve the accuracy of the analysis by referring to the daily report data of other new employees. For example, common problems and issues can be extracted based on the daily report contents of other new employees working on the same project. The analysis unit can also optimize the analysis results by referring to feedback received by other new employees. Furthermore, the analysis unit can adjust the analysis algorithm based on the daily report data of other new employees. This improves the accuracy of the analysis and enables the provision of more appropriate feedback.

[0106] When analyzing the contents of new employee daily reports, the analysis department can improve the accuracy of the analysis by referencing external data sources. For example, the analysis department can analyze the contents of daily reports based on industry trends and benchmark data. The analysis department can also optimize the analysis results by referring to the opinions and advice of external experts. Furthermore, the analysis department can also refer to external databases to provide information related to the contents of daily reports. This improves the accuracy of the analysis and enables more appropriate feedback to be provided.

[0107] When analyzing the contents of new employee daily reports, the analysis unit can automatically adjust the analysis algorithm based on past analysis results. For example, it can compare past analysis results with current analysis results to improve the accuracy of the algorithm. The analysis unit can also learn from past analysis results and optimize the analysis method. Furthermore, the analysis unit can determine analysis priorities based on past analysis results. This improves analysis accuracy and enables more appropriate feedback to be provided.

[0108] The analysis unit can estimate the emotions of new employees and adjust the method of providing feedback based on the estimated emotions. For example, if a new employee is feeling stressed, the analysis unit can provide feedback in positive words based on the analysis results. The analysis unit can also provide detailed feedback if the new employee is relaxed. Furthermore, if the new employee is concentrating, the analysis unit can provide feedback that includes a specific action plan. This makes it possible to provide appropriate feedback according to the emotions of new employees and promote their growth.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The reception department inputs the new employee's daily report. The new employee's daily report includes the job content, progress, problems, etc. The reception department can accept the daily report by text input, voice input, image input, etc. Step 2: The analysis unit uses the generation AI to analyze the daily report entered by the reception unit. The analysis is performed using methods such as text analysis, sentiment analysis, and keyword extraction. Step 3: The generator uses the generation AI to generate feedback based on the daily report analyzed by the analyzer. The feedback includes advice, evaluations, and areas for improvement. Step 4: The delivery unit provides the generated feedback to the new employee. The delivery unit can provide the feedback in the form of text, audio, visual, etc.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires 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.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] 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.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

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

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

[0120] 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).

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

[0122] 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.

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

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0125] 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.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. 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. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may 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.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

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

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

[0136] 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).

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

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

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

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0141] 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.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires 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.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. 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. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0148] 7, 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.

[0149] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

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

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

[0152] 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).

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

[0154] 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.

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

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

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0158] 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.

[0159] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires 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.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. 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. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may 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.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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."

[0170] 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.

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

[0172] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a 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.

[0173] 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.

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

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] [Explanation of symbols]

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

Claims

1. A reception desk where new employees enter their daily reports, an analysis unit that analyzes the daily report input by the reception unit; a generation unit that generates feedback based on the daily report analyzed by the analysis unit; a providing unit that provides the feedback generated by the generating unit; Equipped with A system characterized by:

2. The reception unit Estimate the emotions of new employees and adjust the timing of daily report entries based on the estimated emotions 2. The system of claim 1.

3. The reception unit Analyze the past daily report submission history of new employees and select the appropriate input method 2. The system of claim 1.

4. The reception unit Filter daily reports based on the new hire's current projects and areas of interest 2. The system of claim 1.

5. The reception unit When entering daily reports, select the appropriate input method depending on the input method of new employees.

2. The system of claim 1.

6. The reception unit Estimate the emotions of new employees and prioritize the daily reports to be entered based on the estimated emotions.

2. The system of claim 1.

7. The reception unit When entering daily reports, prioritize relevant content by taking into account the geographic location of new employees.

2. The system of claim 1.

8. The reception unit When entering daily reports, analyze the social media activity of new employees and enter relevant information.

2. The system of claim 1.

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