System

Abstract

The system according to the embodiment analyzes a voice or an image of a user, provides feedback based on the result, and further provides a talent management function.SOLUTION: A system according to an embodiment includes an analysis unit, a feedback unit, and a talent management unit. The analysis unit analyzes a voice or an image of the user. The feedback unit provides feedback based on the result analyzed by the analysis unit. The talent management unit provides a talent management function based on the data obtained by the feedback unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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 technologies do not adequately analyze users' voices and images to provide feedback and perform talent management based on that data, so there is room for improvement.

[0005] The system according to the embodiment aims to analyze the user's voice and image, provide feedback based on the results, and also provide talent management functions. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a feedback unit, and a talent management unit. The analysis unit analyzes the user's voice and image. The feedback unit provides feedback based on the results of the analysis by the analysis unit. The talent management unit provides a talent management function based on the data obtained by the feedback unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the user's voice and image, provide feedback based on the results, and also provide talent management functions. [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 role-playing system according to an embodiment of the present invention allows users to engage in role-playing against an AI "anytime," "anywhere," and "unlimited." When a user engages in role-playing based on a business scenario, the generation AI analyzes the user's voice and image and provides real-time feedback. Examples of such role-playing include practicing sales pitches and rehearsing presentations. Furthermore, the role-playing system provides talent management functions to companies employing users based on the accumulated data. For example, the system evaluates each user's skills and performance and proposes appropriate personnel placement and career paths. This allows companies to maximize their employees' capabilities and realize efficient human resource management. This allows users to engage in role-playing against an AI "anytime," "anywhere," and "unlimited." For example, when a user engages in role-playing based on a business scenario, the generation AI analyzes the user's voice and image and provides real-time feedback. Furthermore, the role-playing system provides talent management functions to companies employing users based on the accumulated data. This allows companies to maximize their employees' capabilities and realize efficient human resource management.

[0029] The role-playing system according to the embodiment includes an analysis unit, a feedback unit, and a talent management unit. The analysis unit analyzes the user's voice and images. For example, the analysis unit analyzes the content of the user's statements using voice analysis technology. The analysis unit can also analyze the user's facial expressions using image analysis technology. The analysis unit can also analyze the tone of the voice to infer the user's emotions. For example, the analysis unit analyzes the pitch and speed of the voice to infer the user's emotions. The feedback unit provides feedback based on the results of the analysis by the analysis unit. For example, the feedback unit provides text feedback based on the analysis results. The feedback unit can also provide audio feedback. The feedback unit can also provide feedback in real time. For example, the feedback unit provides feedback in real time based on the content of the user's statements and facial expressions. The talent management unit provides a talent management function based on data obtained by the feedback unit. For example, the talent management unit evaluates the skills and performance of each user. The talent management unit can also suggest appropriate personnel placement and career paths. The talent management department can also make suggestions to companies on how to maximize the capabilities of their employees. As a result, the role-playing system according to the embodiment can analyze the user's voice and image, provide feedback, and provide a talent management function.

[0030] The role-playing system includes a reception unit that receives user input. The reception unit receives user input. For example, the reception unit can receive voice input. The reception unit can also receive text input. The reception unit can also receive image input. For example, the reception unit converts content input by voice by the user into text using voice recognition technology. The reception unit can also accept content input by text by the user as is. The reception unit can also analyze content input by images by the user using image recognition technology. In this way, by receiving user input, the reception unit can acquire data that allows the system to analyze the user's voice and images, provide feedback, and provide talent management functions.

[0031] The analysis unit can analyze the user's voice and images and evaluate the content of the user's speech, facial expression, and tone of voice. The analysis unit can analyze the content of the user's speech using, for example, voice analysis technology. For example, the analysis unit can analyze the content of the user's speech using keyword extraction technology. The analysis unit can also analyze the content of the user's speech using context analysis technology. The analysis unit can also analyze the user's facial expression using image analysis technology. For example, the analysis unit can analyze the user's facial expression using a facial expression recognition algorithm. The analysis unit can also analyze the user's facial expression using emotion estimation technology. The analysis unit can also analyze the tone of the voice and estimate the user's emotion. For example, the analysis unit can analyze the user's tone of voice using voice pitch analysis technology. The analysis unit can also analyze the user's tone of voice using emotion estimation technology. In this way, the analysis unit can provide more detailed feedback by analyzing the user's voice and images and evaluating the content of the user's speech, facial expression, tone of voice, etc.

[0032] The feedback unit can provide instant feedback based on the analysis results. The feedback unit, for example, provides text feedback based on the analysis results. For example, the feedback unit provides text feedback based on the user's utterances and facial expressions. The feedback unit can also provide audio feedback. For example, the feedback unit provides audio feedback based on the user's utterances and tone of voice. The feedback unit can also provide feedback in real time. For example, the feedback unit provides feedback in real time based on the user's utterances and facial expressions. In this way, the feedback unit can provide feedback in real time based on the analysis results, allowing the user to immediately understand areas for improvement and improve their skills.

[0033] The talent management department can evaluate each user's skills and performance and propose appropriate personnel placement and career paths. The talent management department, for example, evaluates each user's skills and performance. For example, the talent management department evaluates a user's skills and performance based on performance evaluation data. The talent management department can also evaluate a user's skills and performance using a skill matrix. The talent management department also proposes appropriate personnel placement and career paths. For example, the talent management department proposes placement to an appropriate department based on the user's skills and performance. The talent management department can also propose a career path for the user. In this way, the talent management department can evaluate each user's skills and performance and propose appropriate personnel placement and career paths, allowing companies to maximize the capabilities of their employees and achieve efficient human resource management.

[0034] The talent management department can make proposals to companies to maximize the capabilities of their employees. The talent management department, for example, makes proposals to companies to maximize the capabilities of their employees. For example, the talent management department can propose training programs. The talent management department can also propose career development plans. The talent management department can also propose training programs to improve employee skills. In this way, by making proposals to companies to maximize the capabilities of their employees, companies can maximize the capabilities of their employees and improve business efficiency and productivity.

[0035] During analysis, the analysis unit can optimize the analysis algorithm by referring to the user's past role-playing data. The analysis unit, for example, refers to the user's past role-playing data. For example, the analysis unit adjusts the analysis algorithm based on data of role-playing sessions performed by the user in the past. The analysis unit can also refer to the user's past role-playing data to learn specific patterns and improve analysis accuracy. The analysis unit can also optimize the analysis algorithm based on the user's past role-playing data and provide more accurate feedback. In this way, the analysis unit can optimize the analysis algorithm and provide more accurate feedback by referring to the user's past role-playing data.

[0036] During analysis, the analysis unit can customize the analysis criteria according to the user's job content and position. The analysis unit customizes the analysis criteria according to the user's job content and position, for example. For example, if the user is in sales, the analysis unit applies analysis criteria for sales talk. Furthermore, if the user is in a managerial position, the analysis unit can also apply analysis criteria for leadership skills. Furthermore, if the user is in a technical position, the analysis unit can also apply analysis criteria for technical knowledge and skills. In this way, the analysis unit can provide more appropriate feedback by customizing the analysis criteria according to the user's job content and position.

[0037] During analysis, the analysis unit can analyze not only the user's speech content, facial expression, and tone of voice, but also their gestures and posture. For example, the analysis unit analyzes the user's hand movements and posture in addition to the user's speech content. For example, the analysis unit analyzes the user's hand movements using motion analysis technology. The analysis unit can also analyze the user's posture using a posture recognition algorithm. The analysis unit can also analyze the user's facial expressions and eye movements along with the user's tone of voice. For example, the analysis unit analyzes the user's facial expressions using facial expression recognition technology. The analysis unit can also analyze the user's eye movements and track the user's gaze. The analysis unit can also analyze the user's gestures and posture to suggest areas for improvement in the presentation. In this way, the analysis unit can provide more detailed feedback by analyzing the user's gestures and posture in addition to the user's speech content, facial expression, and tone of voice.

[0038] During analysis, the analysis unit can adjust the analysis criteria taking into account the user's geographical location information. The analysis unit adjusts the analysis criteria, for example, taking into account the user's geographical location information. For example, when a user role-plays in different regions, the analysis unit adjusts the analysis criteria taking into account the culture and business etiquette specific to that region. Furthermore, when a user role-plays while on a business trip, the analysis unit can apply analysis criteria that reflect the characteristics of that region. Furthermore, when a user role-plays overseas, the analysis unit can adjust the analysis criteria taking into account international business etiquette. In this way, the analysis unit can adjust the analysis criteria taking into account the user's geographical location information, thereby enabling analysis that reflects the culture and business etiquette specific to that region.

[0039] During the analysis, the analysis unit can analyze the user's social media activities and reflect related data in the analysis. The analysis unit, for example, analyzes the user's social media activities. For example, the analysis unit analyzes the content of the user's comments on social media and reflects that data in the analysis. The analysis unit can also adjust the analysis criteria based on the user's social media activity history. The analysis unit can also analyze the reactions of the user's followers and friends on social media and reflect that data in the analysis. In this way, the analysis unit can provide more detailed feedback by analyzing the user's social media activities and reflecting related data in the analysis.

[0040] During analysis, the analysis unit can customize the analysis algorithm by reflecting the user's past feedback. The analysis unit, for example, reflects the user's past feedback. For example, the analysis unit adjusts the analysis algorithm based on feedback the user has received in the past. The analysis unit can also refer to the content of the user's past feedback and focus on specific areas for improvement when performing analysis. The analysis unit can also customize the analysis algorithm based on the user's past feedback history to provide more accurate feedback. In this way, the analysis unit can provide more accurate feedback by customizing the analysis algorithm by reflecting the user's past feedback.

[0041] When providing feedback, the feedback unit can provide optimal feedback by referring to the user's past feedback history. The feedback unit, for example, refers to the user's past feedback history. For example, the feedback unit provides optimal feedback based on feedback the user has received in the past. The feedback unit can also refer to the user's past feedback history and provide feedback that focuses on specific areas for improvement. The feedback unit can also provide customized feedback based on the content of the user's past feedback. In this way, the feedback unit can provide more appropriate feedback by referring to the user's past feedback history.

[0042] When providing feedback, the feedback unit can customize the content of the feedback according to the user's job content and position. The feedback unit customizes the content of the feedback according to, for example, the user's job content and position. For example, if the user is in sales, the feedback unit can provide feedback on sales talk. Also, if the user is in a managerial position, the feedback unit can provide feedback on leadership skills. Also, if the user is in a technical position, the feedback unit can provide feedback on technical knowledge and skills. In this way, the feedback unit can provide more appropriate feedback by customizing the content of the feedback according to the user's job content and position.

[0043] When providing feedback, the feedback unit can provide feedback on the user's gestures and posture in addition to the content of the user's speech, facial expression, and tone of voice. For example, the feedback unit provides feedback on the user's hand movements and posture in addition to the content of the user's speech. For example, the feedback unit can provide feedback on the user's hand movements using motion analysis technology. The feedback unit can also provide feedback on the user's posture using a posture recognition algorithm. The feedback unit can also provide feedback on the user's facial expressions and eye movements in addition to the tone of the user's voice. For example, the feedback unit can provide feedback on the user's facial expressions using facial expression recognition technology. The feedback unit can also analyze eye movements and provide feedback on the user's gaze. The feedback unit can also provide feedback on the user's gestures and posture and point out areas for improvement in the presentation. In this way, the feedback unit can provide more detailed feedback by providing feedback on the user's gestures and posture in addition to the content of the user's speech, facial expression, and tone of voice.

[0044] When providing feedback, the feedback unit can adjust the content of the feedback taking into account the user's geographical location information. The feedback unit adjusts the content of the feedback, for example, taking into account the user's geographical location information. For example, when a user role-plays in different regions, the feedback unit provides feedback taking into account local culture and business etiquette. Furthermore, when a user role-plays while on a business trip, the feedback unit can provide feedback that reflects the characteristics of the region. Furthermore, when a user role-plays overseas, the feedback unit can provide feedback taking into account international business etiquette. In this way, the feedback unit can adjust the content of the feedback taking into account the user's geographical location information, thereby enabling feedback that reflects local culture and business etiquette.

[0045] When providing feedback, the feedback unit can analyze the user's social media activity and provide relevant feedback. The feedback unit, for example, analyzes the user's social media activity. For example, the feedback unit analyzes the content of the user's social media posts and provides feedback based on that data. The feedback unit can also provide feedback that focuses on specific areas for improvement based on the user's social media activity history. The feedback unit can also analyze the reactions of the user's followers and friends on social media and reflect that data in the feedback. This allows the feedback unit to provide more detailed feedback by analyzing the user's social media activity and providing relevant feedback.

[0046] When providing feedback, the feedback unit can customize the content of the feedback by reflecting the user's past feedback. The feedback unit, for example, reflects the user's past feedback. For example, the feedback unit provides optimal feedback based on feedback the user has received in the past. The feedback unit can also refer to the user's past feedback history and provide feedback that emphasizes specific areas for improvement. The feedback unit can also provide customized feedback based on the content of the user's past feedback. In this way, the feedback unit can provide more appropriate feedback by customizing the content of the feedback by reflecting the user's past feedback.

[0047] When providing talent management, the talent management department can make optimal suggestions by referring to the user's past performance data. The talent management department, for example, refers to the user's past performance data. For example, the talent management department can suggest an optimal career path based on data from role-playing sessions the user has performed in the past. The talent management department can also refer to the user's past performance data to suggest training to improve specific skills. The talent management department can also suggest placement in an appropriate department based on the user's past performance data. In this way, the talent management department can provide more appropriate suggestions by referring to the user's past performance data.

[0048] When providing talent management, the talent management department can customize the proposal content according to the user's job content and position. The talent management department customizes the proposal content according to the user's job content and position, for example. For example, if the user is in sales, the talent management department can propose training to improve sales skills. Furthermore, if the user is in a managerial position, the talent management department can also propose training to improve leadership skills. Furthermore, if the user is in a technical position, the talent management department can also propose training to improve technical knowledge and skills. In this way, the talent management department can provide more appropriate proposals by customizing the proposal content according to the user's job content and position.

[0049] When providing talent management, the talent management unit can consider data regarding gestures and posture in addition to the content of the user's speech, facial expressions, and tone of voice. For example, the talent management unit considers data regarding hand movements and posture in addition to the content of the user's speech. For example, the talent management unit can consider data regarding the user's hand movements using motion analysis technology. The talent management unit can also consider data regarding the user's posture using a posture recognition algorithm. The talent management unit can also consider data regarding facial expressions and eye movements in addition to the user's tone of voice. For example, the talent management unit can consider data regarding the user's facial expressions using facial expression recognition technology. The talent management unit can also analyze eye movements and consider data regarding the user's gaze. The talent management unit can also consider data regarding the user's gestures and posture to make suggestions for improving presentation skills. In this way, the talent management unit can provide more detailed suggestions by considering data regarding gestures and posture in addition to the content of the user's speech, facial expressions, and tone of voice.

[0050] When providing talent management services, the talent management department can adjust the proposal content taking into account the user's geographic location information. The talent management department, for example, adjusts the proposal content taking into account the user's geographic location information. For example, if the user works in a different region, the talent management department adjusts the proposal content taking into account the culture and business etiquette specific to that region. Furthermore, if the user works on a business trip, the talent management department can provide proposal content that reflects the characteristics of that region. Furthermore, if the user works overseas, the talent management department can adjust the proposal content taking into account international business etiquette. In this way, the talent management department can adjust the proposal content taking into account the user's geographic location information, thereby enabling proposals that reflect the culture and business etiquette specific to that region.

[0051] When providing talent management, the talent management unit can analyze the user's social media activities and make relevant suggestions. The talent management unit, for example, analyzes the user's social media activities. For example, the talent management unit analyzes the content of the user's comments on social media and makes suggestions based on that data. The talent management unit can also make suggestions for improving specific skills based on the user's social media activity history. The talent management unit can also analyze the reactions of the user's followers and friends on social media and reflect that data in its suggestions. This allows the talent management unit to provide more detailed suggestions by analyzing the user's social media activities and making relevant suggestions.

[0052] When providing talent management, the talent management department can customize the content of the proposal by reflecting the user's past feedback. The talent management department, for example, reflects the user's past feedback. For example, the talent management department makes optimal proposals based on feedback the user has received in the past. The talent management department can also refer to the user's past feedback history and make proposals that emphasize specific areas for improvement. The talent management department can also make customized proposals based on the user's past feedback. In this way, the talent management department can provide more appropriate proposals by customizing the content of the proposal by reflecting the user's past feedback.

[0053] The reception unit can select the optimal reception method by referring to the user's past input history when receiving the input. The reception unit, for example, refers to the user's past input history. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also select the optimal reception method based on the user's past input history. The reception unit can also predict and suggest an input method to be used in a specific time period from the user's past input history. In this way, the reception unit can provide a more appropriate reception method by referring to the user's past input history.

[0054] The reception unit can customize the input reception criteria according to the user's job content and job title when receiving the input. The reception unit customizes the input reception criteria according to the user's job content and job title, for example. For example, if the user is in sales, the reception unit applies input reception criteria related to sales talk. Furthermore, if the user is in a managerial position, the reception unit can also apply input reception criteria related to leadership skills. Furthermore, if the user is in a technical position, the reception unit can also apply input reception criteria related to technical knowledge and skills. In this way, the reception unit can provide a more appropriate reception method by customizing the input reception criteria according to the user's job content and job title.

[0055] The reception unit can select the optimal reception means depending on the user's input method when receiving the input. The reception unit selects the optimal reception means depending on the user's input method, for example. For example, when the user uses voice input, the reception unit can accept the input using voice recognition technology. Furthermore, when the user uses text input, the reception unit can also accept the input using text analysis technology. Furthermore, when the user uses image input, the reception unit can also accept the input using image recognition technology. In this way, the reception unit can provide a more appropriate reception method by selecting the optimal reception means depending on the user's input method.

[0056] The reception unit can prioritize receiving highly relevant inputs in consideration of the user's geographical location information when receiving the inputs. The reception unit, for example, prioritizes receiving highly relevant inputs in consideration of the user's geographical location information. For example, when a user makes an input in a different region, the reception unit prioritizes receiving information specific to the region. Furthermore, when a user makes an input while on a business trip, the reception unit can also prioritize receiving inputs that reflect the characteristics of the region. Furthermore, when a user makes an input while overseas, the reception unit can also prioritize receiving international information. In this way, the reception unit prioritizes receiving highly relevant inputs in consideration of the user's geographical location information, thereby enabling reception that reflects region-specific information.

[0057] The reception unit can analyze the user's social media activity and receive related inputs at the time of reception. The reception unit, for example, analyzes the user's social media activity. For example, the reception unit analyzes the content of the user's comments on social media and receives inputs based on that data. The reception unit can also preferentially receive specific inputs based on the user's social media activity history. The reception unit can also analyze the reactions of the user's followers and friends on social media and reflect that data in the inputs. In this way, the reception unit can analyze the user's social media activity and receive related inputs, enabling more detailed reception.

[0058] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a call. The reception unit, for example, reflects the user's past feedback. For example, the reception unit provides the optimal reception method based on feedback the user has received in the past. The reception unit can also refer to the user's past feedback history and customize the reception method by focusing on specific areas for improvement. The reception unit can also provide a customized reception method based on the content of the user's past feedback. In this way, the reception unit can provide a more appropriate reception method by customizing the reception method by reflecting the user's past feedback.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] When analyzing a user's voice or image, the analysis unit can also optimize the analysis algorithm by referring to the user's past feedback history. For example, the analysis unit can adjust the analysis algorithm based on feedback the user has received in the past to perform more accurate analysis. The analysis unit can also refer to the content of the user's past feedback and focus on specific areas for improvement in the analysis. Furthermore, the analysis unit can customize the analysis algorithm based on the user's past feedback history and provide more detailed feedback. In this way, the analysis unit can provide more accurate feedback by optimizing the analysis algorithm by reflecting the user's past feedback.

[0061] When accepting input from a user, the acceptance unit can select the optimal acceptance method by referring to the user's past input history. For example, the acceptance unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The acceptance unit can also select the optimal acceptance method based on the user's past input history. Furthermore, the acceptance unit can predict and suggest an input method to be used in a specific time period from the user's past input history. In this way, the acceptance unit can provide a more appropriate acceptance method by referring to the user's past input history.

[0062] When analyzing a user's voice or image, the analysis unit can adjust the analysis criteria taking into account the user's geographical location information. For example, when a user role-plays in different regions, the analysis unit adjusts the analysis criteria taking into account the culture and business etiquette specific to that region. In addition, when a user role-plays while on a business trip, the analysis unit can apply analysis criteria that reflect the characteristics of that region. Furthermore, when a user role-plays overseas, the analysis unit can adjust the analysis criteria taking into account international business etiquette. In this way, the analysis unit can adjust the analysis criteria taking into account the user's geographical location information, thereby enabling analysis that reflects the culture and business etiquette specific to that region.

[0063] When analyzing a user's voice and images, the analysis unit can analyze the user's social media activities and reflect related data in the analysis. For example, the analysis unit can analyze the content of the user's social media posts and reflect that data in the analysis. The analysis unit can also adjust the analysis criteria based on the user's social media activity history. Furthermore, the analysis unit can analyze the reactions of the user's followers and friends on social media and reflect that data in the analysis. This allows the analysis unit to provide more detailed feedback by analyzing the user's social media activities and reflecting related data in the analysis.

[0064] When providing instant feedback based on the analysis results, the feedback unit can customize the content of the feedback by referring to the user's past feedback history. For example, the feedback unit can provide optimal feedback based on feedback the user has received in the past. The feedback unit can also provide feedback that focuses on specific areas for improvement by referring to the user's past feedback history. Furthermore, the feedback unit can provide customized feedback based on the content of the user's past feedback. In this way, the feedback unit can provide more appropriate feedback by customizing the content of the feedback to reflect the user's past feedback.

[0065] When evaluating each user's skills and performance, the talent management department can adjust the evaluation criteria taking into account the user's geographic location information. For example, if a user works in a different region, the talent management department can adjust the evaluation criteria taking into account the culture and business etiquette specific to that region. In addition, if a user works while on a business trip, the talent management department can apply evaluation criteria that reflect the characteristics of that region. Furthermore, if a user works overseas, the talent management department can adjust the evaluation criteria taking into account international business etiquette. In this way, the talent management department can adjust the evaluation criteria taking into account the user's geographic location information, enabling evaluation that reflects the culture and business etiquette specific to that region.

[0066] When evaluating each user's skills and performance, the talent management department can analyze the user's social media activities and reflect the related data in the evaluation. For example, the talent management department can analyze the content of the user's social media posts and reflect the data in the evaluation. The talent management department can also adjust the evaluation criteria based on the user's social media activity history. Furthermore, the talent management department can analyze the reactions of the user's followers and friends on social media and reflect the data in the evaluation. This allows the talent management department to provide a more detailed evaluation by analyzing the user's social media activities and reflecting the related data in the evaluation.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The analysis unit analyzes the user's voice and image. For example, the analysis unit uses voice analysis technology to analyze the content of the user's speech and image analysis technology to analyze the user's facial expressions. It can also analyze the tone of the voice to estimate the user's emotions. Specifically, it analyzes the pitch and speed of the voice to estimate emotions. Step 2: The feedback unit provides feedback based on the results of the analysis by the analysis unit. For example, it can provide text feedback or voice feedback based on the analysis results, and can also provide feedback in real time. Specifically, it provides real-time feedback based on the content of the user's remarks and facial expressions. Step 3: The Talent Management Department provides talent management functions based on the data obtained by the Feedback Department. For example, it evaluates each user's skills and performance and proposes appropriate personnel placement and career paths. It can also make suggestions to companies on how to maximize the capabilities of their employees.

[0069] (Example 2) A role-playing system according to an embodiment of the present invention allows users to engage in role-playing against an AI "anytime," "anywhere," and "unlimited." When a user engages in role-playing based on a business scenario, the generation AI analyzes the user's voice and image and provides real-time feedback. Examples of such role-playing include practicing sales pitches and rehearsing presentations. Furthermore, the role-playing system provides talent management functions to companies employing users based on the accumulated data. For example, the system evaluates each user's skills and performance and proposes appropriate personnel placement and career paths. This allows companies to maximize their employees' capabilities and realize efficient human resource management. This allows users to engage in role-playing against an AI "anytime," "anywhere," and "unlimited." For example, when a user engages in role-playing based on a business scenario, the generation AI analyzes the user's voice and image and provides real-time feedback. Furthermore, the role-playing system provides talent management functions to companies employing users based on the accumulated data. This allows companies to maximize their employees' capabilities and realize efficient human resource management.

[0070] The role-playing system according to the embodiment includes an analysis unit, a feedback unit, and a talent management unit. The analysis unit analyzes the user's voice and images. For example, the analysis unit analyzes the content of the user's statements using voice analysis technology. The analysis unit can also analyze the user's facial expressions using image analysis technology. The analysis unit can also analyze the tone of the voice to infer the user's emotions. For example, the analysis unit analyzes the pitch and speed of the voice to infer the user's emotions. The feedback unit provides feedback based on the results of the analysis by the analysis unit. For example, the feedback unit provides text feedback based on the analysis results. The feedback unit can also provide audio feedback. The feedback unit can also provide feedback in real time. For example, the feedback unit provides feedback in real time based on the content of the user's statements and facial expressions. The talent management unit provides a talent management function based on data obtained by the feedback unit. For example, the talent management unit evaluates the skills and performance of each user. The talent management unit can also suggest appropriate personnel placement and career paths. The talent management department can also make suggestions to companies on how to maximize the capabilities of their employees. As a result, the role-playing system according to the embodiment can analyze the user's voice and image, provide feedback, and provide a talent management function.

[0071] The role-playing system includes a reception unit that receives user input. The reception unit receives user input. For example, the reception unit can receive voice input. The reception unit can also receive text input. The reception unit can also receive image input. For example, the reception unit converts content input by voice by the user into text using voice recognition technology. The reception unit can also accept content input by text by the user as is. The reception unit can also analyze content input by images by the user using image recognition technology. In this way, by receiving user input, the reception unit can acquire data that allows the system to analyze the user's voice and images, provide feedback, and provide talent management functions.

[0072] The analysis unit can analyze the user's voice and images and evaluate the content of the user's speech, facial expression, and tone of voice. The analysis unit can analyze the content of the user's speech using, for example, voice analysis technology. For example, the analysis unit can analyze the content of the user's speech using keyword extraction technology. The analysis unit can also analyze the content of the user's speech using context analysis technology. The analysis unit can also analyze the user's facial expression using image analysis technology. For example, the analysis unit can analyze the user's facial expression using a facial expression recognition algorithm. The analysis unit can also analyze the user's facial expression using emotion estimation technology. The analysis unit can also analyze the tone of the voice and estimate the user's emotion. For example, the analysis unit can analyze the user's tone of voice using voice pitch analysis technology. The analysis unit can also analyze the user's tone of voice using emotion estimation technology. In this way, the analysis unit can provide more detailed feedback by analyzing the user's voice and images and evaluating the content of the user's speech, facial expression, tone of voice, etc.

[0073] The feedback unit can provide instant feedback based on the analysis results. The feedback unit, for example, provides text feedback based on the analysis results. For example, the feedback unit provides text feedback based on the user's utterances and facial expressions. The feedback unit can also provide audio feedback. For example, the feedback unit provides audio feedback based on the user's utterances and tone of voice. The feedback unit can also provide feedback in real time. For example, the feedback unit provides feedback in real time based on the user's utterances and facial expressions. In this way, the feedback unit can provide feedback in real time based on the analysis results, allowing the user to immediately understand areas for improvement and improve their skills.

[0074] The talent management department can evaluate each user's skills and performance and propose appropriate personnel placement and career paths. The talent management department, for example, evaluates each user's skills and performance. For example, the talent management department evaluates a user's skills and performance based on performance evaluation data. The talent management department can also evaluate a user's skills and performance using a skill matrix. The talent management department also proposes appropriate personnel placement and career paths. For example, the talent management department proposes placement to an appropriate department based on the user's skills and performance. The talent management department can also propose a career path for the user. In this way, the talent management department can evaluate each user's skills and performance and propose appropriate personnel placement and career paths, allowing companies to maximize the capabilities of their employees and achieve efficient human resource management.

[0075] The talent management department can make proposals to companies to maximize the capabilities of their employees. The talent management department, for example, makes proposals to companies to maximize the capabilities of their employees. For example, the talent management department can propose training programs. The talent management department can also propose career development plans. The talent management department can also propose training programs to improve employee skills. In this way, by making proposals to companies to maximize the capabilities of their employees, companies can maximize the capabilities of their employees and improve business efficiency and productivity.

[0076] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions. For example, the analysis unit can estimate the user's emotions using facial expression recognition technology. The analysis unit can also estimate the user's emotions using voice analysis technology. The analysis unit can also estimate the user's emotions using text analysis technology. For example, the analysis unit analyzes the content of the user's speech, facial expressions, and tone of voice to estimate the user's emotions. The analysis unit also adjusts the accuracy of the analysis based on the estimated user's emotions. For example, the analysis unit can increase the accuracy of the analysis and provide more detailed feedback when the user is nervous. The analysis unit can also adjust the accuracy of the analysis and simplify the content of the feedback when the user is relaxed. The analysis unit can also adjust the accuracy of the analysis and visually emphasize the content of the feedback when the user is excited. In this way, the analysis unit can provide more appropriate feedback by adjusting the accuracy of the analysis based on the user's emotions.

[0077] During analysis, the analysis unit can optimize the analysis algorithm by referring to the user's past role-playing data. The analysis unit, for example, refers to the user's past role-playing data. For example, the analysis unit adjusts the analysis algorithm based on data of role-playing sessions performed by the user in the past. The analysis unit can also refer to the user's past role-playing data to learn specific patterns and improve analysis accuracy. The analysis unit can also optimize the analysis algorithm based on the user's past role-playing data and provide more accurate feedback. In this way, the analysis unit can optimize the analysis algorithm and provide more accurate feedback by referring to the user's past role-playing data.

[0078] During analysis, the analysis unit can customize the analysis criteria according to the user's job content and position. The analysis unit customizes the analysis criteria according to the user's job content and position, for example. For example, if the user is in sales, the analysis unit applies analysis criteria for sales talk. Furthermore, if the user is in a managerial position, the analysis unit can also apply analysis criteria for leadership skills. Furthermore, if the user is in a technical position, the analysis unit can also apply analysis criteria for technical knowledge and skills. In this way, the analysis unit can provide more appropriate feedback by customizing the analysis criteria according to the user's job content and position.

[0079] During analysis, the analysis unit can analyze not only the user's speech content, facial expression, and tone of voice, but also their gestures and posture. For example, the analysis unit analyzes the user's hand movements and posture in addition to the user's speech content. For example, the analysis unit analyzes the user's hand movements using motion analysis technology. The analysis unit can also analyze the user's posture using a posture recognition algorithm. The analysis unit can also analyze the user's facial expressions and eye movements along with the user's tone of voice. For example, the analysis unit analyzes the user's facial expressions using facial expression recognition technology. The analysis unit can also analyze the user's eye movements and track the user's gaze. The analysis unit can also analyze the user's gestures and posture to suggest areas for improvement in the presentation. In this way, the analysis unit can provide more detailed feedback by analyzing the user's gestures and posture in addition to the user's speech content, facial expression, and tone of voice.

[0080] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions. For example, the analysis unit can estimate the user's emotions using facial expression recognition technology. The analysis unit can also estimate the user's emotions using voice analysis technology. The analysis unit can also estimate the user's emotions using text analysis technology. For example, the analysis unit can analyze the content of the user's speech, facial expressions, and tone of voice to estimate the user's emotions. Furthermore, the analysis unit adjusts the display method of the analysis results based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can display the analysis results simply to reduce visual stress. If the user is relaxed, the analysis unit can display detailed analysis results to allow the user to understand them more deeply. If the user is excited, the analysis unit can visually emphasize the analysis results to provide a display method that attracts the user's interest. In this way, the analysis unit can adjust the display method of the analysis results based on the user's emotions, making it easier for the user to understand the analysis results.

[0081] During analysis, the analysis unit can adjust the analysis criteria taking into account the user's geographical location information. The analysis unit adjusts the analysis criteria, for example, taking into account the user's geographical location information. For example, when a user role-plays in different regions, the analysis unit adjusts the analysis criteria taking into account the culture and business etiquette specific to that region. Furthermore, when a user role-plays while on a business trip, the analysis unit can apply analysis criteria that reflect the characteristics of that region. Furthermore, when a user role-plays overseas, the analysis unit can adjust the analysis criteria taking into account international business etiquette. In this way, the analysis unit can adjust the analysis criteria taking into account the user's geographical location information, thereby enabling analysis that reflects the culture and business etiquette specific to that region.

[0082] During the analysis, the analysis unit can analyze the user's social media activities and reflect related data in the analysis. The analysis unit, for example, analyzes the user's social media activities. For example, the analysis unit analyzes the content of the user's comments on social media and reflects that data in the analysis. The analysis unit can also adjust the analysis criteria based on the user's social media activity history. The analysis unit can also analyze the reactions of the user's followers and friends on social media and reflect that data in the analysis. In this way, the analysis unit can provide more detailed feedback by analyzing the user's social media activities and reflecting related data in the analysis.

[0083] During analysis, the analysis unit can customize the analysis algorithm by reflecting the user's past feedback. The analysis unit, for example, reflects the user's past feedback. For example, the analysis unit adjusts the analysis algorithm based on feedback the user has received in the past. The analysis unit can also refer to the content of the user's past feedback and focus on specific areas for improvement when performing analysis. The analysis unit can also customize the analysis algorithm based on the user's past feedback history to provide more accurate feedback. In this way, the analysis unit can provide more accurate feedback by customizing the analysis algorithm by reflecting the user's past feedback.

[0084] The feedback unit can estimate the user's emotion and adjust the feedback expression method based on the estimated user's emotion. The feedback unit, for example, estimates the user's emotion. For example, the feedback unit can estimate the user's emotion using facial expression recognition technology. The feedback unit can also estimate the user's emotion using voice analysis technology. The feedback unit can also estimate the user's emotion using text analysis technology. For example, the feedback unit can analyze the content of the user's speech, facial expression, and tone of voice to estimate the user's emotion. Furthermore, the feedback unit adjusts the feedback expression method based on the estimated user's emotion. For example, the feedback unit can provide gentle feedback when the user is nervous. The feedback unit can also provide detailed feedback when the user is relaxed. The feedback unit can also provide visually emphasized feedback when the user is excited. In this way, the feedback unit can adjust the feedback expression method based on the user's emotion, making it easier for the user to understand the feedback.

[0085] When providing feedback, the feedback unit can provide optimal feedback by referring to the user's past feedback history. The feedback unit, for example, refers to the user's past feedback history. For example, the feedback unit provides optimal feedback based on feedback the user has received in the past. The feedback unit can also refer to the user's past feedback history and provide feedback that focuses on specific areas for improvement. The feedback unit can also provide customized feedback based on the content of the user's past feedback. In this way, the feedback unit can provide more appropriate feedback by referring to the user's past feedback history.

[0086] When providing feedback, the feedback unit can customize the content of the feedback according to the user's job content and position. The feedback unit customizes the content of the feedback according to, for example, the user's job content and position. For example, if the user is in sales, the feedback unit can provide feedback on sales talk. Also, if the user is in a managerial position, the feedback unit can provide feedback on leadership skills. Also, if the user is in a technical position, the feedback unit can provide feedback on technical knowledge and skills. In this way, the feedback unit can provide more appropriate feedback by customizing the content of the feedback according to the user's job content and position.

[0087] When providing feedback, the feedback unit can provide feedback on the user's gestures and posture in addition to the content of the user's speech, facial expression, and tone of voice. For example, the feedback unit provides feedback on the user's hand movements and posture in addition to the content of the user's speech. For example, the feedback unit can provide feedback on the user's hand movements using motion analysis technology. The feedback unit can also provide feedback on the user's posture using a posture recognition algorithm. The feedback unit can also provide feedback on the user's facial expressions and eye movements in addition to the tone of the user's voice. For example, the feedback unit can provide feedback on the user's facial expressions using facial expression recognition technology. The feedback unit can also analyze eye movements and provide feedback on the user's gaze. The feedback unit can also provide feedback on the user's gestures and posture and point out areas for improvement in the presentation. In this way, the feedback unit can provide more detailed feedback by providing feedback on the user's gestures and posture in addition to the content of the user's speech, facial expression, and tone of voice.

[0088] The feedback unit can estimate the user's emotion and adjust the length of the feedback based on the estimated user's emotion. The feedback unit, for example, estimates the user's emotion. For example, the feedback unit can estimate the user's emotion using facial expression recognition technology. The feedback unit can also estimate the user's emotion using voice analysis technology. The feedback unit can also estimate the user's emotion using text analysis technology. For example, the feedback unit can analyze the content of the user's speech, facial expression, and tone of voice to estimate the user's emotion. Furthermore, the feedback unit adjusts the length of the feedback based on the estimated user's emotion. For example, the feedback unit can provide short and to-the-point feedback when the user is nervous. The feedback unit can also provide detailed feedback when the user is relaxed. The feedback unit can also provide visually emphasized feedback when the user is excited. In this way, the feedback unit can adjust the length of the feedback based on the user's emotion, making it easier for the user to understand the feedback.

[0089] When providing feedback, the feedback unit can adjust the content of the feedback taking into account the user's geographical location information. The feedback unit adjusts the content of the feedback, for example, taking into account the user's geographical location information. For example, when a user role-plays in different regions, the feedback unit provides feedback taking into account local culture and business etiquette. Furthermore, when a user role-plays while on a business trip, the feedback unit can provide feedback that reflects the characteristics of the region. Furthermore, when a user role-plays overseas, the feedback unit can provide feedback taking into account international business etiquette. In this way, the feedback unit can adjust the content of the feedback taking into account the user's geographical location information, thereby enabling feedback that reflects local culture and business etiquette.

[0090] When providing feedback, the feedback unit can analyze the user's social media activity and provide relevant feedback. The feedback unit, for example, analyzes the user's social media activity. For example, the feedback unit analyzes the content of the user's social media posts and provides feedback based on that data. The feedback unit can also provide feedback that focuses on specific areas for improvement based on the user's social media activity history. The feedback unit can also analyze the reactions of the user's followers and friends on social media and reflect that data in the feedback. This allows the feedback unit to provide more detailed feedback by analyzing the user's social media activity and providing relevant feedback.

[0091] When providing feedback, the feedback unit can customize the content of the feedback by reflecting the user's past feedback. The feedback unit, for example, reflects the user's past feedback. For example, the feedback unit provides optimal feedback based on feedback the user has received in the past. The feedback unit can also refer to the user's past feedback history and provide feedback that emphasizes specific areas for improvement. The feedback unit can also provide customized feedback based on the content of the user's past feedback. In this way, the feedback unit can provide more appropriate feedback by customizing the content of the feedback by reflecting the user's past feedback.

[0092] The talent management unit can estimate a user's emotions and adjust the content of talent management suggestions based on the estimated user's emotions. The talent management unit, for example, estimates the user's emotions. For example, the talent management unit estimates the user's emotions using facial expression recognition technology. The talent management unit can also estimate the user's emotions using voice analysis technology. The talent management unit can also estimate the user's emotions using text analysis technology. For example, the talent management unit analyzes the user's speech content, facial expressions, and tone of voice to estimate the user's emotions. Furthermore, the talent management unit adjusts the content of talent management suggestions based on the estimated user's emotions. For example, if the user is nervous, the talent management unit can provide suggestions that help the user relax. If the user is relaxed, the talent management unit can also provide detailed career path suggestions. If the user is excited, the talent management unit can also provide visually emphasized suggestions. In this way, the talent management unit can provide more appropriate suggestions by adjusting the content of talent management suggestions based on the user's emotions.

[0093] When providing talent management, the talent management department can make optimal suggestions by referring to the user's past performance data. The talent management department, for example, refers to the user's past performance data. For example, the talent management department can suggest an optimal career path based on data from role-playing sessions the user has performed in the past. The talent management department can also refer to the user's past performance data to suggest training to improve specific skills. The talent management department can also suggest placement in an appropriate department based on the user's past performance data. In this way, the talent management department can provide more appropriate suggestions by referring to the user's past performance data.

[0094] When providing talent management, the talent management department can customize the proposal content according to the user's job content and position. The talent management department customizes the proposal content according to the user's job content and position, for example. For example, if the user is in sales, the talent management department can propose training to improve sales skills. Furthermore, if the user is in a managerial position, the talent management department can also propose training to improve leadership skills. Furthermore, if the user is in a technical position, the talent management department can also propose training to improve technical knowledge and skills. In this way, the talent management department can provide more appropriate proposals by customizing the proposal content according to the user's job content and position.

[0095] When providing talent management, the talent management unit can consider data regarding gestures and posture in addition to the content of the user's speech, facial expressions, and tone of voice. For example, the talent management unit considers data regarding hand movements and posture in addition to the content of the user's speech. For example, the talent management unit can consider data regarding the user's hand movements using motion analysis technology. The talent management unit can also consider data regarding the user's posture using a posture recognition algorithm. The talent management unit can also consider data regarding facial expressions and eye movements in addition to the user's tone of voice. For example, the talent management unit can consider data regarding the user's facial expressions using facial expression recognition technology. The talent management unit can also analyze eye movements and consider data regarding the user's gaze. The talent management unit can also consider data regarding the user's gestures and posture to make suggestions for improving presentation skills. In this way, the talent management unit can provide more detailed suggestions by considering data regarding gestures and posture in addition to the content of the user's speech, facial expressions, and tone of voice.

[0096] The talent management unit can estimate a user's emotions and determine talent management priorities based on the estimated user's emotions. The talent management unit estimates the user's emotions, for example. For example, the talent management unit estimates the user's emotions using facial expression recognition technology. The talent management unit can also estimate the user's emotions using voice analysis technology. The talent management unit can also estimate the user's emotions using text analysis technology. For example, the talent management unit analyzes the content of the user's statements, facial expressions, and tone of voice to estimate the user's emotions. Furthermore, the talent management unit determines talent management priorities based on the estimated user's emotions. For example, if the user is nervous, the talent management unit prioritizes suggestions that will help the user relax. If the user is relaxed, the talent management unit can also prioritize suggestions for detailed career paths. If the user is excited, the talent management unit can also prioritize suggestions that are visually emphasized. In this way, the talent management unit can provide more appropriate suggestions by determining talent management priorities based on the user's emotions.

[0097] When providing talent management services, the talent management department can adjust the proposal content taking into account the user's geographic location information. The talent management department, for example, adjusts the proposal content taking into account the user's geographic location information. For example, if the user works in a different region, the talent management department adjusts the proposal content taking into account the culture and business etiquette specific to that region. Furthermore, if the user works on a business trip, the talent management department can provide proposal content that reflects the characteristics of that region. Furthermore, if the user works overseas, the talent management department can adjust the proposal content taking into account international business etiquette. In this way, the talent management department can adjust the proposal content taking into account the user's geographic location information, thereby enabling proposals that reflect the culture and business etiquette specific to that region.

[0098] When providing talent management, the talent management unit can analyze the user's social media activities and make relevant suggestions. The talent management unit, for example, analyzes the user's social media activities. For example, the talent management unit analyzes the content of the user's comments on social media and makes suggestions based on that data. The talent management unit can also make suggestions for improving specific skills based on the user's social media activity history. The talent management unit can also analyze the reactions of the user's followers and friends on social media and reflect that data in its suggestions. This allows the talent management unit to provide more detailed suggestions by analyzing the user's social media activities and making relevant suggestions.

[0099] When providing talent management, the talent management department can customize the content of the proposal by reflecting the user's past feedback. The talent management department, for example, reflects the user's past feedback. For example, the talent management department makes optimal proposals based on feedback the user has received in the past. The talent management department can also refer to the user's past feedback history and make proposals that emphasize specific areas for improvement. The talent management department can also make customized proposals based on the user's past feedback. In this way, the talent management department can provide more appropriate proposals by customizing the content of the proposal by reflecting the user's past feedback.

[0100] The reception unit can estimate the user's emotion and adjust the timing of input reception based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion. For example, the reception unit can estimate the user's emotion using facial expression recognition technology. The reception unit can also estimate the user's emotion using voice analysis technology. The reception unit can also estimate the user's emotion using text analysis technology. For example, the reception unit analyzes the content of the user's speech, facial expression, and tone of voice to estimate the user's emotion. The reception unit also adjusts the timing of input reception based on the estimated user's emotion. For example, if the user is nervous, the reception unit delays the timing of input reception to provide the user with time to relax. The reception unit can also adjust the timing of input reception to smoothly accept input when the user is relaxed. The reception unit can also adjust the timing of input reception to calm the user's excitement when the user is excited. In this way, the reception unit adjusts the timing of input reception based on the user's emotion, allowing the user to enter input in a relaxed state.

[0101] The reception unit can select the optimal reception method by referring to the user's past input history when receiving the input. The reception unit, for example, refers to the user's past input history. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also select the optimal reception method based on the user's past input history. The reception unit can also predict and suggest an input method to be used in a specific time period from the user's past input history. In this way, the reception unit can provide a more appropriate reception method by referring to the user's past input history.

[0102] The reception unit can customize the input reception criteria according to the user's job content and job title when receiving the input. The reception unit customizes the input reception criteria according to the user's job content and job title, for example. For example, if the user is in sales, the reception unit applies input reception criteria related to sales talk. Furthermore, if the user is in a managerial position, the reception unit can also apply input reception criteria related to leadership skills. Furthermore, if the user is in a technical position, the reception unit can also apply input reception criteria related to technical knowledge and skills. In this way, the reception unit can provide a more appropriate reception method by customizing the input reception criteria according to the user's job content and job title.

[0103] The reception unit can select the optimal reception means depending on the user's input method when receiving the input. The reception unit selects the optimal reception means depending on the user's input method, for example. For example, when the user uses voice input, the reception unit can accept the input using voice recognition technology. Furthermore, when the user uses text input, the reception unit can also accept the input using text analysis technology. Furthermore, when the user uses image input, the reception unit can also accept the input using image recognition technology. In this way, the reception unit can provide a more appropriate reception method by selecting the optimal reception means depending on the user's input method.

[0104] The reception unit can estimate the user's emotion and determine the priority of input reception based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion. For example, the reception unit can estimate the user's emotion using facial expression recognition technology. The reception unit can also estimate the user's emotion using voice analysis technology. The reception unit can also estimate the user's emotion using text analysis technology. For example, the reception unit can analyze the content of the user's speech, facial expression, and tone of voice to estimate the user's emotion. Furthermore, the reception unit determines the priority of input reception based on the estimated user's emotion. For example, if the user is nervous, the reception unit can prioritize accepting input that will help the user relax. Furthermore, if the user is relaxed, the reception unit can prioritize accepting detailed input. Furthermore, if the user is excited, the reception unit can prioritize accepting visually emphasized input. In this way, the reception unit can prioritize accepting input reception based on the user's emotion, allowing the user to enter input in a relaxed state.

[0105] The reception unit can prioritize receiving highly relevant inputs in consideration of the user's geographical location information when receiving the inputs. The reception unit, for example, prioritizes receiving highly relevant inputs in consideration of the user's geographical location information. For example, when a user makes an input in a different region, the reception unit prioritizes receiving information specific to the region. Furthermore, when a user makes an input while on a business trip, the reception unit can also prioritize receiving inputs that reflect the characteristics of the region. Furthermore, when a user makes an input while overseas, the reception unit can also prioritize receiving international information. In this way, the reception unit prioritizes receiving highly relevant inputs in consideration of the user's geographical location information, thereby enabling reception that reflects region-specific information.

[0106] The reception unit can analyze the user's social media activity and receive related inputs at the time of reception. The reception unit, for example, analyzes the user's social media activity. For example, the reception unit analyzes the content of the user's comments on social media and receives inputs based on that data. The reception unit can also preferentially receive specific inputs based on the user's social media activity history. The reception unit can also analyze the reactions of the user's followers and friends on social media and reflect that data in the inputs. In this way, the reception unit can analyze the user's social media activity and receive related inputs, enabling more detailed reception.

[0107] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a call. The reception unit, for example, reflects the user's past feedback. For example, the reception unit provides the optimal reception method based on feedback the user has received in the past. The reception unit can also refer to the user's past feedback history and customize the reception method by focusing on specific areas for improvement. The reception unit can also provide a customized reception method based on the content of the user's past feedback. In this way, the reception unit can provide a more appropriate reception method by customizing the reception method by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the analysis unit, feedback unit, talent management unit, and reception unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit acquires the user's voice and image using the camera 42 and microphone 38B of the smart device 14, and the acquired voice and image are analyzed by the specific processing unit 290 of the data processing device 12. The feedback unit provides real-time feedback based on the analysis results and presents the feedback to the user via the output device 40 of the smart device 14. The talent management unit, realized by the specific processing unit 290 of the data processing device 12, evaluates the user's skills and performance and proposes appropriate personnel placement and career paths to companies. The reception unit accepts user input using the reception device 38 of the smart device 14, and the input is analyzed by the specific processing unit 290 of the data processing device 12. Furthermore, the reception unit estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned analysis unit, feedback unit, talent management unit, and reception unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit acquires the user's voice and image using the camera 42 and microphone 238 of the smart glasses 214, which are analyzed by the specific processing unit 290 of the data processing device 12. The feedback unit provides real-time feedback based on the analysis results and presents it to the user through the speaker 240 of the smart glasses 214. The talent management unit, realized by the specific processing unit 290 of the data processing device 12, evaluates the user's skills and performance and proposes appropriate personnel placement and career paths to companies. The reception unit accepts user input using the microphone 238 of the smart glasses 214, which is analyzed by the specific processing unit 290 of the data processing device 12. Furthermore, the reception unit estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned analysis unit, feedback unit, talent management unit, and reception unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the analysis unit acquires the user's voice and image using the camera 42 and microphone 238 of the headset-type terminal 314, and the acquired voice and image are analyzed by the specific processing unit 290 of the data processing device 12. The feedback unit provides real-time feedback based on the analysis results and presents the feedback to the user through the speaker 240 of the headset-type terminal 314. The talent management unit is realized by the specific processing unit 290 of the data processing device 12, and evaluates the user's skills and performance and proposes appropriate personnel placement and career paths to companies. The reception unit receives user input using the microphone 238 of the headset-type terminal 314, and the input is analyzed by the specific processing unit 290 of the data processing device 12. Furthermore, the reception unit estimates the user's emotions and adjusts the timing of receiving the input based on the estimated emotions. === Hard Collateral 1-4 === Each of the multiple elements, including the analysis unit, feedback unit, talent management unit, and reception unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit acquires the user's voice and image using the camera 42 and microphone 238 of the robot 414, and the acquired voice and image are analyzed by the specific processing unit 290 of the data processing device 12. The feedback unit provides real-time feedback based on the analysis results and presents the feedback to the user through the speaker 240 of the robot 414. The talent management unit, realized by the specific processing unit 290 of the data processing device 12, evaluates the user's skills and performance and proposes appropriate personnel placement and career paths to companies. The reception unit receives user input using the microphone 238 of the robot 414, and the input is analyzed by the specific processing unit 290 of the data processing device 12. Furthermore, the reception unit estimates the user's emotions and adjusts the timing of receiving the input based on the estimated emotions.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] When analyzing a user's voice or image, the analysis unit can also optimize the analysis algorithm by referring to the user's past feedback history. For example, the analysis unit can adjust the analysis algorithm based on feedback the user has received in the past to perform more accurate analysis. The analysis unit can also refer to the content of the user's past feedback and focus on specific areas for improvement in the analysis. Furthermore, the analysis unit can customize the analysis algorithm based on the user's past feedback history and provide more detailed feedback. In this way, the analysis unit can provide more accurate feedback by optimizing the analysis algorithm by reflecting the user's past feedback.

[0110] When accepting input from a user, the acceptance unit can select the optimal acceptance method by referring to the user's past input history. For example, the acceptance unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The acceptance unit can also select the optimal acceptance method based on the user's past input history. Furthermore, the acceptance unit can predict and suggest an input method to be used in a specific time period from the user's past input history. In this way, the acceptance unit can provide a more appropriate acceptance method by referring to the user's past input history.

[0111] When analyzing a user's voice or image, the analysis unit can adjust the analysis criteria taking into account the user's geographical location information. For example, when a user role-plays in different regions, the analysis unit adjusts the analysis criteria taking into account the culture and business etiquette specific to that region. In addition, when a user role-plays while on a business trip, the analysis unit can apply analysis criteria that reflect the characteristics of that region. Furthermore, when a user role-plays overseas, the analysis unit can adjust the analysis criteria taking into account international business etiquette. In this way, the analysis unit can adjust the analysis criteria taking into account the user's geographical location information, thereby enabling analysis that reflects the culture and business etiquette specific to that region.

[0112] When providing instant feedback based on the analysis result, the feedback unit can estimate the user's emotions and adjust the way the feedback is expressed based on the estimated user's emotions. For example, if the user is nervous, the feedback unit can provide feedback in gentle words. If the user is relaxed, the feedback unit can also provide detailed feedback. Furthermore, if the user is excited, the feedback unit can also provide visually emphasized feedback. In this way, the feedback unit can adjust the way the feedback is expressed based on the user's emotions, making it easier for the user to understand the feedback.

[0113] When evaluating the skills and performance of each user, the talent management unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user's emotions. For example, if the user is nervous, the talent management unit can relax the evaluation criteria to allow the user to relax. The talent management unit can also provide a more detailed evaluation when the user is relaxed. Furthermore, if the user is excited, the talent management unit can provide a more visually emphasized evaluation. In this way, the talent management unit can provide a more appropriate evaluation by adjusting the evaluation criteria based on the user's emotions.

[0114] When analyzing a user's voice and images, the analysis unit can analyze the user's social media activities and reflect related data in the analysis. For example, the analysis unit can analyze the content of the user's social media posts and reflect that data in the analysis. The analysis unit can also adjust the analysis criteria based on the user's social media activity history. Furthermore, the analysis unit can analyze the reactions of the user's followers and friends on social media and reflect that data in the analysis. This allows the analysis unit to provide more detailed feedback by analyzing the user's social media activities and reflecting related data in the analysis.

[0115] When providing instant feedback based on the analysis results, the feedback unit can customize the content of the feedback by referring to the user's past feedback history. For example, the feedback unit can provide optimal feedback based on feedback the user has received in the past. The feedback unit can also provide feedback that focuses on specific areas for improvement by referring to the user's past feedback history. Furthermore, the feedback unit can provide customized feedback based on the content of the user's past feedback. In this way, the feedback unit can provide more appropriate feedback by customizing the content of the feedback to reflect the user's past feedback.

[0116] When evaluating each user's skills and performance, the talent management department can adjust the evaluation criteria taking into account the user's geographic location information. For example, if a user works in a different region, the talent management department can adjust the evaluation criteria taking into account the culture and business etiquette specific to that region. In addition, if a user works while on a business trip, the talent management department can apply evaluation criteria that reflect the characteristics of that region. Furthermore, if a user works overseas, the talent management department can adjust the evaluation criteria taking into account international business etiquette. In this way, the talent management department can adjust the evaluation criteria taking into account the user's geographic location information, enabling evaluation that reflects the culture and business etiquette specific to that region.

[0117] When analyzing the user's voice and images, the analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can display the analysis results simply to reduce visual stress. If the user is relaxed, the analysis unit can display detailed analysis results to allow the user to understand them more deeply. Furthermore, if the user is excited, the analysis unit can visually emphasize the analysis results and provide a display method that attracts the user's interest. In this way, the analysis unit can adjust the display method of the analysis results based on the user's emotions, making it easier for the user to understand the analysis results.

[0118] When evaluating each user's skills and performance, the talent management department can analyze the user's social media activities and reflect the related data in the evaluation. For example, the talent management department can analyze the content of the user's social media posts and reflect the data in the evaluation. The talent management department can also adjust the evaluation criteria based on the user's social media activity history. Furthermore, the talent management department can analyze the reactions of the user's followers and friends on social media and reflect the data in the evaluation. This allows the talent management department to provide a more detailed evaluation by analyzing the user's social media activities and reflecting the related data in the evaluation.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The analysis unit analyzes the user's voice and image. For example, the analysis unit uses voice analysis technology to analyze the content of the user's speech and image analysis technology to analyze the user's facial expressions. It can also analyze the tone of the voice to estimate the user's emotions. Specifically, it analyzes the pitch and speed of the voice to estimate emotions. Step 2: The feedback unit provides feedback based on the results of the analysis by the analysis unit. For example, it can provide text feedback or voice feedback based on the analysis results, and can also provide feedback in real time. Specifically, it provides real-time feedback based on the content of the user's remarks and facial expressions. Step 3: The Talent Management Department provides talent management functions based on the data obtained by the Feedback Department. For example, it evaluates each user's skills and performance and proposes appropriate personnel placement and career paths. It can also make suggestions to companies on how to maximize the capabilities of their employees.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] [Explanation of symbols]

[0193] 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. an analysis unit that analyzes the user's voice and image; a feedback unit that provides feedback based on the results of the analysis by the analysis unit; a talent management unit that provides a talent management function based on the data obtained by the feedback unit. A system characterized by:

2. A reception unit that receives user input is provided.

2. The system of claim 1.

3. The analysis unit Analyzes the user's voice and image to evaluate what is said, facial expressions, and tone of voice 2. The system of claim 1.

4. The feedback unit Provides immediate feedback based on analysis results 2. The system of claim 1.

5. The talent management department: Evaluate each user's skills and performance to suggest appropriate personnel placement and career paths 2. The system of claim 1.

6. The talent management department: Providing suggestions to companies on how to maximize the capabilities of their employees 2. The system of claim 1.

7. The analysis unit Estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions.

2. The system of claim 1.

8. The analysis unit During analysis, the analysis algorithm is optimized by referencing the user's past role-playing data.

2. The system of claim 1.

9. The analysis unit Customize analysis criteria according to the user's job duties and position during analysis 2. The system of claim 1.

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