system

Abstract

The system according to the embodiment aims to select an appropriate corrector and enable presentation practice that is close to the real thing. [Solution] The system according to the embodiment includes a collection unit, an analysis unit, a matching unit, and a practice unit. The collection unit collects data on employees' job titles, areas of expertise, and past presentation experience. The analysis unit selects a corrector based on the data collected by the collection unit. The matching unit matches the corrector selected by the analysis unit with the presentation subject. The practice unit practices the presentation with the corrector matched by the matching unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there was a problem that when practicing presentations, there was a gap between the attributes of the actual audience and the corrector, making it difficult to obtain effective feedback.

[0005] The system according to the embodiment aims to select an appropriate corrector and enable presentation practice that is close to the real thing. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a matching unit, and a practice unit. The collection unit collects data on employees' job titles, areas of expertise, and past presentation experience. The analysis unit selects a corrector based on the data collected by the collection unit. The matching unit matches the corrector selected by the analysis unit with the presentation subject. The practice unit practices the presentation with the corrector matched by the matching unit. [Effects of the Invention]

[0007] The system according to the embodiment can select an appropriate corrector and provide a presentation practice that is close to the real thing. [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 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 presentation practice support system according to an embodiment of the present invention matches in-house correctors with the attribute data of the presentation recipients, enabling a presentation practice that closely resembles the real thing. This presentation practice support system collects and analyzes data on in-house employees and the attribute data of the presentation recipients, and matches them with an appropriate in-house corrector. This allows presentation practice with minimal discrepancies between the attributes of the actual audience and the corrector. This improves the quality of presentations and increases the success rate of winning projects. For example, the presentation practice support system first collects data on in-house employees and the attribute data of the presentation recipients. Detailed data, such as employees' job titles, fields of expertise, and past presentation experience, is collected. For example, data on employees from different departments, such as sales and engineering departments, is collected. Attribute data on the presentation recipients, such as the audience's job titles, fields of expertise, and interests, is also collected. This allows for an understanding of the attributes of the presentation recipients. Next, the presentation practice support system analyzes the collected data and matches them with an appropriate in-house corrector. For example, if the presentation recipient is an employee from the engineering department, an appropriate corrector is selected based on the employee data from the engineering department. Similarly, if the presentation recipient is an employee from the sales department, an appropriate corrector is selected based on the employee data from the sales department. This allows presentation practice with minimal gap in the attributes of the actual audience and the corrector. Furthermore, the presentation practice support system allows the presentation to be practiced with the matched corrector. For example, if the presentation is aimed at employees in the engineering department, the presentation will be practiced with a corrector from the engineering department. This allows the presentation to receive feedback similar to that of an actual audience, improving the quality of the presentation. Furthermore, based on the results of the presentation practice, areas for improvement in the presentation content can be identified and revised. This increases the completeness of the presentation and the success rate of acquiring projects. This system matches in-house correctors with the presentation targets based on their attribute data, allowing for a presentation practice that is closer to the real thing. This improves the quality of the presentation and increases the success rate of acquiring projects. This allows the presentation practice support system to improve the quality of presentations and increase the success rate of acquiring projects.

[0029] A presentation practice support system according to an embodiment includes a collection unit, an analysis unit, a matching unit, and a practice unit. The collection unit collects data on employees' job titles, fields of expertise, and past presentation experience. Employee job titles include, for example, manager, engineer, and sales representative. Employee fields of expertise include, for example, IT, marketing, and finance. Past presentation experience includes, for example, the number of presentations, success rate, and feedback. The collection unit, for example, collects data based on employees' job titles and fields of expertise. The collection unit can also collect data based on past presentation experience. For example, the collection unit can collect data based on employees' job titles and fields of expertise, and collect data based on past presentation experience. The analysis unit selects a corrector based on the data collected by the collection unit. For example, the analysis unit selects a corrector based on employees' job titles and fields of expertise. The analysis unit can also select a corrector based on past presentation experience. For example, the analysis unit can select a corrector based on employees' job titles and fields of expertise, and select a corrector based on past presentation experience. The matching unit matches the corrector selected by the analysis unit with the presentation subject. The matching unit performs matching based on, for example, the employee's job title or field of expertise. The matching unit can also perform matching based on past presentation experience. For example, the matching unit can perform matching based on the employee's job title or field of expertise, and then perform matching based on past presentation experience. The practice unit practices the presentation with the corrector matched by the matching unit. The practice unit performs presentation practice based on, for example, the employee's job title or field of expertise. The practice unit can also perform presentation practice based on past presentation experience. For example, the practice unit can perform presentation practice based on the employee's job title or field of expertise, and then perform presentation practice based on past presentation experience. As a result, the presentation practice support system according to the embodiment selects an appropriate corrector based on data such as the employee's job title, field of expertise, and past presentation experience, and then performs presentation practice, thereby improving the quality of the presentation and increasing the success rate of winning projects.

[0030] The collection unit can analyze employees' past presentation success rates and determine the priority of data to be collected. For example, the collection unit prioritizes collecting data from employees with a high past success rate. The collection unit can also postpone data from employees with a low success rate and treat it as less important data. Furthermore, the collection unit can analyze commonalities between presentations with a high success rate and prioritize collecting data containing those elements. This allows important data to be collected preferentially by determining the priority of data based on past presentation success rates. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input employees' past presentation data into a generation AI and have the generation AI perform an analysis of the success rate.

[0031] When collecting data, the collection unit can adjust the collection method based on the employee's current project and workload. For example, the collection unit avoids collecting data from employees with high workloads and prioritizes collecting data from employees with low workloads. The collection unit can also collect data at an appropriate time depending on the progress of the project. Furthermore, when the workload is high, the collection unit can adopt a simple data collection method to reduce the burden. This enables efficient data collection by adjusting the data collection method in consideration of the employee's workload. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input employee workload data into the generation AI and have the generation AI adjust the collection method.

[0032] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of employees. For example, the collection unit can prioritize collecting data from geographically nearby employees to enable a prompt response. The collection unit can also postpone collecting data from geographically distant employees to enable efficient data collection. Furthermore, the collection unit can prioritize collecting data related to a specific region based on the geographical location information. This enables efficient data collection by collecting data while taking geographical location information into account. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location data of employees into the generation AI and have the generation AI select highly relevant data.

[0033] During data collection, the collection unit can analyze employees' social media activities and collect relevant data. For example, the collection unit can collect data related to areas of interest from employees' social media activities. The collection unit can also analyze comments and posts on social media to prioritize the collection of relevant data. Furthermore, the collection unit can collect data from influential employees based on their social media followers and friendships. In this way, by analyzing social media activities and collecting data, highly relevant data can be efficiently collected. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input employees' social media data into a generation AI and have the generation AI collect relevant data.

[0034] During analysis, the analysis unit can optimize the analysis algorithm by referring to past presentation practice results. The analysis unit, for example, optimizes the analysis algorithm based on the results of past successful presentation practice. The analysis unit can also analyze the results of past unsuccessful presentation practice and build an analysis algorithm that reflects areas for improvement. Furthermore, the analysis unit can extract common points from past presentation practice results and reflect them in the analysis algorithm. In this way, the accuracy of the analysis is improved by optimizing the analysis algorithm by referring to the results of past presentation practice. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past presentation practice data into a generation AI and have the generation AI optimize the analysis algorithm.

[0035] During analysis, the analysis unit can apply different analysis methods depending on the employee's position and field of expertise. For example, the analysis unit can apply technical analysis methods to employees in the engineering department and provide specialized feedback. The analysis unit can also apply sales-specific analysis methods to employees in the sales department and provide practical feedback. Furthermore, the analysis unit can apply management-related analysis methods to managerial employees and provide strategic feedback. In this way, appropriate feedback can be provided by applying analysis methods depending on the employee's position and field of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input employee position and field of expertise data into the generation AI and have the generation AI apply the analysis method.

[0036] During analysis, the analysis unit can determine the analysis priority based on the employee's submission time. For example, the analysis unit prioritizes analysis of data from employees whose submission deadlines are approaching and provides results quickly. The analysis unit can also postpone data from employees whose submission deadlines are further away, allowing for more efficient analysis. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the submission time. This enables efficient analysis by determining the analysis priority based on the submission time. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input employee submission time data into the generation AI and have the generation AI determine the analysis priority.

[0037] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the employee's related literature. The analysis unit can improve the accuracy of the analysis by, for example, referring to related literature written by the employee. The analysis unit can also improve the accuracy of the analysis based on literature cited by the employee. Furthermore, the analysis unit can improve the accuracy of the analysis based on literature previously referenced by the employee. In this way, by improving the accuracy of the analysis by referring to related literature, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the employee's related literature data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0038] The matching unit can improve the accuracy of matching by taking into account the interrelationships between employees. For example, the matching unit selects a compatible corrector based on past cooperative relationships between employees. The matching unit can also avoid past conflicts between employees and perform smooth matching. Furthermore, the matching unit can select an appropriate corrector based on the employees' common project experience. This enables smooth matching by performing matching while taking into account the interrelationships between employees. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input employee interrelationship data into the generation AI and cause the generation AI to improve the accuracy of matching.

[0039] The matching unit can perform matching while taking employee attribute information into consideration. The matching unit selects an appropriate corrector based on, for example, the employee's job title or field of expertise. The matching unit can also select the most suitable corrector based on the employee's past presentation experience. Furthermore, the matching unit can also select an appropriate corrector based on the employee's interests and areas of expertise. In this way, by performing matching while taking employee attribute information into consideration, an appropriate corrector can be selected. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input employee attribute information data into the generation AI and have the generation AI perform matching.

[0040] The matching unit can perform matching taking into account the geographical distribution of employees. For example, the matching unit can prioritize matching between geographically close employees to ensure a quick response. The matching unit can also perform efficient matching by avoiding matching between geographically distant employees. Furthermore, the matching unit can match employees related to a specific region based on geographical distribution. This enables efficient matching by performing matching taking geographical distribution into consideration. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input employee geographical distribution data into the generation AI and have the generation AI perform matching.

[0041] The matching unit can improve the accuracy of matching by referring to the employee's related literature during matching. The matching unit can improve the accuracy of matching by referring to, for example, related literature written by the employee. The matching unit can also improve the accuracy of matching based on literature cited by the employee. Furthermore, the matching unit can improve the accuracy of matching based on literature referenced by the employee in the past. In this way, by improving the accuracy of matching by referring to related literature, an appropriate editor can be selected. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the employee's related literature data into the generation AI and have the generation AI improve the accuracy of matching.

[0042] When practicing a presentation, the practice unit can optimize the practice method by referring to past practice results. For example, the practice unit provides an optimal practice method based on past successful presentation practice results. The practice unit can also analyze past unsuccessful presentation practice results and provide a practice method that reflects areas for improvement. Furthermore, the practice unit can extract common points from past presentation practice results and provide an optimal practice method. In this way, the effectiveness of practice is improved by optimizing the practice method by referring to past practice results. Some or all of the above-mentioned processing in the practice unit may be performed using, for example, AI, or may be performed without using AI. For example, the practice unit can input past practice data into a generation AI and have the generation AI optimize the practice method.

[0043] The practice department can customize the practice method based on the employee's current work situation when practicing a presentation. For example, the practice department can provide a short and effective practice method for employees with a high workload. The practice department can also provide a practice method that includes detailed feedback for employees with a low workload. Furthermore, the practice department can practice at an appropriate time depending on the progress of the project. This allows for efficient practice by customizing the practice method according to the employee's work situation. Some or all of the above-mentioned processing in the practice department may be performed using, for example, AI, or may be performed without using AI. For example, the practice department can input employee work situation data into the generation AI and have the generation AI customize the practice method.

[0044] When practicing a presentation, the practice department can select the optimal practice method by taking into account the employees' geographic location information. For example, the practice department can prioritize practice between geographically close employees to ensure quick feedback. The practice department can also avoid practice between geographically distant employees to ensure efficient practice. Furthermore, the practice department can select a practice method related to a specific region based on the geographic location information. This enables efficient practice by selecting a practice method by taking geographic location information into account. Some or all of the above-mentioned processing in the practice department may be performed using, for example, AI, or may be performed without using AI. For example, the practice department can input the employees' geographic location data into the generation AI and have the generation AI select a practice method.

[0045] When practicing a presentation, the practice department can analyze employees' social media activity and suggest ways to practice. For example, the practice department can suggest practice methods related to the employee's areas of interest based on the employee's social media activity. The practice department can also analyze comments and posts on social media to suggest related practice methods. Furthermore, the practice department can suggest feedback from influential employees based on social media followers and friendships. This allows for highly relevant practice by analyzing social media activity and suggesting ways to practice. Some or all of the above-mentioned processing in the practice department may be performed using, for example, AI, or may be performed without using AI. For example, the practice department can input employees' social media data into a generation AI and have the generation AI execute suggestions for practice methods.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The data collection department can monitor employees' health status and adjust the frequency and method of data collection based on their health condition. For example, if an employee feels fatigued, the frequency of data collection can be reduced to reduce the burden. Also, if an employee is healthy, detailed data collection can be performed to obtain more information. Furthermore, if an employee is ill, only important data can be collected as a priority and processed quickly. This allows the timing and method of data collection to be adjusted according to the employee's health status, reducing the burden on employees and enabling efficient data collection.

[0048] The collection department can customize the content of data collection taking into account the hobbies and interests of employees. For example, if an employee is interested in sports, data related to sports can be collected first. If an employee is interested in music, data related to music can be collected. Furthermore, if an employee is interested in travel, data related to travel can be collected. In this way, by customizing the content of data collection according to the hobbies and interests of employees, more relevant data can be collected.

[0049] The data collection department can analyze employees' communication styles and select the appropriate data collection method. For example, if employees prefer verbal communication, data can be collected in the form of an interview. If employees prefer written communication, data can be collected in the form of a questionnaire. Furthermore, if employees prefer digital tools, data can be collected using online forms or chatbots. This allows for efficient data collection by selecting a data collection method according to employees' communication styles.

[0050] The data collection department can determine the priority of data collection taking into account the career goals of employees. For example, priority can be given to collecting data on employees who are aiming for promotion. Data on employees who are aiming to improve their professional skills can also be collected preferentially. Data on employees who wish to be transferred can also be collected preferentially. In this way, by determining the priority of data collection according to the career goals of employees, important data can be collected preferentially.

[0051] The collection department can adjust the timing of data collection taking into account employees' working hours. For example, data collection from night-shift employees can be done at night, and data collection from day-shift employees can be done during the day. Furthermore, data collection from employees on a flextime system can be flexibly adjusted. This allows for efficient data collection by adjusting the timing of data collection according to employees' working hours.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The data collection department collects data on employees' job titles, areas of expertise, and past presentation experience. Employee job titles include, for example, manager, engineer, sales representative, etc., and areas of expertise include, for example, IT, marketing, finance, etc. Past presentation experience includes the number of presentations, success rate, feedback, etc. Step 2: The analysis department selects correctors based on the data collected by the collection department. The analysis department selects correctors based on the employee's position, area of ​​expertise, and past presentation experience. Step 3: The matching department matches the correctors selected by the analysis department with the presentation subjects. The matching department matches based on the employee's position, area of ​​expertise, and past presentation experience. Step 4: The Practice Department will practice presentations with the correctors matched by the Matching Department. The Practice Department will practice presentations based on the employee's position, area of ​​expertise, and past presentation experience.

[0054] (Example 2) A presentation practice support system according to an embodiment of the present invention matches in-house correctors with the attribute data of the presentation recipients, enabling a presentation practice that closely resembles the real thing. This presentation practice support system collects and analyzes data on in-house employees and the attribute data of the presentation recipients, and matches them with an appropriate in-house corrector. This allows presentation practice with minimal discrepancies between the attributes of the actual audience and the corrector. This improves the quality of presentations and increases the success rate of winning projects. For example, the presentation practice support system first collects data on in-house employees and the attribute data of the presentation recipients. Detailed data, such as employees' job titles, fields of expertise, and past presentation experience, is collected. For example, data on employees from different departments, such as sales and engineering departments, is collected. Attribute data on the presentation recipients, such as the audience's job titles, fields of expertise, and interests, is also collected. This allows for an understanding of the attributes of the presentation recipients. Next, the presentation practice support system analyzes the collected data and matches them with an appropriate in-house corrector. For example, if the presentation recipient is an employee from the engineering department, an appropriate corrector is selected based on the employee data from the engineering department. Similarly, if the presentation recipient is an employee from the sales department, an appropriate corrector is selected based on the employee data from the sales department. This allows presentation practice with minimal gap in the attributes of the actual audience and the corrector. Furthermore, the presentation practice support system allows the presentation to be practiced with the matched corrector. For example, if the presentation is aimed at employees in the engineering department, the presentation will be practiced with a corrector from the engineering department. This allows the presentation to receive feedback similar to that of an actual audience, improving the quality of the presentation. Furthermore, based on the results of the presentation practice, areas for improvement in the presentation content can be identified and revised. This increases the completeness of the presentation and the success rate of acquiring projects. This system matches in-house correctors with the presentation targets based on their attribute data, allowing for a presentation practice that is closer to the real thing. This improves the quality of the presentation and increases the success rate of acquiring projects. This allows the presentation practice support system to improve the quality of presentations and increase the success rate of acquiring projects.

[0055] A presentation practice support system according to an embodiment includes a collection unit, an analysis unit, a matching unit, and a practice unit. The collection unit collects data on employees' job titles, fields of expertise, and past presentation experience. Employee job titles include, for example, manager, engineer, and sales representative. Employee fields of expertise include, for example, IT, marketing, and finance. Past presentation experience includes, for example, the number of presentations, success rate, and feedback. The collection unit, for example, collects data based on employees' job titles and fields of expertise. The collection unit can also collect data based on past presentation experience. For example, the collection unit can collect data based on employees' job titles and fields of expertise, and collect data based on past presentation experience. The analysis unit selects a corrector based on the data collected by the collection unit. For example, the analysis unit selects a corrector based on employees' job titles and fields of expertise. The analysis unit can also select a corrector based on past presentation experience. For example, the analysis unit can select a corrector based on employees' job titles and fields of expertise, and select a corrector based on past presentation experience. The matching unit matches the corrector selected by the analysis unit with the presentation subject. The matching unit performs matching based on, for example, the employee's job title or field of expertise. The matching unit can also perform matching based on past presentation experience. For example, the matching unit can perform matching based on the employee's job title or field of expertise, and then perform matching based on past presentation experience. The practice unit practices the presentation with the corrector matched by the matching unit. The practice unit performs presentation practice based on, for example, the employee's job title or field of expertise. The practice unit can also perform presentation practice based on past presentation experience. For example, the practice unit can perform presentation practice based on the employee's job title or field of expertise, and then perform presentation practice based on past presentation experience. As a result, the presentation practice support system according to the embodiment selects an appropriate corrector based on data such as the employee's job title, field of expertise, and past presentation experience, and then performs presentation practice, thereby improving the quality of the presentation and increasing the success rate of winning projects.

[0056] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the burden on the user. Furthermore, if the user is relaxed, the collection unit can collect detailed data to obtain more information. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting only important data and process it quickly. This reduces the burden on the user by adjusting the timing of data collection according to the user's emotions, enabling efficient data collection. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0057] The collection unit can analyze employees' past presentation success rates and determine the priority of data to be collected. For example, the collection unit prioritizes collecting data from employees with a high past success rate. The collection unit can also postpone data from employees with a low success rate and treat it as less important data. Furthermore, the collection unit can analyze commonalities between presentations with a high success rate and prioritize collecting data containing those elements. This allows important data to be collected preferentially by determining the priority of data based on past presentation success rates. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input employees' past presentation data into a generation AI and have the generation AI perform an analysis of the success rate.

[0058] When collecting data, the collection unit can adjust the collection method based on the employee's current project and workload. For example, the collection unit avoids collecting data from employees with high workloads and prioritizes collecting data from employees with low workloads. The collection unit can also collect data at an appropriate time depending on the progress of the project. Furthermore, when the workload is high, the collection unit can adopt a simple data collection method to reduce the burden. This enables efficient data collection by adjusting the data collection method in consideration of the employee's workload. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input employee workload data into the generation AI and have the generation AI adjust the collection method.

[0059] The collection unit can estimate the user's emotions and select the type of data to collect based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can collect data using simple questions or a short questionnaire. Alternatively, if the user is relaxed, the collection unit can collect data using a detailed interview or a long questionnaire. Furthermore, if the user is in a hurry, the collection unit can select only important data and collect it quickly. This enables efficient data collection by selecting the type of data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0060] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of employees. For example, the collection unit can prioritize collecting data from geographically nearby employees to enable a prompt response. The collection unit can also postpone collecting data from geographically distant employees to enable efficient data collection. Furthermore, the collection unit can prioritize collecting data related to a specific region based on the geographical location information. This enables efficient data collection by collecting data while taking geographical location information into account. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location data of employees into the generation AI and have the generation AI select highly relevant data.

[0061] During data collection, the collection unit can analyze employees' social media activities and collect relevant data. For example, the collection unit can collect data related to areas of interest from employees' social media activities. The collection unit can also analyze comments and posts on social media to prioritize the collection of relevant data. Furthermore, the collection unit can collect data from influential employees based on their social media followers and friendships. In this way, by analyzing social media activities and collecting data, highly relevant data can be efficiently collected. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input employees' social media data into a generation AI and have the generation AI collect relevant data.

[0062] The analysis unit can estimate the user's emotions and adjust the parameters of the analysis algorithm based on the estimated user emotions. For example, when the user is relaxed, the analysis unit performs a detailed analysis and provides highly accurate results. Furthermore, when the user is in a hurry, the analysis unit can perform a simplified analysis and provide quick results. Furthermore, when the user is stressed, the analysis unit can reduce the analysis load and provide quick results. This enables efficient analysis by adjusting the parameters of the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the parameters of the analysis algorithm.

[0063] During analysis, the analysis unit can optimize the analysis algorithm by referring to past presentation practice results. The analysis unit, for example, optimizes the analysis algorithm based on the results of past successful presentation practice. The analysis unit can also analyze the results of past unsuccessful presentation practice and build an analysis algorithm that reflects areas for improvement. Furthermore, the analysis unit can extract common points from past presentation practice results and reflect them in the analysis algorithm. In this way, the accuracy of the analysis is improved by optimizing the analysis algorithm by referring to the results of past presentation practice. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past presentation practice data into a generation AI and have the generation AI optimize the analysis algorithm.

[0064] During analysis, the analysis unit can apply different analysis methods depending on the employee's position and field of expertise. For example, the analysis unit can apply technical analysis methods to employees in the engineering department and provide specialized feedback. The analysis unit can also apply sales-specific analysis methods to employees in the sales department and provide practical feedback. Furthermore, the analysis unit can apply management-related analysis methods to managerial employees and provide strategic feedback. In this way, appropriate feedback can be provided by applying analysis methods depending on the employee's position and field of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input employee position and field of expertise data into the generation AI and have the generation AI apply the analysis method.

[0065] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows for highly visible display by adjusting the display method of the analysis results according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0066] During analysis, the analysis unit can determine the analysis priority based on the employee's submission time. For example, the analysis unit prioritizes analysis of data from employees whose submission deadlines are approaching and provides results quickly. The analysis unit can also postpone data from employees whose submission deadlines are further away, allowing for more efficient analysis. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the submission time. This enables efficient analysis by determining the analysis priority based on the submission time. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input employee submission time data into the generation AI and have the generation AI determine the analysis priority.

[0067] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the employee's related literature. The analysis unit can improve the accuracy of the analysis by, for example, referring to related literature written by the employee. The analysis unit can also improve the accuracy of the analysis based on literature cited by the employee. Furthermore, the analysis unit can improve the accuracy of the analysis based on literature previously referenced by the employee. In this way, by improving the accuracy of the analysis by referring to related literature, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the employee's related literature data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0068] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated user emotions. For example, if the user is relaxed, the matching unit applies detailed matching criteria to select the most suitable corrector. Furthermore, if the user is in a hurry, the matching unit can apply simple matching criteria to quickly select a corrector. Furthermore, if the user is stressed, the matching unit can apply less burdensome matching criteria to select an appropriate corrector. This allows the selection of an appropriate corrector by adjusting the matching criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the matching unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the matching unit can input the user's emotion data into the generation AI and have the generation AI adjust the matching criteria.

[0069] The matching unit can improve the accuracy of matching by taking into account the interrelationships between employees. For example, the matching unit selects a compatible corrector based on past cooperative relationships between employees. The matching unit can also avoid past conflicts between employees and perform smooth matching. Furthermore, the matching unit can select an appropriate corrector based on the employees' common project experience. This enables smooth matching by performing matching while taking into account the interrelationships between employees. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input employee interrelationship data into the generation AI and cause the generation AI to improve the accuracy of matching.

[0070] The matching unit can perform matching while taking employee attribute information into consideration. The matching unit selects an appropriate corrector based on, for example, the employee's job title or field of expertise. The matching unit can also select the most suitable corrector based on the employee's past presentation experience. Furthermore, the matching unit can also select an appropriate corrector based on the employee's interests and areas of expertise. In this way, by performing matching while taking employee attribute information into consideration, an appropriate corrector can be selected. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input employee attribute information data into the generation AI and have the generation AI perform matching.

[0071] The matching unit can estimate the user's emotions and adjust the display order of the matching results based on the estimated user emotions. For example, if the user is nervous, the matching unit provides a simple, highly visible display order. Furthermore, if the user is relaxed, the matching unit can provide a display order that includes detailed information. Furthermore, if the user is in a hurry, the matching unit can provide a display order that focuses on the main points. This allows for highly visible display by adjusting the display order of the matching results according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the matching unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the matching unit can input the user's emotion data into the generation AI and have the generation AI adjust the display order.

[0072] The matching unit can perform matching taking into account the geographical distribution of employees. For example, the matching unit can prioritize matching between geographically close employees to ensure a quick response. The matching unit can also perform efficient matching by avoiding matching between geographically distant employees. Furthermore, the matching unit can match employees related to a specific region based on geographical distribution. This enables efficient matching by performing matching taking geographical distribution into consideration. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input employee geographical distribution data into the generation AI and have the generation AI perform matching.

[0073] The matching unit can improve the accuracy of matching by referring to the employee's related literature during matching. The matching unit can improve the accuracy of matching by referring to, for example, related literature written by the employee. The matching unit can also improve the accuracy of matching based on literature cited by the employee. Furthermore, the matching unit can improve the accuracy of matching based on literature referenced by the employee in the past. In this way, by improving the accuracy of matching by referring to related literature, an appropriate editor can be selected. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the employee's related literature data into the generation AI and have the generation AI improve the accuracy of matching.

[0074] The practice unit can estimate the user's emotions and adjust the presentation practice method based on the estimated user emotions. For example, if the user is nervous, the practice unit can provide the presentation practice in a relaxed environment. If the user is relaxed, the practice unit can also provide detailed feedback to improve the quality of the practice. Furthermore, if the user is in a hurry, the practice unit can provide a short, effective practice method. This enables effective practice by adjusting the presentation practice method according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the practice unit can be performed using, for example, an AI, or without an AI. For example, the practice unit can input the user's emotion data into the generation AI and have the generation AI adjust the practice method.

[0075] When practicing a presentation, the practice unit can optimize the practice method by referring to past practice results. For example, the practice unit provides an optimal practice method based on past successful presentation practice results. The practice unit can also analyze past unsuccessful presentation practice results and provide a practice method that reflects areas for improvement. Furthermore, the practice unit can extract common points from past presentation practice results and provide an optimal practice method. In this way, the effectiveness of practice is improved by optimizing the practice method by referring to past practice results. Some or all of the above-mentioned processing in the practice unit may be performed using, for example, AI, or may be performed without using AI. For example, the practice unit can input past practice data into a generation AI and have the generation AI optimize the practice method.

[0076] The practice department can customize the practice method based on the employee's current work situation when practicing a presentation. For example, the practice department can provide a short and effective practice method for employees with a high workload. The practice department can also provide a practice method that includes detailed feedback for employees with a low workload. Furthermore, the practice department can practice at an appropriate time depending on the progress of the project. This allows for efficient practice by customizing the practice method according to the employee's work situation. Some or all of the above-mentioned processing in the practice department may be performed using, for example, AI, or may be performed without using AI. For example, the practice department can input employee work situation data into the generation AI and have the generation AI customize the practice method.

[0077] The practice unit can estimate the user's emotions and prioritize presentation practice sessions based on the estimated user emotions. For example, if the user is nervous, the practice unit can prioritize practice sessions in a relaxed environment. Furthermore, if the user is relaxed, the practice unit can prioritize practice sessions that include detailed feedback. Furthermore, if the user is in a hurry, the practice unit can prioritize short, effective practice sessions. This enables effective practice sessions by prioritizing presentation practice sessions based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the practice unit can be performed using, for example, an AI, or without an AI. For example, the practice unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of practice sessions.

[0078] When practicing a presentation, the practice department can select the optimal practice method by taking into account the employees' geographic location information. For example, the practice department can prioritize practice between geographically close employees to ensure quick feedback. The practice department can also avoid practice between geographically distant employees to ensure efficient practice. Furthermore, the practice department can select a practice method related to a specific region based on the geographic location information. This enables efficient practice by selecting a practice method by taking geographic location information into account. Some or all of the above-mentioned processing in the practice department may be performed using, for example, AI, or may be performed without using AI. For example, the practice department can input the employees' geographic location data into the generation AI and have the generation AI select a practice method.

[0079] When practicing a presentation, the practice department can analyze employees' social media activity and suggest ways to practice. For example, the practice department can suggest practice methods related to the employee's areas of interest based on the employee's social media activity. The practice department can also analyze comments and posts on social media to suggest related practice methods. Furthermore, the practice department can suggest feedback from influential employees based on social media followers and friendships. This allows for highly relevant practice by analyzing social media activity and suggesting ways to practice. Some or all of the above-mentioned processing in the practice department may be performed using, for example, AI, or may be performed without using AI. For example, the practice department can input employees' social media data into a generation AI and have the generation AI execute suggestions for practice methods. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, matching unit, and practice unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data on employees' job titles and fields of expertise using the control unit 46A of the smart device 14. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and selects an appropriate corrector. The matching unit matches the corrector selected by the specific processing unit 290 of the data processing device 12 with the presentation target. The practice unit practices the presentation with the corrector matched by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, matching unit, and practice unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data on employee positions and areas of expertise using the control unit 46A of the smart glasses 214. The analysis unit analyzes the data collected by, for example, the specific processing unit 290 of the data processing device 12 and selects an appropriate corrector. For example, the matching unit matches the corrector selected by the specific processing unit 290 of the data processing device 12 with the presentation subject. For example, the practice unit practices the presentation with the corrector matched by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, matching unit, and practice unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects data on employee job titles and fields of expertise using the control unit 46A of the headset type terminal 314. The analysis unit analyzes the data collected, for example, by the specific processing unit 290 of the data processing device 12, and selects an appropriate corrector. The matching unit matches, for example, the corrector selected by the specific processing unit 290 of the data processing device 12 with the presentation target. The practice unit, for example, practices the presentation with the corrector matched by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, matching unit, and practice unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data on employee positions and areas of expertise using the control unit 46A of the robot 414. The analysis unit analyzes the data collected, for example, by the specific processing unit 290 of the data processing device 12, and selects an appropriate corrector. The matching unit matches, for example, the corrector selected by the specific processing unit 290 of the data processing device 12 with the subject of the presentation. The practice unit, for example, practices the presentation with the corrector matched by the control unit 46A of the robot 414.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The data collection department can monitor employees' health status and adjust the frequency and method of data collection based on their health condition. For example, if an employee feels fatigued, the frequency of data collection can be reduced to reduce the burden. Also, if an employee is healthy, detailed data collection can be performed to obtain more information. Furthermore, if an employee is ill, only important data can be collected as a priority and processed quickly. This allows the timing and method of data collection to be adjusted according to the employee's health status, reducing the burden on employees and enabling efficient data collection.

[0082] The collection department can customize the content of data collection taking into account the hobbies and interests of employees. For example, if an employee is interested in sports, data related to sports can be collected first. If an employee is interested in music, data related to music can be collected. Furthermore, if an employee is interested in travel, data related to travel can be collected. In this way, by customizing the content of data collection according to the hobbies and interests of employees, more relevant data can be collected.

[0083] The data collection department can analyze employees' communication styles and select the appropriate data collection method. For example, if employees prefer verbal communication, data can be collected in the form of an interview. If employees prefer written communication, data can be collected in the form of a questionnaire. Furthermore, if employees prefer digital tools, data can be collected using online forms or chatbots. This allows for efficient data collection by selecting a data collection method according to employees' communication styles.

[0084] The data collection department can determine the priority of data collection taking into account the career goals of employees. For example, priority can be given to collecting data on employees who are aiming for promotion. Data on employees who are aiming to improve their professional skills can also be collected preferentially. Data on employees who wish to be transferred can also be collected preferentially. In this way, by determining the priority of data collection according to the career goals of employees, important data can be collected preferentially.

[0085] The collection department can adjust the timing of data collection taking into account employees' working hours. For example, data collection from night-shift employees can be done at night, and data collection from day-shift employees can be done during the day. Furthermore, data collection from employees on a flextime system can be flexibly adjusted. This allows for efficient data collection by adjusting the timing of data collection according to employees' working hours.

[0086] The analysis unit can estimate the user's emotions and adjust the method of providing feedback on the analysis results based on the estimated user emotions. For example, if the user is nervous, positive feedback can be given priority. If the user is relaxed, detailed feedback can be provided. Furthermore, if the user is in a hurry, feedback that focuses on the main points can be provided. This allows for effective feedback by adjusting the method of providing feedback on the analysis results according to the user's emotions.

[0087] The analysis unit can estimate the user's emotions and adjust the speed of the analysis based on the estimated user emotions. For example, if the user is relaxed, a detailed analysis can be performed to provide highly accurate results. If the user is in a hurry, a simple analysis can be performed to provide quick results. Furthermore, if the user is feeling stressed, the analysis load can be reduced to provide quick results. This allows for efficient analysis by adjusting the speed of the analysis according to the user's emotions.

[0088] The analysis unit can estimate the user's emotions and customize the display format of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display format can be provided. If the user is relaxed, a display format including detailed information can be provided. Furthermore, if the user is in a hurry, a display format that focuses on the main points can be provided. In this way, by customizing the display format of the analysis results according to the user's emotions, a highly visible display can be achieved.

[0089] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis of important data can be prioritized. Alternatively, if the user is relaxed, the analysis of detailed data can be prioritized. Furthermore, if the user is in a hurry, the analysis of simple data can be prioritized to provide results quickly. This allows for efficient analysis by determining the priority of analysis according to the user's emotions.

[0090] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. For example, if the user is relaxed, a detailed analysis can be performed to provide highly accurate results. If the user is in a hurry, a simple analysis can be performed to provide quick results. Furthermore, if the user is feeling stressed, the analysis load can be reduced to provide quick results. This allows for efficient analysis by adjusting the accuracy of the analysis according to the user's emotions.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The data collection department collects data on employees' job titles, areas of expertise, and past presentation experience. Employee job titles include, for example, manager, engineer, sales representative, etc., and areas of expertise include, for example, IT, marketing, finance, etc. Past presentation experience includes the number of presentations, success rate, feedback, etc. Step 2: The analysis department selects correctors based on the data collected by the collection department. The analysis department selects correctors based on the employee's position, area of ​​expertise, and past presentation experience. Step 3: The matching department matches the correctors selected by the analysis department with the presentation subjects. The matching department matches based on the employee's position, area of ​​expertise, and past presentation experience. Step 4: The Practice Department will practice presentations with the correctors matched by the Matching Department. The Practice Department will practice presentations based on the employee's position, area of ​​expertise, and past presentation experience.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0095] 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.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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, in order to avoid confusion and to 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.

[0163] 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.

[0164] [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department collects data on employees' job titles, areas of expertise, and past presentation experiences. an analysis unit that selects a corrector based on the data collected by the collection unit; a matching unit that matches the correctors selected by the analysis unit with presentation targets; a practice unit that performs presentation practice with the corrector matched by the matching unit. A system characterized by:

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

3. The collecting unit Analyze employees' past presentation success rates and prioritize the data to be collected 2. The system of claim 1.

4. The collecting unit When collecting data, adjust collection methods based on employees' current projects and workloads 2. The system of claim 1.

5. The collecting unit Inferring user emotions and selecting the type of data to collect based on the estimated user emotions 2. The system of claim 1.

6. The collecting unit When collecting data, prioritize the collection of relevant data by taking into account the geographic location of employees.

2. The system of claim 1.

7. The collecting unit During data collection, analyze employees' social media activity and collect relevant data.

2. The system of claim 1.

8. The analysis unit Estimate the user's emotions and adjust the parameters of the analysis algorithm based on the estimated user emotions.

2. The system of claim 1.

Images