system

Abstract

The system according to the embodiment aims to allow new recruits to train without any hesitation. [Solution] A system according to an embodiment includes a selection unit, a generation unit, a simulation unit, an evaluation unit, and a feedback unit. The selection unit selects training content. The generation unit generates a training scenario based on the content selected by the selection unit. The simulation unit asks questions and conducts simulations based on the scenario generated by the generation unit. The evaluation unit evaluates the answers and actions of the new employee. The feedback unit provides feedback based on the results of the evaluation by the evaluation 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, training new employees required them to interact with their superiors and colleagues, making it difficult for them to receive training without feeling uncomfortable.

[0005] The system according to the embodiment aims to allow new recruits to train without any hesitation. [Means for solving the problem]

[0006] The system according to the embodiment includes a selection unit, a generation unit, a simulation unit, an evaluation unit, and a feedback unit. The selection unit selects training content. The generation unit generates a training scenario based on the content selected by the selection unit. The simulation unit asks questions and performs simulations based on the scenario generated by the generation unit. The evaluation unit evaluates the answers and actions of the new employee. The feedback unit provides feedback based on the results of the evaluation by the evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows new employees to train without any hesitation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A training system according to an embodiment of the present invention uses a test-and-answer AI to help newly hired shop crew members become immediately effective. This training system begins with a new employee selecting training content and inputting it into the AI. The AI ​​then generates training scenarios based on the selected content, asking questions and conducting simulations for the new employee. The new employee can acquire practical skills through answering the AI's questions and simulations. The AI ​​evaluates the new employee's responses and behavior and provides feedback. This allows new employees to progress through training at their own pace, enabling them to become immediately effective. For example, in customer service training, the AI ​​plays the role of a customer, and the new employee serves the customer. The AI ​​prompts the new employee for appropriate responses to customer questions and requests and evaluates the new employee's response. In product knowledge training, the AI ​​poses product-related questions to the new employee, who answers them. The AI ​​evaluates the accuracy of the answers and provides feedback to supplement the necessary knowledge. This system allows new employees to effectively train without feeling embarrassed or nervous. Furthermore, the AI ​​is available 24 hours a day, allowing new employees to train at their own convenience. This promotes the immediate effectiveness of shop crew members and improves the efficiency of shop operations. This allows new recruits to progress through the training system at their own pace, making them immediately effective.

[0029] A training system according to an embodiment includes a selection unit, a generation unit, a simulation unit, an evaluation unit, and a feedback unit. The selection unit allows a new employee to select training content. The selection unit provides a training menu, such as customer service training, product knowledge training, and complaint handling training. The generation unit generates a training scenario based on the content selected by the selection unit. The generation unit generates a training scenario based on, for example, past training data or actual customer service data. The simulation unit asks questions and performs a simulation based on the scenario generated by the generation unit. The simulation unit provides, for example, a simulation in which an AI plays the role of a customer and the new employee provides customer service. The evaluation unit evaluates the new employee's answers and actions. The evaluation unit assigns a score based on, for example, evaluation criteria. The feedback unit provides feedback based on the results of the evaluation by the evaluation unit. The feedback unit provides specific feedback to the new employee based on, for example, the evaluation results. As a result, the training system according to an embodiment allows new employees to progress through training at their own pace, enabling them to become immediately effective.

[0030] The selection unit can provide training menus such as customer service training, product knowledge training, and complaint handling training. The selection unit provides training menus such as customer service training, product knowledge training, and complaint handling training, allowing new employees to select from a variety of training menus. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, when selecting a training menu, the selection unit can analyze the new employee's past training history and automatically suggest the optimal training menu.

[0031] The generation unit can generate a training scenario based on past training data or actual customer service data. The generation unit generates a training scenario based on, for example, past training data or actual customer service data. For example, the generation unit can generate a scenario in which a new employee is weak based on past training data. The generation unit can also generate a scenario in which a new employee is strong based on past training data. Furthermore, the generation unit can generate a scenario in which skill improvement is observed based on past training data. This generates a realistic training scenario. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, when generating a training scenario based on past training data, the generation unit can adjust the content of the scenario using a generation AI.

[0032] The simulation unit can provide a simulation in which an AI plays the role of a customer and a new employee serves the customer. The simulation unit can provide, for example, a simulation in which an AI plays the role of a customer and a new employee serves the customer. For example, the simulation unit can provide a realistic simulation using specific methods and techniques in which an AI plays the role of a customer. This allows the new employee to acquire practical skills. Some or all of the above-mentioned processing in the simulation unit can be performed, for example, using an AI, or can be performed without using an AI. For example, when an AI plays the role of a customer, the simulation unit can use a generation AI to generate customer questions and requests.

[0033] The evaluation unit can evaluate the new employee's answers and actions and assign a score based on the evaluation criteria. The evaluation unit, for example, evaluates the new employee's answers and actions and assigns a score based on the evaluation criteria. For example, the evaluation unit can objectively evaluate the new employee's skill level based on a scoring method and evaluation items. This allows the new employee's skill level to be objectively evaluated. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can adjust the evaluation criteria using a generation AI when evaluating the new employee's answers and actions.

[0034] The feedback unit can provide specific feedback to the new employee based on the evaluation results. The feedback unit provides specific feedback to the new employee based on, for example, the evaluation results. For example, the feedback unit can make the new employee understand specific areas for improvement based on the format and timing of the feedback. This allows the new employee to understand specific areas for improvement. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, when providing feedback based on the evaluation results, the feedback unit can adjust the content of the feedback using a generation AI.

[0035] The selection unit can analyze the rookie's past training history and automatically suggest a training menu. The selection unit, for example, analyzes the rookie's past training history and automatically suggests an optimal training menu. For example, the selection unit can prioritize suggesting training menus that the rookie found difficult in the past. The selection unit can also suggest training menus that the rookie has previously received high marks for. Furthermore, the selection unit can suggest menus that show improvement in the rookie's skills based on the rookie's past training history. This allows the optimal training menu to be suggested for the rookie. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can use a generation AI when analyzing the rookie's past training history.

[0036] The selection unit can perform filtering based on the rookie's current skill level when selecting a training menu. The selection unit can perform filtering based on the rookie's current skill level when selecting a training menu, for example. For example, the selection unit can display a training menu of an appropriate difficulty level according to the rookie's current skill level. Furthermore, the selection unit can also preferentially display a basic training menu when the rookie's skill level is low. Furthermore, the selection unit can also preferentially display an applied training menu when the rookie's skill level is high. In this way, a training menu according to the rookie's skill level is provided. Some or all of the above-described processing in the selection unit can be performed using, for example, AI, or can be performed without using AI. For example, the selection unit can evaluate the skill level using a generation AI when evaluating the rookie's current skill level.

[0037] When selecting a training menu, the selection unit can prioritize displaying a highly relevant menu in consideration of the geographical location information of the new recruit. For example, when selecting a training menu, the selection unit can prioritize displaying a highly relevant menu in consideration of the geographical location information of the new recruit. For example, when the new recruit works in a specific area, the selection unit can prioritize displaying a training menu related to that area. Furthermore, when the new recruit works in a specific store, the selection unit can also display a training menu tailored to the characteristics of that store. Furthermore, when the new recruit serves customers in a specific area, the selection unit can also display a training menu based on the customer characteristics of that area. In this way, a training menu based on the geographical location information is provided. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, when acquiring the geographical location information of the new recruit, the selection unit can analyze the location information using a generation AI.

[0038] The selection unit can analyze the newcomer's social media activities and suggest a related menu when selecting a training menu. For example, the selection unit can analyze the newcomer's social media activities and suggest a related menu when selecting a training menu. For example, the selection unit can suggest a training menu related to topics the newcomer has shown interest in on social media. The selection unit can also suggest a training menu based on industry trends that the newcomer follows on social media. Furthermore, the selection unit can suggest a related training menu based on experiences the newcomer has shared on social media. In this way, a training menu based on social media activities is provided. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can perform the analysis using generative AI when analyzing the newcomer's social media activities.

[0039] The generation unit can improve the accuracy of a training scenario by referring to past training data when generating the training scenario. For example, the generation unit can improve the accuracy of a scenario by referring to past training data when generating the training scenario. For example, the generation unit can generate a scenario in which a rookie is weak based on past training data. The generation unit can also generate a scenario in which a rookie is good based on past training data. Furthermore, the generation unit can generate a scenario in which skill improvement is observed based on past training data. This generates a highly accurate scenario based on past data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, when referring to past training data, the generation unit can analyze the data using a generation AI.

[0040] The generation unit can enhance the realism of the training scenario based on actual customer service data when generating the scenario. The generation unit can enhance the realism of the scenario based on actual customer service data when generating the training scenario. For example, the generation unit can generate a realistic customer service scenario based on actual customer service data. The generation unit can also generate a realistic complaint handling scenario based on actual customer service data. Furthermore, the generation unit can generate a realistic product knowledge scenario based on actual customer service data. This generates a realistic scenario based on actual data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, when generating a scenario based on actual customer service data, the generation unit can analyze the data using a generation AI.

[0041] When generating training scenarios, the generation unit can determine the priority of the scenarios based on the submission time of the newcomer. For example, when generating training scenarios, the generation unit determines the priority of the scenarios based on the submission time of the newcomer. For example, if the newcomer submits early, the generation unit can generate a scenario with priority. Also, if the newcomer submits late, the generation unit can generate a scenario later. Furthermore, if the newcomer submits within a specific deadline, the generation unit can generate a scenario to match that deadline. This provides scenarios prioritized based on the submission time. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, when evaluating the submission time of the newcomer, the generation unit can analyze the submission time using a generation AI.

[0042] The generation unit can adjust the order of scenarios based on the relevance of the new employee when generating training scenarios. For example, the generation unit can adjust the order of scenarios based on the relevance of the new employee when generating training scenarios. For example, if the new employee requires a specific skill, the generation unit can prioritize generating scenarios related to that skill. Furthermore, if the new employee is engaged in a specific task, the generation unit can also prioritize generating scenarios related to that task. Furthermore, if the new employee is to interact with a specific customer, the generation unit can also prioritize generating scenarios related to that customer interaction. This provides an order of scenarios based on relevance. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, when evaluating the relevance of the new employee, the generation unit can use a generation AI to evaluate the relevance.

[0043] The simulation unit can improve the accuracy of the simulation by taking into account the interrelationships between ideas during the simulation. For example, the simulation unit can provide a realistic simulation by taking into account the interrelationships between ideas during the simulation. The simulation unit can also adjust the simulation scenario by taking into account the interrelationships between ideas. Furthermore, the simulation unit can evaluate the results of the simulation by taking into account the interrelationships between ideas. This provides a highly accurate simulation that takes into account the interrelationships between ideas. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can use a generation AI to evaluate the interrelationships between ideas.

[0044] The simulation unit can perform a simulation taking into account attribute information of the idea submitter during the simulation. The simulation unit can perform a simulation taking into account attribute information of the idea submitter during the simulation, for example. For example, the simulation unit can adjust the simulation scenario taking into account attribute information of the idea submitter. The simulation unit can also adjust the difficulty of the simulation taking into account attribute information of the idea submitter. Furthermore, the simulation unit can evaluate the results of the simulation taking into account attribute information of the idea submitter. This provides a simulation that takes into account the attribute information of the submitter. Some or all of the above-described processing in the simulation unit can be performed using, for example, AI, or can be performed without using AI. For example, the simulation unit can use a generation AI to evaluate the attribute information of the submitter.

[0045] The simulation unit can perform a simulation taking into account the geographical distribution of ideas during the simulation. For example, the simulation unit can perform a simulation taking into account the geographical distribution of ideas during the simulation. For example, the simulation unit can adjust the simulation scenario taking into account the geographical distribution of ideas. The simulation unit can also adjust the difficulty of the simulation taking into account the geographical distribution of ideas. Furthermore, the simulation unit can evaluate the results of the simulation taking into account the geographical distribution of ideas. This provides a simulation that takes geographical distribution into account. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can use a generation AI to perform the evaluation when evaluating the geographical distribution.

[0046] The simulation unit can improve the accuracy of the simulation by referring to literature related to the idea during the simulation. The simulation unit can improve the accuracy of the simulation by referring to literature related to the idea during the simulation, for example. For example, the simulation unit can adjust the simulation scenario by referring to literature related to the idea. The simulation unit can also adjust the difficulty of the simulation by referring to literature related to the idea. Furthermore, the simulation unit can evaluate the results of the simulation by referring to literature related to the idea. This provides a highly accurate simulation that references the related literature. Some or all of the above-mentioned processing in the simulation unit can be performed using, for example, AI, or can be performed without using AI. For example, when referring to related literature, the simulation unit can analyze the literature using a generation AI.

[0047] The evaluation unit can optimize the evaluation algorithm by referring to past evaluation data during evaluation. The evaluation unit can optimize the evaluation algorithm by referring to past evaluation data, for example, during evaluation. For example, the evaluation unit can adjust the evaluation criteria for new employees based on past evaluation data. The evaluation unit can also optimize the evaluation algorithm based on past evaluation data. Furthermore, the evaluation unit can improve the accuracy of the evaluation based on past evaluation data. This provides an optimal evaluation based on past data. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, when referring to past evaluation data, the evaluation unit can analyze the data using a generation AI.

[0048] The evaluation unit can perform the evaluation by taking into consideration the attribute information of the new employee. For example, the evaluation unit can perform the evaluation by taking into consideration the attribute information of the new employee. For example, the evaluation unit can adjust the evaluation criteria based on the attribute information of the new employee. The evaluation unit can also optimize the evaluation algorithm based on the attribute information of the new employee. Furthermore, the evaluation unit can improve the accuracy of the evaluation based on the attribute information of the new employee. This provides an evaluation that takes into consideration the attribute information. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can perform the evaluation by using a generation AI when evaluating the attribute information of the new employee.

[0049] The evaluation unit can perform the evaluation by taking into account the geographical location information of the new employee. For example, the evaluation unit can perform the evaluation by taking into account the geographical location information of the new employee. For example, if the new employee works in a specific area, the evaluation unit can perform the evaluation by taking into account the characteristics of the area. Furthermore, if the new employee works in a specific store, the evaluation unit can perform the evaluation by taking into account the characteristics of the store. Furthermore, if the new employee deals with customers in a specific area, the evaluation unit can perform the evaluation by taking into account the customer characteristics of the area. In this way, an evaluation that takes into account the geographical location information is provided. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can analyze the location information using a generation AI when evaluating the geographical location information of the new employee.

[0050] The evaluation unit can improve the accuracy of the evaluation by referring to the related literature of the newcomer during the evaluation. The evaluation unit can improve the accuracy of the evaluation by referring to the related literature of the newcomer during the evaluation, for example. For example, the evaluation unit can adjust the evaluation criteria by referring to the related literature of the newcomer. The evaluation unit can also optimize the evaluation algorithm by referring to the related literature of the newcomer. Furthermore, the evaluation unit can improve the accuracy of the evaluation by referring to the related literature of the newcomer. This provides a highly accurate evaluation by referring to the related literature. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can analyze the literature using a generation AI when referring to the related literature.

[0051] The feedback unit can improve the accuracy of feedback by referring to past feedback data when providing feedback. For example, the feedback unit can improve the accuracy of feedback by referring to past feedback data when providing feedback. For example, the feedback unit can adjust the feedback content for newcomers based on past feedback data. The feedback unit can also optimize a feedback algorithm based on past feedback data. Furthermore, the feedback unit can improve the accuracy of feedback based on past feedback data. This provides highly accurate feedback based on past data. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can analyze data using a generation AI when referring to past feedback data.

[0052] The feedback unit can provide feedback taking into account the attribute information of the new employee when providing feedback. For example, the feedback unit can provide feedback taking into account the attribute information of the new employee when providing feedback. For example, the feedback unit can adjust the feedback content based on the attribute information of the new employee. The feedback unit can also optimize the feedback algorithm based on the attribute information of the new employee. Furthermore, the feedback unit can improve the accuracy of the feedback based on the attribute information of the new employee. This allows feedback that takes the attribute information into account to be provided. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can use a generation AI when evaluating the attribute information of the new employee.

[0053] The feedback unit can provide feedback taking into account the geographical location information of the new employee when providing feedback. For example, the feedback unit can provide feedback taking into account the geographical location information of the new employee when providing feedback. For example, if the new employee works in a specific area, the feedback unit can provide feedback taking into account the characteristics of the area. Furthermore, if the new employee works in a specific store, the feedback unit can provide feedback taking into account the characteristics of the store. Furthermore, if the new employee deals with customers in a specific area, the feedback unit can provide feedback taking into account the customer characteristics of the area. In this way, feedback taking into account the geographical location information is provided. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can analyze the location information using a generation AI when evaluating the geographical location information of the new employee.

[0054] The feedback unit can improve the accuracy of the feedback by referring to the related literature of the newcomer when providing feedback. For example, the feedback unit can improve the accuracy of the feedback by referring to the related literature of the newcomer when providing feedback. For example, the feedback unit can adjust the feedback content by referring to the related literature of the newcomer. The feedback unit can also optimize the feedback algorithm by referring to the related literature of the newcomer. Furthermore, the feedback unit can improve the accuracy of the feedback by referring to the related literature of the newcomer. This provides highly accurate feedback that refers to the related literature. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can analyze the literature using a generation AI when referring to the related literature.

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

[0056] The training system may further include a reward unit. The reward unit may provide rewards such as badges and points when new employees complete training. For example, if a new employee completes customer service training, the reward unit may award the employee with a "customer service master" badge. If a new employee achieves a high score in product knowledge training, the reward unit may award the employee points, which can be accumulated and exchanged for rewards. Furthermore, if a new employee demonstrates excellent performance in complaint handling training, the reward unit may award the employee with a special title. This may increase the new employee's motivation for training.

[0057] The training system may further include a communication unit. The communication unit may provide a function for promoting communication between new recruits and their trainers. For example, if a new recruit has a question during training, the new recruit can send the question to the trainer in real time through the communication unit. The system may also provide a chat function for new recruits to share their training progress and experiences with each other. Furthermore, the communication unit may provide a forum for sharing training feedback with each other. This allows new recruits to progress through training without feeling isolated.

[0058] The training system can further include a customization unit. The customization unit can customize the training content according to the individual needs and goals of the new employee. For example, if the new employee wants to strengthen a specific skill, the customization unit can provide a training plan specialized for that skill. Also, if the new employee is scheduled to engage in a specific job, the customization unit can provide training content related to that job with priority. Furthermore, if the new employee wants to train during a specific time period, the customization unit can provide a training schedule that matches that time period. This allows the new employee to receive training that is optimal for them.

[0059] The training system can further include an analytics section. The analytics section can analyze the training data of new employees and evaluate the effectiveness of the training. For example, the analytics section can visualize the training progress and scores of new employees using graphs and charts. The analytics section can also identify areas for skill improvement and issues based on the training history of new employees. Furthermore, the analytics section can evaluate the effectiveness of the training and suggest areas for improvement. This makes it possible to objectively evaluate the effectiveness of training and develop effective training plans.

[0060] The training system can further include a guide unit. The guide unit can provide appropriate guidance and hints to the newcomer as he or she progresses through the training. For example, if the newcomer encounters difficulties during the training, the guide unit can provide hints and advice for solving the problem. The guide unit can also guide the newcomer on the next step to take depending on the progress of the training. Furthermore, the guide unit can provide best practices for the newcomer to effectively progress through the training. This allows the newcomer to smoothly progress through the training.

[0061] The training system may further include a reminder unit. The reminder unit may provide a reminder to ensure that the new employee does not forget to perform the training. For example, the reminder unit may notify the new employee of the time to start the training. It may also remind the new employee of the deadline for completing the training. Furthermore, the reminder unit may provide a reminder to encourage the new employee to resume the training if the new employee has interrupted it. This allows the new employee to proceed with the training in a planned manner.

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

[0063] Step 1: The selection unit allows the new employee to select training content. The selection unit provides a training menu, such as customer service training, product knowledge training, and complaint handling training. Step 2: The generating unit generates a training scenario based on the content selected by the selecting unit. The generating unit generates the training scenario based on, for example, past training data or actual customer service data. Step 3: The simulation unit asks questions and performs simulations based on the scenario generated by the generation unit. For example, the simulation unit provides a simulation in which the AI ​​plays the role of a customer and a new employee serves the customer. Step 4: The evaluation unit evaluates the newcomer's answers and actions. For example, the evaluation unit assigns a score based on the evaluation criteria. Step 5: The feedback unit provides feedback based on the results of the evaluation by the evaluation unit. For example, the feedback unit provides specific feedback to the new employee based on the evaluation results.

[0064] (Example 2) A training system according to an embodiment of the present invention uses a test-and-answer AI to help newly hired shop crew members become immediately effective. This training system begins with a new employee selecting training content and inputting it into the AI. The AI ​​then generates training scenarios based on the selected content, asking questions and conducting simulations for the new employee. The new employee can acquire practical skills through answering the AI's questions and simulations. The AI ​​evaluates the new employee's responses and behavior and provides feedback. This allows new employees to progress through training at their own pace, enabling them to become immediately effective. For example, in customer service training, the AI ​​plays the role of a customer, and the new employee serves the customer. The AI ​​prompts the new employee for appropriate responses to customer questions and requests and evaluates the new employee's response. In product knowledge training, the AI ​​poses product-related questions to the new employee, who answers them. The AI ​​evaluates the accuracy of the answers and provides feedback to supplement the necessary knowledge. This system allows new employees to effectively train without feeling embarrassed or nervous. Furthermore, the AI ​​is available 24 hours a day, allowing new employees to train at their own convenience. This promotes the immediate effectiveness of shop crew members and improves the efficiency of shop operations. This allows new recruits to progress through the training system at their own pace, making them immediately effective.

[0065] A training system according to an embodiment includes a selection unit, a generation unit, a simulation unit, an evaluation unit, and a feedback unit. The selection unit allows a new employee to select training content. The selection unit provides a training menu, such as customer service training, product knowledge training, and complaint handling training. The generation unit generates a training scenario based on the content selected by the selection unit. The generation unit generates a training scenario based on, for example, past training data or actual customer service data. The simulation unit asks questions and performs a simulation based on the scenario generated by the generation unit. The simulation unit provides, for example, a simulation in which an AI plays the role of a customer and the new employee provides customer service. The evaluation unit evaluates the new employee's answers and actions. The evaluation unit assigns a score based on, for example, evaluation criteria. The feedback unit provides feedback based on the results of the evaluation by the evaluation unit. The feedback unit provides specific feedback to the new employee based on, for example, the evaluation results. As a result, the training system according to an embodiment allows new employees to progress through training at their own pace, enabling them to become immediately effective.

[0066] The selection unit can provide training menus such as customer service training, product knowledge training, and complaint handling training. The selection unit provides training menus such as customer service training, product knowledge training, and complaint handling training, allowing new employees to select from a variety of training menus. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, when selecting a training menu, the selection unit can analyze the new employee's past training history and automatically suggest the optimal training menu.

[0067] The generation unit can generate a training scenario based on past training data or actual customer service data. The generation unit generates a training scenario based on, for example, past training data or actual customer service data. For example, the generation unit can generate a scenario in which a new employee is weak based on past training data. The generation unit can also generate a scenario in which a new employee is strong based on past training data. Furthermore, the generation unit can generate a scenario in which skill improvement is observed based on past training data. This generates a realistic training scenario. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, when generating a training scenario based on past training data, the generation unit can adjust the content of the scenario using a generation AI.

[0068] The simulation unit can provide a simulation in which an AI plays the role of a customer and a new employee serves the customer. The simulation unit can provide, for example, a simulation in which an AI plays the role of a customer and a new employee serves the customer. For example, the simulation unit can provide a realistic simulation using specific methods and techniques in which an AI plays the role of a customer. This allows the new employee to acquire practical skills. Some or all of the above-mentioned processing in the simulation unit can be performed, for example, using an AI, or can be performed without using an AI. For example, when an AI plays the role of a customer, the simulation unit can use a generation AI to generate customer questions and requests.

[0069] The evaluation unit can evaluate the new employee's answers and actions and assign a score based on the evaluation criteria. The evaluation unit, for example, evaluates the new employee's answers and actions and assigns a score based on the evaluation criteria. For example, the evaluation unit can objectively evaluate the new employee's skill level based on a scoring method and evaluation items. This allows the new employee's skill level to be objectively evaluated. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can adjust the evaluation criteria using a generation AI when evaluating the new employee's answers and actions.

[0070] The feedback unit can provide specific feedback to the new employee based on the evaluation results. The feedback unit provides specific feedback to the new employee based on, for example, the evaluation results. For example, the feedback unit can make the new employee understand specific areas for improvement based on the format and timing of the feedback. This allows the new employee to understand specific areas for improvement. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, when providing feedback based on the evaluation results, the feedback unit can adjust the content of the feedback using a generation AI.

[0071] The selection unit can estimate the newcomer's emotions and adjust the display order of the training menus based on the estimated newcomer's emotions. The selection unit, for example, estimates the newcomer's emotions and adjusts the display order of the training menus based on the estimated newcomer's emotions. For example, if the newcomer is nervous, the selection unit can first display an easy training menu that will help the newcomer relax. Furthermore, if the newcomer is excited, the selection unit can also preferentially display a challenging training menu. Furthermore, if the newcomer is tired, the selection unit can first display a training menu that can be completed in a short time. This provides a training menu that suits the newcomer's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the selection unit may be performed using an AI, or may be performed without using an AI. For example, when estimating the newcomer's emotions, the selection unit can use a generation AI to estimate the emotions.

[0072] The selection unit can analyze the rookie's past training history and automatically suggest a training menu. The selection unit, for example, analyzes the rookie's past training history and automatically suggests an optimal training menu. For example, the selection unit can prioritize suggesting training menus that the rookie found difficult in the past. The selection unit can also suggest training menus that the rookie has previously received high marks for. Furthermore, the selection unit can suggest menus that show improvement in the rookie's skills based on the rookie's past training history. This allows the optimal training menu to be suggested for the rookie. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can use a generation AI when analyzing the rookie's past training history.

[0073] The selection unit can perform filtering based on the rookie's current skill level when selecting a training menu. The selection unit can perform filtering based on the rookie's current skill level when selecting a training menu, for example. For example, the selection unit can display a training menu of an appropriate difficulty level according to the rookie's current skill level. Furthermore, the selection unit can also preferentially display a basic training menu when the rookie's skill level is low. Furthermore, the selection unit can also preferentially display an applied training menu when the rookie's skill level is high. In this way, a training menu according to the rookie's skill level is provided. Some or all of the above-described processing in the selection unit can be performed using, for example, AI, or can be performed without using AI. For example, the selection unit can evaluate the skill level using a generation AI when evaluating the rookie's current skill level.

[0074] The selection unit can estimate the newcomer's emotions and adjust the difficulty of the training menu based on the estimated newcomer's emotions. The selection unit, for example, estimates the newcomer's emotions and adjusts the difficulty of the training menu based on the estimated newcomer's emotions. For example, the selection unit can provide a training menu with a low level of difficulty if the newcomer is nervous. The selection unit can also provide a training menu with a high level of difficulty if the newcomer is relaxed. Furthermore, the selection unit can also provide a training menu with a low level of difficulty that can be completed in a short time if the newcomer is tired. In this way, a training menu with a level of difficulty that corresponds to the newcomer's emotions is provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can 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 selection unit may be performed using an AI, or may be performed without using an AI. For example, when estimating the newcomer's emotions, the selection unit can use a generation AI to estimate the emotions.

[0075] When selecting a training menu, the selection unit can prioritize displaying a highly relevant menu in consideration of the geographical location information of the new recruit. For example, when selecting a training menu, the selection unit can prioritize displaying a highly relevant menu in consideration of the geographical location information of the new recruit. For example, when the new recruit works in a specific area, the selection unit can prioritize displaying a training menu related to that area. Furthermore, when the new recruit works in a specific store, the selection unit can also display a training menu tailored to the characteristics of that store. Furthermore, when the new recruit serves customers in a specific area, the selection unit can also display a training menu based on the customer characteristics of that area. In this way, a training menu based on the geographical location information is provided. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, when acquiring the geographical location information of the new recruit, the selection unit can analyze the location information using a generation AI.

[0076] The selection unit can analyze the newcomer's social media activities and suggest a related menu when selecting a training menu. For example, the selection unit can analyze the newcomer's social media activities and suggest a related menu when selecting a training menu. For example, the selection unit can suggest a training menu related to topics the newcomer has shown interest in on social media. The selection unit can also suggest a training menu based on industry trends that the newcomer follows on social media. Furthermore, the selection unit can suggest a related training menu based on experiences the newcomer has shared on social media. In this way, a training menu based on social media activities is provided. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can perform the analysis using generative AI when analyzing the newcomer's social media activities.

[0077] The generation unit can estimate the newcomer's emotions and adjust the content of the training scenario based on the estimated newcomer's emotions. The generation unit, for example, estimates the newcomer's emotions and adjusts the content of the training scenario based on the estimated newcomer's emotions. For example, if the newcomer is nervous, the generation unit can generate a scenario that helps the newcomer relax. Furthermore, if the newcomer is excited, the generation unit can generate a challenging scenario. Furthermore, if the newcomer is tired, the generation unit can generate a scenario that can be completed in a short time. This provides a training scenario that matches the newcomer's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using an AI, for example, or may be performed without using an AI. For example, when estimating the newcomer's emotions, the generation unit can estimate the emotions using a generation AI.

[0078] The generation unit can improve the accuracy of a training scenario by referring to past training data when generating the training scenario. For example, the generation unit can improve the accuracy of a scenario by referring to past training data when generating the training scenario. For example, the generation unit can generate a scenario in which a rookie is weak based on past training data. The generation unit can also generate a scenario in which a rookie is good based on past training data. Furthermore, the generation unit can generate a scenario in which skill improvement is observed based on past training data. This generates a highly accurate scenario based on past data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, when referring to past training data, the generation unit can analyze the data using a generation AI.

[0079] The generation unit can enhance the realism of the training scenario based on actual customer service data when generating the scenario. The generation unit can enhance the realism of the scenario based on actual customer service data when generating the training scenario. For example, the generation unit can generate a realistic customer service scenario based on actual customer service data. The generation unit can also generate a realistic complaint handling scenario based on actual customer service data. Furthermore, the generation unit can generate a realistic product knowledge scenario based on actual customer service data. This generates a realistic scenario based on actual data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, when generating a scenario based on actual customer service data, the generation unit can analyze the data using a generation AI.

[0080] The generation unit can estimate the newcomer's emotions and adjust the length of the scenario based on the estimated newcomer's emotions. For example, the generation unit can estimate the newcomer's emotions and adjust the length of the scenario based on the estimated newcomer's emotions. For example, if the newcomer is nervous, the generation unit can generate a scenario that can be completed in a short time. Furthermore, if the newcomer is relaxed, the generation unit can generate a longer scenario that includes detailed explanations. Furthermore, if the newcomer is excited, the generation unit can generate a scenario that adds visually stimulating effects. This provides a scenario length that corresponds to the newcomer's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, when estimating the newcomer's emotions, the generation unit can use a generation AI to estimate the emotions.

[0081] When generating training scenarios, the generation unit can determine the priority of the scenarios based on the submission time of the newcomer. For example, when generating training scenarios, the generation unit determines the priority of the scenarios based on the submission time of the newcomer. For example, if the newcomer submits early, the generation unit can generate a scenario with priority. Also, if the newcomer submits late, the generation unit can generate a scenario later. Furthermore, if the newcomer submits within a specific deadline, the generation unit can generate a scenario to match that deadline. This provides scenarios prioritized based on the submission time. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, when evaluating the submission time of the newcomer, the generation unit can analyze the submission time using a generation AI.

[0082] The generation unit can adjust the order of scenarios based on the relevance of the new employee when generating training scenarios. For example, the generation unit can adjust the order of scenarios based on the relevance of the new employee when generating training scenarios. For example, if the new employee requires a specific skill, the generation unit can prioritize generating scenarios related to that skill. Furthermore, if the new employee is engaged in a specific task, the generation unit can also prioritize generating scenarios related to that task. Furthermore, if the new employee is to interact with a specific customer, the generation unit can also prioritize generating scenarios related to that customer interaction. This provides an order of scenarios based on relevance. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, when evaluating the relevance of the new employee, the generation unit can use a generation AI to evaluate the relevance.

[0083] The simulation unit can estimate the new employee's emotions and adjust the difficulty of the simulation based on the estimated new employee's emotions. The simulation unit, for example, estimates the new employee's emotions and adjusts the difficulty of the simulation based on the estimated new employee's emotions. For example, the simulation unit can provide a simulation with a low level of difficulty if the new employee is nervous. The simulation unit can also provide a simulation with a high level of difficulty if the new employee is relaxed. Furthermore, the simulation unit can provide a simulation with a low level of difficulty that can be completed in a short time if the new employee is tired. This provides a simulation with a level of difficulty that corresponds to the new employee's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the simulation unit can be performed using an AI, for example, or without an AI. For example, when estimating the new employee's emotions, the simulation unit can use a generation AI to estimate the emotions.

[0084] The simulation unit can improve the accuracy of the simulation by taking into account the interrelationships between ideas during the simulation. For example, the simulation unit can provide a realistic simulation by taking into account the interrelationships between ideas during the simulation. The simulation unit can also adjust the simulation scenario by taking into account the interrelationships between ideas. Furthermore, the simulation unit can evaluate the results of the simulation by taking into account the interrelationships between ideas. This provides a highly accurate simulation that takes into account the interrelationships between ideas. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can use a generation AI to evaluate the interrelationships between ideas.

[0085] The simulation unit can perform a simulation taking into account attribute information of the idea submitter during the simulation. The simulation unit can perform a simulation taking into account attribute information of the idea submitter during the simulation, for example. For example, the simulation unit can adjust the simulation scenario taking into account attribute information of the idea submitter. The simulation unit can also adjust the difficulty of the simulation taking into account attribute information of the idea submitter. Furthermore, the simulation unit can evaluate the results of the simulation taking into account attribute information of the idea submitter. This provides a simulation that takes into account the attribute information of the submitter. Some or all of the above-described processing in the simulation unit can be performed using, for example, AI, or can be performed without using AI. For example, the simulation unit can use a generation AI to evaluate the attribute information of the submitter.

[0086] The simulation unit can estimate the new employee's emotions and adjust the order in which the simulation results are displayed based on the estimated new employee's emotions. The simulation unit, for example, estimates the new employee's emotions and adjusts the order in which the simulation results are displayed based on the estimated new employee's emotions. For example, if the new employee is nervous, the simulation unit can display the results in order from simple to complex. Furthermore, if the new employee is relaxed, the simulation unit can display the results in order from detailed to complex. Furthermore, if the new employee is excited, the simulation unit can display the results in order from most visually stimulating to most complex. In this way, the simulation results are displayed in an order according to the new employee's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the simulation unit may be performed using an AI, or may be performed without using an AI. For example, when estimating the new employee's emotions, the simulation unit can use a generation AI to estimate the emotions.

[0087] The simulation unit can perform a simulation taking into account the geographical distribution of ideas during the simulation. For example, the simulation unit can perform a simulation taking into account the geographical distribution of ideas during the simulation. For example, the simulation unit can adjust the simulation scenario taking into account the geographical distribution of ideas. The simulation unit can also adjust the difficulty of the simulation taking into account the geographical distribution of ideas. Furthermore, the simulation unit can evaluate the results of the simulation taking into account the geographical distribution of ideas. This provides a simulation that takes geographical distribution into account. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can use a generation AI to perform the evaluation when evaluating the geographical distribution.

[0088] The simulation unit can improve the accuracy of the simulation by referring to literature related to the idea during the simulation. The simulation unit can improve the accuracy of the simulation by referring to literature related to the idea during the simulation, for example. For example, the simulation unit can adjust the simulation scenario by referring to literature related to the idea. The simulation unit can also adjust the difficulty of the simulation by referring to literature related to the idea. Furthermore, the simulation unit can evaluate the results of the simulation by referring to literature related to the idea. This provides a highly accurate simulation that references the related literature. Some or all of the above-mentioned processing in the simulation unit can be performed using, for example, AI, or can be performed without using AI. For example, when referring to related literature, the simulation unit can analyze the literature using a generation AI.

[0089] The evaluation unit can estimate the new employee's emotions and adjust the evaluation criteria based on the estimated new employee's emotions. The evaluation unit, for example, estimates the new employee's emotions and adjusts the evaluation criteria based on the estimated new employee's emotions. For example, the evaluation unit can relax the evaluation criteria when the new employee is nervous. The evaluation unit can also tighten the evaluation criteria when the new employee is relaxed. Furthermore, the evaluation unit can also relax the evaluation criteria when the new employee is tired. This provides evaluation criteria according to the new employee's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit may be performed using an AI, for example, or without using an AI. For example, when estimating the new employee's emotions, the evaluation unit can use a generation AI to estimate the emotions.

[0090] The evaluation unit can optimize the evaluation algorithm by referring to past evaluation data during evaluation. The evaluation unit can optimize the evaluation algorithm by referring to past evaluation data, for example, during evaluation. For example, the evaluation unit can adjust the evaluation criteria for new employees based on past evaluation data. The evaluation unit can also optimize the evaluation algorithm based on past evaluation data. Furthermore, the evaluation unit can improve the accuracy of the evaluation based on past evaluation data. This provides an optimal evaluation based on past data. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, when referring to past evaluation data, the evaluation unit can analyze the data using a generation AI.

[0091] The evaluation unit can perform the evaluation by taking into consideration the attribute information of the new employee. For example, the evaluation unit can perform the evaluation by taking into consideration the attribute information of the new employee. For example, the evaluation unit can adjust the evaluation criteria based on the attribute information of the new employee. The evaluation unit can also optimize the evaluation algorithm based on the attribute information of the new employee. Furthermore, the evaluation unit can improve the accuracy of the evaluation based on the attribute information of the new employee. This provides an evaluation that takes into consideration the attribute information. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can perform the evaluation by using a generation AI when evaluating the attribute information of the new employee.

[0092] The evaluation unit can estimate the new employee's emotions and adjust the display method of the evaluation results based on the estimated new employee's emotions. The evaluation unit, for example, estimates the new employee's emotions and adjusts the display method of the evaluation results based on the estimated new employee's emotions. For example, if the new employee is nervous, the evaluation unit can provide a simple, highly visible display method. If the new employee is relaxed, the evaluation unit can provide a display method including detailed information. Furthermore, if the new employee is in a hurry, the evaluation unit can provide a display method that focuses on the main points. This allows the evaluation results to be provided in a display method that corresponds to the new employee's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit may be performed using an AI, or may be performed without using an AI. For example, when estimating the new employee's emotions, the evaluation unit can use a generation AI to estimate the emotions.

[0093] The evaluation unit can perform the evaluation by taking into account the geographical location information of the new employee. For example, the evaluation unit can perform the evaluation by taking into account the geographical location information of the new employee. For example, if the new employee works in a specific area, the evaluation unit can perform the evaluation by taking into account the characteristics of the area. Furthermore, if the new employee works in a specific store, the evaluation unit can perform the evaluation by taking into account the characteristics of the store. Furthermore, if the new employee deals with customers in a specific area, the evaluation unit can perform the evaluation by taking into account the customer characteristics of the area. In this way, an evaluation that takes into account the geographical location information is provided. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can analyze the location information using a generation AI when evaluating the geographical location information of the new employee.

[0094] The evaluation unit can improve the accuracy of the evaluation by referring to the related literature of the newcomer during the evaluation. The evaluation unit can improve the accuracy of the evaluation by referring to the related literature of the newcomer during the evaluation, for example. For example, the evaluation unit can adjust the evaluation criteria by referring to the related literature of the newcomer. The evaluation unit can also optimize the evaluation algorithm by referring to the related literature of the newcomer. Furthermore, the evaluation unit can improve the accuracy of the evaluation by referring to the related literature of the newcomer. This provides a highly accurate evaluation by referring to the related literature. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can analyze the literature using a generation AI when referring to the related literature.

[0095] The feedback unit can estimate the new employee's emotions and adjust the content of the feedback based on the estimated new employee's emotions. The feedback unit, for example, estimates the new employee's emotions and adjusts the content of the feedback based on the estimated new employee's emotions. For example, if the new employee is nervous, the feedback unit can preferentially provide positive feedback. Furthermore, if the new employee is relaxed, the feedback unit can provide detailed feedback. Furthermore, if the new employee is tired, the feedback unit can provide brief and to-the-point feedback. In this way, feedback according to the new employee's emotions is provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the feedback unit may be performed using an AI, for example, or without using an AI. For example, when estimating the new employee's emotions, the feedback unit can estimate the emotions using a generation AI.

[0096] The feedback unit can improve the accuracy of feedback by referring to past feedback data when providing feedback. For example, the feedback unit can improve the accuracy of feedback by referring to past feedback data when providing feedback. For example, the feedback unit can adjust the feedback content for newcomers based on past feedback data. The feedback unit can also optimize a feedback algorithm based on past feedback data. Furthermore, the feedback unit can improve the accuracy of feedback based on past feedback data. This provides highly accurate feedback based on past data. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can analyze data using a generation AI when referring to past feedback data.

[0097] The feedback unit can provide feedback taking into account the attribute information of the new employee when providing feedback. For example, the feedback unit can provide feedback taking into account the attribute information of the new employee when providing feedback. For example, the feedback unit can adjust the feedback content based on the attribute information of the new employee. The feedback unit can also optimize the feedback algorithm based on the attribute information of the new employee. Furthermore, the feedback unit can improve the accuracy of the feedback based on the attribute information of the new employee. This allows feedback that takes the attribute information into account to be provided. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can use a generation AI when evaluating the attribute information of the new employee.

[0098] The feedback unit can estimate the new employee's emotions and adjust the feedback display method based on the estimated new employee's emotions. The feedback unit, for example, estimates the new employee's emotions and adjusts the feedback display method based on the estimated new employee's emotions. For example, if the new employee is nervous, the feedback unit can provide a simple, highly visible display method. If the new employee is relaxed, the feedback unit can provide a display method including detailed information. If the new employee is in a hurry, the feedback unit can provide a display method that focuses on the main points. In this way, feedback is provided in a display method that corresponds to the new employee's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the feedback unit may be performed using an AI, for example, or without using an AI. For example, when estimating the new employee's emotions, the feedback unit can use a generation AI to estimate the emotions.

[0099] The feedback unit can provide feedback taking into account the geographical location information of the new employee when providing feedback. For example, the feedback unit can provide feedback taking into account the geographical location information of the new employee when providing feedback. For example, if the new employee works in a specific area, the feedback unit can provide feedback taking into account the characteristics of the area. Furthermore, if the new employee works in a specific store, the feedback unit can provide feedback taking into account the characteristics of the store. Furthermore, if the new employee deals with customers in a specific area, the feedback unit can provide feedback taking into account the customer characteristics of the area. In this way, feedback taking into account the geographical location information is provided. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can analyze the location information using a generation AI when evaluating the geographical location information of the new employee.

[0100] The feedback unit can improve the accuracy of the feedback by referring to the related literature of the newcomer when providing feedback. For example, the feedback unit can improve the accuracy of the feedback by referring to the related literature of the newcomer when providing feedback. For example, the feedback unit can adjust the feedback content by referring to the related literature of the newcomer. The feedback unit can also optimize the feedback algorithm by referring to the related literature of the newcomer. Furthermore, the feedback unit can improve the accuracy of the feedback by referring to the related literature of the newcomer. This provides highly accurate feedback that refers to the related literature. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can analyze the literature using a generation AI when referring to the related literature. === Hard Collateral 1-1 === Each of the multiple elements, including the selection unit, generation unit, simulation unit, evaluation unit, and feedback unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the selection unit is implemented by the control unit 46A of the smart device 14 and provides an interface for the newcomer to select training content. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a training scenario based on the selected content. The simulation unit is implemented by the control unit 46A of the smart device 14 and asks questions and performs simulations based on the generated scenario. The evaluation unit is implemented by the specific processing unit 290 of the data processing device 12 and evaluates the newcomer's answers and actions. The feedback unit is implemented by the specific processing unit 290 of the data processing device 12 and provides feedback based on the evaluation results. === Hard Collateral 1-2 === Each of the multiple elements, including the selection unit, generation unit, simulation unit, evaluation unit, and feedback unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the smart glasses 214 and provides an interface for the newcomer to select training content. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a training scenario based on the selected content. The simulation unit is realized by the control unit 46A of the smart glasses 214 and asks questions and performs simulations based on the generated scenario. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the newcomer's answers and actions. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and provides feedback based on the evaluation results. === Hard Collateral 1-3 === Each of the multiple elements, including the selection unit, generation unit, simulation unit, evaluation unit, and feedback unit, described above, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the selection unit is implemented by the control unit 46A of the headset-type terminal 314 and provides an interface for the newcomer to select training content. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates a training scenario based on the selected content. The simulation unit is implemented, for example, by the control unit 46A of the headset-type terminal 314 and performs questions and simulations based on the generated scenario. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the newcomer's answers and actions. The feedback unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides feedback based on the evaluation results. === Hard Collateral 1-4 === Each of the multiple elements, including the selection unit, generation unit, simulation unit, evaluation unit, and feedback unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the robot 414 and provides an interface for the newcomer to select training content. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a training scenario based on the selected content. The simulation unit is realized, for example, by the control unit 46A of the robot 414 and asks questions and performs simulations based on the generated scenario. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the newcomer's answers and actions. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides feedback based on the evaluation results.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The training system may further include a reward unit. The reward unit may provide rewards such as badges and points when new employees complete training. For example, if a new employee completes customer service training, the reward unit may award the employee with a "customer service master" badge. If a new employee achieves a high score in product knowledge training, the reward unit may award the employee points, which can be accumulated and exchanged for rewards. Furthermore, if a new employee demonstrates excellent performance in complaint handling training, the reward unit may award the employee with a special title. This may increase the new employee's motivation for training.

[0103] The training system may further include a communication unit. The communication unit may provide a function for promoting communication between new recruits and their trainers. For example, if a new recruit has a question during training, the new recruit can send the question to the trainer in real time through the communication unit. The system may also provide a chat function for new recruits to share their training progress and experiences with each other. Furthermore, the communication unit may provide a forum for sharing training feedback with each other. This allows new recruits to progress through training without feeling isolated.

[0104] The training system can further include a customization unit. The customization unit can customize the training content according to the individual needs and goals of the new employee. For example, if the new employee wants to strengthen a specific skill, the customization unit can provide a training plan specialized for that skill. Also, if the new employee is scheduled to engage in a specific job, the customization unit can provide training content related to that job with priority. Furthermore, if the new employee wants to train during a specific time period, the customization unit can provide a training schedule that matches that time period. This allows the new employee to receive training that is optimal for them.

[0105] The training system can further include an analytics section. The analytics section can analyze the training data of new employees and evaluate the effectiveness of the training. For example, the analytics section can visualize the training progress and scores of new employees using graphs and charts. The analytics section can also identify areas for skill improvement and issues based on the training history of new employees. Furthermore, the analytics section can evaluate the effectiveness of the training and suggest areas for improvement. This makes it possible to objectively evaluate the effectiveness of training and develop effective training plans.

[0106] The training system can further include a guide unit. The guide unit can provide appropriate guidance and hints to the newcomer as he or she progresses through the training. For example, if the newcomer encounters difficulties during the training, the guide unit can provide hints and advice for solving the problem. The guide unit can also guide the newcomer on the next step to take depending on the progress of the training. Furthermore, the guide unit can provide best practices for the newcomer to effectively progress through the training. This allows the newcomer to smoothly progress through the training.

[0107] The training system can further include an emotion estimation unit. The emotion estimation unit can estimate the emotion of the newcomer in real time and adjust the training content based on the emotion. For example, if the newcomer feels stressed during training, the emotion estimation unit can provide training content that helps the newcomer to relax. Also, if the newcomer has lost motivation, the emotion estimation unit can provide training content that will increase motivation. Furthermore, if the newcomer has confidence, the emotion estimation unit can provide challenging training content. In this way, training is provided that is suited to the newcomer's emotions.

[0108] The training system may further include a reminder unit. The reminder unit may provide a reminder to ensure that the new employee does not forget to perform the training. For example, the reminder unit may notify the new employee of the time to start the training. It may also remind the new employee of the deadline for completing the training. Furthermore, the reminder unit may provide a reminder to encourage the new employee to resume the training if the new employee has interrupted it. This allows the new employee to proceed with the training in a planned manner.

[0109] The training system may further include an emotion feedback unit. The emotion feedback unit may estimate the emotion of the newcomer and provide feedback based on the emotion. For example, if the newcomer feels anxious during training, the emotion feedback unit may provide positive feedback. If the newcomer feels confident, the emotion feedback unit may provide detailed feedback. If the newcomer feels tired, the emotion feedback unit may provide brief and to-the-point feedback. In this way, feedback according to the newcomer's emotion is provided.

[0110] The training system can further include an emotion monitoring unit. The emotion monitoring unit can continuously monitor the emotions of the newcomer and adjust the training content based on the data. For example, if the newcomer becomes nervous during training, the emotion monitoring unit can provide training content to relieve the tension. Also, if the newcomer loses interest in training, the emotion monitoring unit can provide training content to attract the newcomer's interest. Furthermore, if the newcomer has positive emotions toward training, the emotion monitoring unit can provide training content to maintain those emotions. In this way, training is provided according to the newcomer's emotions.

[0111] The training system can further include an emotion alert unit. The emotion alert unit can monitor the emotions of the rookie in real time and issue an alert if it detects an abnormal emotional state. For example, if the rookie feels extreme stress during training, the emotion alert unit can notify the trainer of that state. Also, if the rookie feels extreme fatigue during training, the emotion alert unit can notify the rookie of that state. Furthermore, if the rookie feels extreme excitement during training, the emotion alert unit can notify the trainer of that state. This enables a quick response to abnormal emotional states.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The selection unit allows the new employee to select training content. The selection unit provides a training menu, such as customer service training, product knowledge training, and complaint handling training. Step 2: The generating unit generates a training scenario based on the content selected by the selecting unit. The generating unit generates the training scenario based on, for example, past training data or actual customer service data. Step 3: The simulation unit asks questions and performs simulations based on the scenario generated by the generation unit. For example, the simulation unit provides a simulation in which the AI ​​plays the role of a customer and a new employee serves the customer. Step 4: The evaluation unit evaluates the newcomer's answers and actions. For example, the evaluation unit assigns a score based on the evaluation criteria. Step 5: The feedback unit provides feedback based on the results of the evaluation by the evaluation unit. For example, the feedback unit provides specific feedback to the new employee based on the evaluation results.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0184] 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.

[0185] [Explanation of symbols]

[0186] 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 selection section for selecting training content; a generation unit that generates a training scenario based on the content selected by the selection unit; a simulation unit that asks questions and performs simulations based on the scenario generated by the generation unit; An evaluation department that evaluates the answers and actions of new recruits; a feedback unit that provides feedback based on the result of the evaluation by the evaluation unit. A system characterized by:

2. The selection unit We provide training menus for customer service training, product knowledge training, and complaint handling training.

2. The system of claim 1.

3. The generation unit Generate training scenarios based on past training data or actual customer service data 2. The system of claim 1.

4. The simulation unit Provide a simulation in which AI plays the role of a customer and new employees handle customer service.

2. The system of claim 1.

5. The evaluation unit Evaluate new recruits' responses and behaviors and assign them a score based on the evaluation criteria 2. The system of claim 1.

6. The feedback unit Provide specific feedback to new hires based on the evaluation results 2. The system of claim 1.

7. The selection unit Estimate the emotions of newcomers and adjust the display order of training menus based on the estimated emotions of newcomers 2. The system of claim 1.

8. The selection unit Analyze new recruits' past training history and automatically suggest training menus 2. The system of claim 1.

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