system

Abstract

The system according to this embodiment aims to provide efficient and rapid training for new operators. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, and an evaluation unit. The collection unit collects customer interaction history information. The generation unit generates example questions based on the information collected by the collection unit. The evaluation unit evaluates the answers of new operators to the example questions generated by the generation unit and provides advice.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that a great deal of manpower and time are required for the education of new operators, and efficient education is difficult.

[0005] The system according to the embodiment aims to efficiently and in a short period of time educate new operators.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, a generation unit, and an evaluation unit. The collection unit collects response history information with customers. The generation unit generates question examples based on the information collected by the collection unit. The evaluation unit evaluates the answers of new operators to the question examples generated by the generation unit and gives advice.

Effects of the Invention

[0007] The system according to this embodiment can efficiently and quickly train new operators. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The educational support system according to an embodiment of the present invention is a system for streamlining the training of new operators in a call center. This educational support system collects customer interaction history information, and an AI automatically generates example questions and speaks to the new operator. When the new operator answers, the AI ​​judges the appropriateness of the answer and provides advice. At this time, the system categorizes cases into patterns such as complaint cases, billing inquiry cases, and cancellation request cases to achieve efficient training. As a result, the educational support system can complete new employee training efficiently and in a short period of time, and improve the confidence of the operators. It also improves the satisfaction of users who call. For example, if a new operator has had sufficient practice in handling complaints, they will be able to handle actual complaints more smoothly. This improves customer satisfaction and enhances the credibility of the company. Furthermore, this system can be applied to fields other than call centers. For example, it can be used for new employee training in various fields such as bank teller services and interview preparation at cram schools. As a result, it is expected to improve the efficiency of training in a wide range of industries.

[0029] The educational support system according to this embodiment comprises a collection unit, a generation unit, and an evaluation unit. The collection unit collects customer interaction history information. The collection unit can collect, for example, call content, chat history, and email exchanges. The collection unit can use AI to automatically collect this information and store it in a database. The generation unit generates example questions based on the information collected by the collection unit. The generation unit categorizes customer interactions into patterns such as complaint cases, billing inquiries, and cancellation requests, and generates example questions corresponding to each pattern. The generation unit can use AI to learn from past interaction history and generate optimal example questions. The evaluation unit evaluates the responses of new operators to the example questions generated by the generation unit and provides advice. The evaluation unit evaluates, for example, the content of greetings, the accuracy of explanations, speaking speed, and volume. The evaluation unit can use AI to analyze the responses of new operators and provide specific advice. As a result, the educational support system according to this embodiment can streamline the training of new operators based on customer interaction history information.

[0030] The data collection unit collects customer interaction history information. For example, it can collect call content, chat history, and email exchanges. Specifically, call content is converted into text data using speech recognition technology, while chat history and email exchanges are collected directly as text data. This data is automatically collected using AI and stored in a database. The data collection unit can collect data in real time, and the database is updated each time a customer interaction is completed. Furthermore, the data collection unit can perform pre-processing such as noise reduction and misrecognition correction to ensure data quality. For example, since call audio data often contains background noise, a noise reduction filter is applied to obtain clear audio data. Also, if misrecognition occurs during the conversion to text data using speech recognition technology, the AI ​​automatically analyzes the context and corrects it to the correct text. This allows the data collection unit to efficiently collect high-quality data and store it in the database. Furthermore, the data collection unit can centrally manage the collected data and collaborate with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the generation and evaluation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The generation unit generates example questions based on information collected by the collection unit. The generation unit categorizes questions into patterns such as complaints, billing inquiries, and cancellation requests, and generates example questions appropriate to each pattern. Specifically, it uses AI to learn from past interaction history and generates the most suitable example questions for each pattern. For example, in complaint cases, it generates questions to elicit specific customer dissatisfaction and questions to propose solutions. In billing inquiries, it generates questions about the details of billing plans and change procedures. In cancellation requests, it generates questions to confirm the reason for cancellation and to propose solutions to prevent cancellation. The generation unit uses AI to analyze the collected data and generate the most suitable example questions. For example, it uses natural language processing technology to analyze customer statements and generate appropriate questions. Furthermore, the generated example questions are evaluated by comparing them with past interaction history, and modifications and improvements are made as needed. This allows the generation unit to always provide the most suitable example questions based on the latest information. In addition, the generation unit stores the generated example questions in a database, making them accessible to the evaluation unit. This allows the generation unit to efficiently and effectively generate example questions, thereby improving the overall system performance.

[0032] The evaluation unit assesses and advises new operators on their responses to example questions generated by the generation unit. Specifically, it evaluates aspects such as the content of greetings, the accuracy of explanations, speaking speed, and volume. The evaluation unit can use AI to analyze new operators' responses and provide specific advice. For example, it can use speech recognition technology to convert the new operator's statements into text data and use natural language processing technology to evaluate the accuracy and appropriateness of the content. It can also use speech analysis technology to evaluate speaking speed and volume and advise on appropriate speed and volume. Furthermore, based on past evaluation data, the evaluation unit can track the growth of new operators and provide individualized training plans. For example, it can provide additional training to address weaknesses in specific areas or training based on examples of operators with excellent customer service skills. This allows the evaluation unit to support the skill improvement of new operators and enhance the quality of customer service. In addition, the evaluation unit stores the evaluation results in a database, making them accessible to the collection and generation units. This allows the evaluation unit to efficiently and effectively evaluate new operators and improve the overall system performance.

[0033] The generation unit can categorize cases into patterns such as complaint cases, fee inquiry cases, and cancellation request cases. For example, in complaint cases, the generation unit generates example questions based on the type of complaint the customer made. In fee inquiry cases, it generates example questions based on the type of fee inquiry the customer asked. In cancellation request cases, it generates example questions based on the reason the customer requested cancellation. This allows for efficient training through the categorization of example questions. Some or all of the above processing in the generation unit may be performed using AI, or it may be performed without AI. For example, the generation unit can input past interaction history into AI, which can then automatically categorize and generate example questions.

[0034] The evaluation unit can assess the content of greetings, the accuracy of explanations, speaking speed, and volume. For example, the evaluation unit can assess whether the content of greetings is appropriate, whether the explanations are accurate, whether the speaking speed is appropriate, and whether the volume is appropriate. This allows for the evaluation of the new operator's specific skills and clarifies areas for improvement. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the new operator's responses into an AI, which can then automatically perform the evaluation and provide specific advice.

[0035] The evaluation department can provide specific advice based on the evaluation results. For example, if the answer is accurate, the evaluation department will provide feedback such as "This is the correct answer," and if it is incorrect, it will provide advice such as "This part is incorrect." It will also evaluate the content of greetings, the accuracy of explanations, speaking speed, and volume, and provide specific advice. In this way, specific advice based on the evaluation results can support the skill improvement of new operators. Some or all of the above processes in the evaluation department may be performed using AI, or not. For example, the evaluation department can input the new operator's answers into an AI, which can then automatically perform the evaluation and provide specific advice.

[0036] The generation unit can generate example questions that demonstrate applicability to other industries. For example, it can generate example questions for bank teller services or for interview preparation at cram schools. This demonstrates applicability to other industries, thereby improving the system's versatility. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input customer interaction history from other industries into the AI, which can then automatically generate example questions.

[0037] The evaluation unit can set evaluation criteria that demonstrate applicability to other industries. For example, the evaluation unit can set evaluation criteria for bank teller services or for interview preparation at cram schools. This improves the system's versatility by demonstrating its applicability to other industries. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input customer service history from other industries into the AI, which can then automatically set evaluation criteria.

[0038] The data collection unit can analyze past interaction history and select the optimal collection method. For example, the data collection unit can analyze from past interaction history that a large number of inquiries occur during specific time periods and focus its data collection efforts on those times. Based on past interaction history, the data collection unit can analyze that problems frequently occur when a particular operator handles an inquiry and prioritize the collection of that operator's interaction history. The data collection unit can analyze past interaction history to identify that a particular customer frequently files complaints and collect detailed interaction history for that customer. This enables efficient information collection by selecting the optimal collection method based on the analysis of past interaction history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past interaction history data into a generating AI, which can then automatically select the optimal collection method.

[0039] The collection unit can filter customer attribute information when collecting interaction history information. For example, the collection unit can prioritize collecting interaction history information of customers in a specific age group based on the customer's age information. The collection unit can filter and collect interaction history information of customers of a specific gender based on the customer's gender information. The collection unit can prioritize collecting interaction history information of customers in a specific region based on the customer's region information. This allows for the efficient collection of highly relevant information through filtering based on customer attribute information. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input customer attribute information into a generating AI, and the generating AI can automatically filter and collect interaction history information.

[0040] The data collection unit can prioritize the collection of highly relevant information by considering the customer's purchase history when collecting interaction history information. For example, the data collection unit can prioritize the collection of interaction history information related to recently purchased products based on the customer's purchase history. The data collection unit can prioritize the collection of interaction history information related to frequently purchased products based on the customer's purchase history. The data collection unit can prioritize the collection of interaction history information related to high-priced products based on the customer's purchase history. This allows for the efficient collection of highly relevant information by considering the customer's purchase history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input customer purchase history data into a generating AI, which can automatically prioritize the collection of highly relevant information.

[0041] The data collection unit can analyze the customer's social media activity and collect relevant information when collecting interaction history information. For example, the data collection unit can analyze the customer's social media activity and collect relevant interaction history information based on recent posts. The data collection unit can analyze the customer's social media activity and prioritize the collection of interaction history information related to specific topics. The data collection unit can analyze the customer's social media activity and collect relevant interaction history information based on the customer's interests. This allows for the efficient collection of highly relevant information by analyzing the customer's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the customer's social media data into a generating AI, which can then automatically collect relevant information.

[0042] The generation unit can adjust the level of detail in example questions based on the importance of the interaction history when generating example questions. For example, the generation unit generates example questions that include detailed content for important interaction history. For example questions that are less important, the generation unit keeps the content concise. The generation unit adjusts the level of detail in example questions in stages according to the importance of the interaction history. This allows for the provision of appropriate example questions by adjusting the level of detail based on the importance of the interaction history. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input interaction history data into a generation AI, which can automatically evaluate importance and adjust the level of detail in example questions.

[0043] The generation unit can apply different generation algorithms depending on the category of the interaction history when generating example questions. For example, for complaint cases, the generation unit applies a generation algorithm specialized for complaint handling. For fee inquiry cases, the generation unit applies a question generation algorithm related to fees. For cancellation request cases, the generation unit applies a generation algorithm specialized for cancellation handling. This allows for the provision of appropriate example questions by applying a generation algorithm according to the category of the interaction history. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input interaction history data into a generation AI, which can automatically determine the category and apply an appropriate generation algorithm.

[0044] The generation unit can determine the priority of example questions based on the submission timing of the interaction history when generating example questions. For example, the generation unit prioritizes generating example questions based on recent interaction history. The generation unit lowers the priority of example questions based on older interaction history. The generation unit adjusts the order of question generation according to the submission timing of the interaction history. This allows for the provision of appropriate example questions by determining the priority of example questions based on the submission timing of the interaction history. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input interaction history data into a generation AI, which can automatically evaluate the submission timing and determine the priority of example questions.

[0045] The generation unit can adjust the order of example questions based on the relevance of the interaction history when generating example questions. For example, the generation unit prioritizes generating example questions based on highly relevant interaction history. The generation unit postpones generating example questions based on less relevant interaction history. The generation unit adjusts the order of question generation in stages according to the relevance of the interaction history. This allows for the provision of appropriate example questions by adjusting the order of question questions based on the relevance of the interaction history. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input interaction history data into a generation AI, which can automatically evaluate the relevance and adjust the order of example questions.

[0046] The evaluation unit can optimize its evaluation algorithm by referring to past evaluation data when evaluating responses. For example, the evaluation unit can adjust the evaluation algorithm based on past evaluation data to improve accuracy. The evaluation unit analyzes past evaluation data and optimizes the evaluation algorithm based on specific patterns. The evaluation unit refers to past evaluation data, reviews the evaluation criteria, and improves the algorithm. As a result, the accuracy of the evaluation algorithm is improved by referring to past evaluation data. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input past evaluation data into a generating AI, and the generating AI can automatically optimize the evaluation algorithm.

[0047] The evaluation unit can perform evaluations while considering the respondent's attribute information. For example, the evaluation unit may consider the respondent's years of experience and apply stricter evaluation criteria to experienced respondents. The evaluation unit may also consider the respondent's job title and set evaluation criteria appropriate to that title. The evaluation unit may individually adjust the evaluation criteria based on the respondent's attribute information. This makes it possible to perform appropriate evaluations by considering the respondent's attribute information. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit may input the respondent's attribute information into a generating AI, which can then automatically adjust the evaluation criteria.

[0048] The evaluation unit can perform evaluations while considering the respondent's geographical location information. For example, the evaluation unit can apply region-specific evaluation criteria based on the respondent's geographical location information. The evaluation unit sets region-specific evaluation criteria considering the respondent's geographical location information. The evaluation unit adjusts the evaluation criteria based on the respondent's geographical location information and performs the evaluation. This allows for the application of region-specific evaluation criteria by considering the respondent's geographical location information. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the respondent's geographical location information into a generating AI, which can then automatically adjust the evaluation criteria.

[0049] The evaluation unit can improve the accuracy of its evaluations by referring to relevant literature and materials when evaluating responses. For example, the evaluation unit can refer to relevant literature and revise the evaluation criteria to improve accuracy. The evaluation unit can improve its evaluation algorithm based on relevant materials to perform more accurate evaluations. The evaluation unit can refer to literature and materials and update the evaluation criteria to incorporate the latest knowledge. As a result, the accuracy of the evaluation is improved by referring to relevant literature and materials. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input relevant literature and materials into a generating AI, and the generating AI can automatically update the evaluation criteria.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The training support system can also include a feedback section. This section provides individualized feedback based on evaluation results of new operators' responses. For example, it can offer specific advice not only on the accuracy of responses but also on the tone and attitude of the interaction. Furthermore, the feedback section can refer to past evaluation results and generate reports that visualize the operator's growth. This allows new operators to track their progress and maintain motivation. Additionally, the feedback section can share examples of excellent responses from other operators for learning reference. Through this, the training support system can assist new operators in improving their skills through individualized feedback and visualization of their growth.

[0052] The training support system can also include a simulation section. This section provides scenarios that simulate actual customer interactions, allowing new operators to practice practical skills. For example, the simulation section can provide scenarios for handling complaints, allowing new operators to practice appropriate responses. Furthermore, the simulation section can provide various scenarios, such as billing inquiries and cancellation requests, enabling new operators to handle diverse situations. In addition, the simulation section can evaluate each scenario and provide specific feedback. This allows the training support system to help new operators improve their skills through practical simulations.

[0053] The training support system can also include a data analysis unit. This unit analyzes collected call history information in detail to evaluate the effectiveness of training. For example, it can analyze the call history of new operators and visualize changes in their skills before and after training. It can also evaluate how effective a particular training program was and identify areas for improvement. Furthermore, it can objectively evaluate the skill level of new operators by comparing their call history with that of other operators. This allows the training support system to perform data-driven training effectiveness evaluations and support the development of effective training programs.

[0054] The training support system can also include a customization section. This customization section provides training programs tailored to the individual needs of new operators. For example, it can suggest optimal training content based on the new operator's past experience and skill level. Furthermore, it can adjust training methods according to the new operator's learning style. In addition, the customization section continuously updates the training program based on feedback from new operators to support effective learning. This allows the training support system to provide customized training programs tailored to individual needs, thereby supporting the skill development of new operators.

[0055] The training support system can also include a reminder function. This function sends regular training reminders to new operators to encourage continued learning. For example, it can notify new operators of their daily training schedule, helping them to progress systematically. It can also send reminders to review specific training content, reinforcing learning. Furthermore, the reminder function can monitor the progress of new operators and suggest additional training as needed. Through its reminder function, the training support system can help new operators maintain their learning and effectively improve their skills.

[0056] The training support system can also include a community section. The community section provides a platform for new operators to exchange information and opinions. For example, through an online forum, new operators can share questions and concerns and receive advice from other operators. The community section can also hold regular online meetings, providing opportunities for new operators to interact directly. Furthermore, the community section can share examples of excellent customer service and success stories for learning purposes. In this way, the training support system can assist new operators in information exchange and skill development through its community functions.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The data collection unit collects customer interaction history information. The data collection unit can collect, for example, call content, chat history, and email exchanges. The data collection unit can use AI to automatically collect this information and store it in a database. Step 2: The generation unit generates example questions based on the information collected by the collection unit. The generation unit categorizes the questions into patterns such as complaint cases, fee inquiries, and cancellation requests, and generates example questions for each pattern. The generation unit can use AI to learn from past interaction history and generate optimal example questions. Step 3: The evaluation unit evaluates the new operator's answers to the example questions generated by the generation unit and provides advice. The evaluation unit evaluates, for example, the content of the greeting, the accuracy of the explanation, the speed of speaking, and the volume of the voice. The evaluation unit can use AI to analyze the new operator's answers and provide specific advice.

[0059] (Example of form 2) The educational support system according to an embodiment of the present invention is a system for streamlining the training of new operators in a call center. This educational support system collects customer interaction history information, and an AI automatically generates example questions and speaks to the new operator. When the new operator answers, the AI ​​judges the appropriateness of the answer and provides advice. At this time, the system categorizes cases into patterns such as complaint cases, billing inquiry cases, and cancellation request cases to achieve efficient training. As a result, the educational support system can complete new employee training efficiently and in a short period of time, and improve the confidence of the operators. It also improves the satisfaction of users who call. For example, if a new operator has had sufficient practice in handling complaints, they will be able to handle actual complaints more smoothly. This improves customer satisfaction and enhances the credibility of the company. Furthermore, this system can be applied to fields other than call centers. For example, it can be used for new employee training in various fields such as bank teller services and interview preparation at cram schools. As a result, it is expected to improve the efficiency of training in a wide range of industries.

[0060] The educational support system according to this embodiment comprises a collection unit, a generation unit, and an evaluation unit. The collection unit collects customer interaction history information. The collection unit can collect, for example, call content, chat history, and email exchanges. The collection unit can use AI to automatically collect this information and store it in a database. The generation unit generates example questions based on the information collected by the collection unit. The generation unit categorizes customer interactions into patterns such as complaint cases, billing inquiries, and cancellation requests, and generates example questions corresponding to each pattern. The generation unit can use AI to learn from past interaction history and generate optimal example questions. The evaluation unit evaluates the responses of new operators to the example questions generated by the generation unit and provides advice. The evaluation unit evaluates, for example, the content of greetings, the accuracy of explanations, speaking speed, and volume. The evaluation unit can use AI to analyze the responses of new operators and provide specific advice. As a result, the educational support system according to this embodiment can streamline the training of new operators based on customer interaction history information.

[0061] The data collection unit collects customer interaction history information. For example, it can collect call content, chat history, and email exchanges. Specifically, call content is converted into text data using speech recognition technology, while chat history and email exchanges are collected directly as text data. This data is automatically collected using AI and stored in a database. The data collection unit can collect data in real time, and the database is updated each time a customer interaction is completed. Furthermore, the data collection unit can perform pre-processing such as noise reduction and misrecognition correction to ensure data quality. For example, since call audio data often contains background noise, a noise reduction filter is applied to obtain clear audio data. Also, if misrecognition occurs during the conversion to text data using speech recognition technology, the AI ​​automatically analyzes the context and corrects it to the correct text. This allows the data collection unit to efficiently collect high-quality data and store it in the database. Furthermore, the data collection unit can centrally manage the collected data and collaborate with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the generation and evaluation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0062] The generation unit generates example questions based on information collected by the collection unit. The generation unit categorizes questions into patterns such as complaints, billing inquiries, and cancellation requests, and generates example questions appropriate to each pattern. Specifically, it uses AI to learn from past interaction history and generates the most suitable example questions for each pattern. For example, in complaint cases, it generates questions to elicit specific customer dissatisfaction and questions to propose solutions. In billing inquiries, it generates questions about the details of billing plans and change procedures. In cancellation requests, it generates questions to confirm the reason for cancellation and to propose solutions to prevent cancellation. The generation unit uses AI to analyze the collected data and generate the most suitable example questions. For example, it uses natural language processing technology to analyze customer statements and generate appropriate questions. Furthermore, the generated example questions are evaluated by comparing them with past interaction history, and modifications and improvements are made as needed. This allows the generation unit to always provide the most suitable example questions based on the latest information. In addition, the generation unit stores the generated example questions in a database, making them accessible to the evaluation unit. This allows the generation unit to efficiently and effectively generate example questions, thereby improving the overall system performance.

[0063] The evaluation unit assesses and advises new operators on their responses to example questions generated by the generation unit. Specifically, it evaluates aspects such as the content of greetings, the accuracy of explanations, speaking speed, and volume. The evaluation unit can use AI to analyze new operators' responses and provide specific advice. For example, it can use speech recognition technology to convert the new operator's statements into text data and use natural language processing technology to evaluate the accuracy and appropriateness of the content. It can also use speech analysis technology to evaluate speaking speed and volume and advise on appropriate speed and volume. Furthermore, based on past evaluation data, the evaluation unit can track the growth of new operators and provide individualized training plans. For example, it can provide additional training to address weaknesses in specific areas or training based on examples of operators with excellent customer service skills. This allows the evaluation unit to support the skill improvement of new operators and enhance the quality of customer service. In addition, the evaluation unit stores the evaluation results in a database, making them accessible to the collection and generation units. This allows the evaluation unit to efficiently and effectively evaluate new operators and improve the overall system performance.

[0064] The generation unit can categorize cases into patterns such as complaint cases, fee inquiry cases, and cancellation request cases. For example, in complaint cases, the generation unit generates example questions based on the type of complaint the customer made. In fee inquiry cases, it generates example questions based on the type of fee inquiry the customer asked. In cancellation request cases, it generates example questions based on the reason the customer requested cancellation. This allows for efficient training through the categorization of example questions. Some or all of the above processing in the generation unit may be performed using AI, or it may be performed without AI. For example, the generation unit can input past interaction history into AI, which can then automatically categorize and generate example questions.

[0065] The evaluation unit can assess the content of greetings, the accuracy of explanations, speaking speed, and volume. For example, the evaluation unit can assess whether the content of greetings is appropriate, whether the explanations are accurate, whether the speaking speed is appropriate, and whether the volume is appropriate. This allows for the evaluation of the new operator's specific skills and clarifies areas for improvement. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the new operator's responses into an AI, which can then automatically perform the evaluation and provide specific advice.

[0066] The evaluation department can provide specific advice based on the evaluation results. For example, if the answer is accurate, the evaluation department will provide feedback such as "This is the correct answer," and if it is incorrect, it will provide advice such as "This part is incorrect." It will also evaluate the content of greetings, the accuracy of explanations, speaking speed, and volume, and provide specific advice. In this way, specific advice based on the evaluation results can support the skill improvement of new operators. Some or all of the above processes in the evaluation department may be performed using AI, or not. For example, the evaluation department can input the new operator's answers into an AI, which can then automatically perform the evaluation and provide specific advice.

[0067] The generation unit can generate example questions that demonstrate applicability to other industries. For example, it can generate example questions for bank teller services or for interview preparation at cram schools. This demonstrates applicability to other industries, thereby improving the system's versatility. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input customer interaction history from other industries into the AI, which can then automatically generate example questions.

[0068] The evaluation unit can set evaluation criteria that demonstrate applicability to other industries. For example, the evaluation unit can set evaluation criteria for bank teller services or for interview preparation at cram schools. This improves the system's versatility by demonstrating its applicability to other industries. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input customer service history from other industries into the AI, which can then automatically set evaluation criteria.

[0069] The data collection unit can estimate the user's emotions and adjust the timing of collecting interaction history information based on the estimated emotions. For example, if the user is angry, the data collection unit will collect interaction history information urgently to understand the situation requiring a quick response. If the user is relaxed, the data collection unit will collect interaction history information regularly to analyze long-term trends. If the user is feeling anxious, the data collection unit will collect interaction history information frequently to consider countermeasures to alleviate the user's anxiety. This allows for appropriate information collection by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's voice data into a generative AI, which can automatically estimate emotions and adjust the collection timing.

[0070] The data collection unit can analyze past interaction history and select the optimal collection method. For example, the data collection unit can analyze from past interaction history that a large number of inquiries occur during specific time periods and focus its data collection efforts on those times. Based on past interaction history, the data collection unit can analyze that problems frequently occur when a particular operator handles an inquiry and prioritize the collection of that operator's interaction history. The data collection unit can analyze past interaction history to identify that a particular customer frequently files complaints and collect detailed interaction history for that customer. This enables efficient information collection by selecting the optimal collection method based on the analysis of past interaction history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past interaction history data into a generating AI, which can then automatically select the optimal collection method.

[0071] The collection unit can filter customer attribute information when collecting interaction history information. For example, the collection unit can prioritize collecting interaction history information of customers in a specific age group based on the customer's age information. The collection unit can filter and collect interaction history information of customers of a specific gender based on the customer's gender information. The collection unit can prioritize collecting interaction history information of customers in a specific region based on the customer's region information. This allows for the efficient collection of highly relevant information through filtering based on customer attribute information. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input customer attribute information into a generating AI, and the generating AI can automatically filter and collect interaction history information.

[0072] The data collection unit can estimate the user's emotions and determine the priority of the interaction history information to collect based on the estimated emotions. For example, if the user is angry, the data collection unit will prioritize collecting that interaction history information to understand the situation requiring a quick response. If the user is relaxed, the data collection unit will postpone collecting that interaction history information and prioritize collecting other more urgent information. If the user is feeling anxious, the data collection unit will prioritize collecting that interaction history information to consider countermeasures to alleviate the user's anxiety. This allows for the priority collection of important information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's voice data into a generative AI, which can automatically estimate emotions and determine the priority of the interaction history information to collect.

[0073] The data collection unit can prioritize the collection of highly relevant information by considering the customer's purchase history when collecting interaction history information. For example, the data collection unit can prioritize the collection of interaction history information related to recently purchased products based on the customer's purchase history. The data collection unit can prioritize the collection of interaction history information related to frequently purchased products based on the customer's purchase history. The data collection unit can prioritize the collection of interaction history information related to high-priced products based on the customer's purchase history. This allows for the efficient collection of highly relevant information by considering the customer's purchase history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input customer purchase history data into a generating AI, which can automatically prioritize the collection of highly relevant information.

[0074] The data collection unit can analyze the customer's social media activity and collect relevant information when collecting interaction history information. For example, the data collection unit can analyze the customer's social media activity and collect relevant interaction history information based on recent posts. The data collection unit can analyze the customer's social media activity and prioritize the collection of interaction history information related to specific topics. The data collection unit can analyze the customer's social media activity and collect relevant interaction history information based on the customer's interests. This allows for the efficient collection of highly relevant information by analyzing the customer's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the customer's social media data into a generating AI, which can then automatically collect relevant information.

[0075] The generation unit can estimate the user's emotions and adjust the wording of example questions based on the estimated emotions. For example, if the user is angry, the generation unit will soften the wording of the example questions to soothe the user's emotions. If the user is relaxed, the generation unit will make the wording of the example questions friendly to create a more approachable atmosphere. If the user is anxious, the generation unit will make the wording of the example questions polite to alleviate the user's anxiety. This allows for the provision of appropriate example questions by adjusting the wording of the example questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's voice data into the generation AI, which can automatically estimate emotions and adjust the wording of example questions.

[0076] The generation unit can adjust the level of detail in example questions based on the importance of the interaction history when generating example questions. For example, the generation unit generates example questions that include detailed content for important interaction history. For example questions that are less important, the generation unit keeps the content concise. The generation unit adjusts the level of detail in example questions in stages according to the importance of the interaction history. This allows for the provision of appropriate example questions by adjusting the level of detail based on the importance of the interaction history. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input interaction history data into a generation AI, which can automatically evaluate importance and adjust the level of detail in example questions.

[0077] The generation unit can apply different generation algorithms depending on the category of the interaction history when generating example questions. For example, for complaint cases, the generation unit applies a generation algorithm specialized for complaint handling. For fee inquiry cases, the generation unit applies a question generation algorithm related to fees. For cancellation request cases, the generation unit applies a generation algorithm specialized for cancellation handling. This allows for the provision of appropriate example questions by applying a generation algorithm according to the category of the interaction history. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input interaction history data into a generation AI, which can automatically determine the category and apply an appropriate generation algorithm.

[0078] The generation unit can estimate the user's emotions and adjust the length of sample questions based on the estimated emotions. For example, if the user is in a hurry, the generation unit shortens the sample questions to allow for quick answers. If the user is relaxed, the generation unit lengthens the sample questions to provide more detailed information. If the user is anxious, the generation unit adjusts the sample questions to an appropriate length to alleviate the user's anxiety. This allows for the provision of appropriate sample questions by adjusting their length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's voice data into the generation AI, which can automatically estimate emotions and adjust the length of sample questions.

[0079] The generation unit can determine the priority of example questions based on the submission timing of the interaction history when generating example questions. For example, the generation unit prioritizes generating example questions based on recent interaction history. The generation unit lowers the priority of example questions based on older interaction history. The generation unit adjusts the order of question generation according to the submission timing of the interaction history. This allows for the provision of appropriate example questions by determining the priority of example questions based on the submission timing of the interaction history. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input interaction history data into a generation AI, which can automatically evaluate the submission timing and determine the priority of example questions.

[0080] The generation unit can adjust the order of example questions based on the relevance of the interaction history when generating example questions. For example, the generation unit prioritizes generating example questions based on highly relevant interaction history. The generation unit postpones generating example questions based on less relevant interaction history. The generation unit adjusts the order of question generation in stages according to the relevance of the interaction history. This allows for the provision of appropriate example questions by adjusting the order of question questions based on the relevance of the interaction history. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input interaction history data into a generation AI, which can automatically evaluate the relevance and adjust the order of example questions.

[0081] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria for the response based on the estimated emotions. For example, if the user is angry, the evaluation unit will apply strict evaluation criteria and demand an accurate response. If the user is relaxed, the evaluation unit will apply flexible evaluation criteria and evaluate a friendly response. If the user is feeling anxious, the evaluation unit will apply evaluation criteria that emphasize a polite response. This allows for appropriate evaluation by adjusting the evaluation criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the user's voice data into a generative AI, which can automatically estimate emotions and adjust the evaluation criteria.

[0082] The evaluation unit can optimize its evaluation algorithm by referring to past evaluation data when evaluating responses. For example, the evaluation unit can adjust the evaluation algorithm based on past evaluation data to improve accuracy. The evaluation unit analyzes past evaluation data and optimizes the evaluation algorithm based on specific patterns. The evaluation unit refers to past evaluation data, reviews the evaluation criteria, and improves the algorithm. As a result, the accuracy of the evaluation algorithm is improved by referring to past evaluation data. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input past evaluation data into a generating AI, and the generating AI can automatically optimize the evaluation algorithm.

[0083] The evaluation unit can perform evaluations while considering the respondent's attribute information. For example, the evaluation unit may consider the respondent's years of experience and apply stricter evaluation criteria to experienced respondents. The evaluation unit may also consider the respondent's job title and set evaluation criteria appropriate to that title. The evaluation unit may individually adjust the evaluation criteria based on the respondent's attribute information. This makes it possible to perform appropriate evaluations by considering the respondent's attribute information. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit may input the respondent's attribute information into a generating AI, which can then automatically adjust the evaluation criteria.

[0084] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation results based on the estimated user emotions. For example, if the user is nervous, the evaluation unit provides a simple and highly visible display method. If the user is relaxed, the evaluation unit provides a display method that includes detailed information. If the user is in a hurry, the evaluation unit provides a display method that gets straight to the point. This allows for the display of appropriate evaluation results by adjusting the display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the user's voice data into the generative AI, which can automatically estimate emotions and adjust the display method of the evaluation results.

[0085] The evaluation unit can perform evaluations while considering the respondent's geographical location information. For example, the evaluation unit can apply region-specific evaluation criteria based on the respondent's geographical location information. The evaluation unit sets region-specific evaluation criteria considering the respondent's geographical location information. The evaluation unit adjusts the evaluation criteria based on the respondent's geographical location information and performs the evaluation. This allows for the application of region-specific evaluation criteria by considering the respondent's geographical location information. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the respondent's geographical location information into a generating AI, which can then automatically adjust the evaluation criteria.

[0086] The evaluation unit can improve the accuracy of its evaluations by referring to relevant literature and materials when evaluating responses. For example, the evaluation unit can refer to relevant literature and revise the evaluation criteria to improve accuracy. The evaluation unit can improve its evaluation algorithm based on relevant materials to perform more accurate evaluations. The evaluation unit can refer to literature and materials and update the evaluation criteria to incorporate the latest knowledge. As a result, the accuracy of the evaluation is improved by referring to relevant literature and materials. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input relevant literature and materials into a generating AI, and the generating AI can automatically update the evaluation criteria.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The training support system can also include a feedback section. This section provides individualized feedback based on evaluation results of new operators' responses. For example, it can offer specific advice not only on the accuracy of responses but also on the tone and attitude of the interaction. Furthermore, the feedback section can refer to past evaluation results and generate reports that visualize the operator's growth. This allows new operators to track their progress and maintain motivation. Additionally, the feedback section can share examples of excellent responses from other operators for learning reference. Through this, the training support system can assist new operators in improving their skills through individualized feedback and visualization of their growth.

[0089] The training support system can also include a simulation section. This section provides scenarios that simulate actual customer interactions, allowing new operators to practice practical skills. For example, the simulation section can provide scenarios for handling complaints, allowing new operators to practice appropriate responses. Furthermore, the simulation section can provide various scenarios, such as billing inquiries and cancellation requests, enabling new operators to handle diverse situations. In addition, the simulation section can evaluate each scenario and provide specific feedback. This allows the training support system to help new operators improve their skills through practical simulations.

[0090] The training support system can further generate response scenarios based on the user's emotions using emotion estimation functionality. For example, if the user is angry, the system will generate a scenario requesting a calm and polite response. If the user is relaxed, it will generate a scenario requesting a friendly and approachable response. If the user is feeling anxious, it will generate a scenario requesting a reassuring response. This allows new operators to learn appropriate responses based on the user's emotions. Emotion estimation functionality is implemented using, for example, voice analysis or facial recognition technology. This enables the training support system to provide response scenarios based on the user's emotions and help improve the skills of new operators.

[0091] The training support system can further monitor the emotions of new operators using emotion estimation capabilities and suggest breaks at appropriate times. For example, if a new operator is feeling stressed, the system will suggest a break to provide time to refresh. If the new operator is tired, the system will suggest a short break to allow them to regain their concentration. If the new operator is relaxed, the system will suggest continuing the training. This allows new operators to take breaks at appropriate times and progress through training efficiently. The emotion estimation capability is implemented using, for example, voice analysis or biosensors. This enables the training support system to suggest breaks based on the new operator's emotions, thereby improving the efficiency of training.

[0092] The training support system can further analyze interaction history based on the user's emotions using emotion estimation functionality. For example, if a user is angry, the system can analyze the interaction history in detail to identify problems. If a user is relaxed, the system can analyze the interaction history and share it with other operators as a success story. If a user is anxious, the system can analyze the interaction history to find areas for improvement. In this way, the training support system can analyze interaction history based on the user's emotions and help improve operators' skills. Emotion estimation functionality is implemented using technologies such as voice analysis and text analysis. This allows the training support system to analyze interaction history based on the user's emotions and provide effective feedback.

[0093] The training support system can further utilize emotion estimation capabilities to adjust training content based on the emotions of new operators. For example, if a new operator is stressed, the system will provide training at a lower difficulty level, gradually improving their skills. If a new operator is relaxed, the system will provide training at a higher difficulty level, stimulating their sense of challenge. If a new operator is tired, the system will provide training that can be completed in a short time, allowing for efficient learning. In this way, the training support system can adjust training content according to the emotions of new operators, supporting effective learning. Emotion estimation capabilities are implemented, for example, using voice analysis or biosensors. This allows the training support system to adjust training content based on the emotions of new operators, supporting skill improvement.

[0094] The training support system can also include a data analysis unit. This unit analyzes collected call history information in detail to evaluate the effectiveness of training. For example, it can analyze the call history of new operators and visualize changes in their skills before and after training. It can also evaluate how effective a particular training program was and identify areas for improvement. Furthermore, it can objectively evaluate the skill level of new operators by comparing their call history with that of other operators. This allows the training support system to perform data-driven training effectiveness evaluations and support the development of effective training programs.

[0095] The training support system can also include a customization section. This customization section provides training programs tailored to the individual needs of new operators. For example, it can suggest optimal training content based on the new operator's past experience and skill level. Furthermore, it can adjust training methods according to the new operator's learning style. In addition, the customization section continuously updates the training program based on feedback from new operators to support effective learning. This allows the training support system to provide customized training programs tailored to individual needs, thereby supporting the skill development of new operators.

[0096] The training support system can also include a reminder function. This function sends regular training reminders to new operators to encourage continued learning. For example, it can notify new operators of their daily training schedule, helping them to progress systematically. It can also send reminders to review specific training content, reinforcing learning. Furthermore, the reminder function can monitor the progress of new operators and suggest additional training as needed. Through its reminder function, the training support system can help new operators maintain their learning and effectively improve their skills.

[0097] The training support system can also include a community section. The community section provides a platform for new operators to exchange information and opinions. For example, through an online forum, new operators can share questions and concerns and receive advice from other operators. The community section can also hold regular online meetings, providing opportunities for new operators to interact directly. Furthermore, the community section can share examples of excellent customer service and success stories for learning purposes. In this way, the training support system can assist new operators in information exchange and skill development through its community functions.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The data collection unit collects customer interaction history information. The data collection unit can collect, for example, call content, chat history, and email exchanges. The data collection unit can use AI to automatically collect this information and store it in a database. Step 2: The generation unit generates example questions based on the information collected by the collection unit. The generation unit categorizes the questions into patterns such as complaint cases, fee inquiries, and cancellation requests, and generates example questions for each pattern. The generation unit can use AI to learn from past interaction history and generate optimal example questions. Step 3: The evaluation unit evaluates the new operator's answers to the example questions generated by the generation unit and provides advice. The evaluation unit evaluates, for example, the content of the greeting, the accuracy of the explanation, the speed of speaking, and the volume of the voice. The evaluation unit can use AI to analyze the new operator's answers and provide specific advice.

[0100] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0101] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0102] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0103] Each of the multiple elements described above, including the collection unit, generation unit, and evaluation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects customer interaction history information using the camera 42 and microphone 38B of the smart device 14 and stores it in the database 24 by the control unit 46A. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates example questions based on the collected information. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12 and evaluates the answers of new operators and provides advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] As shown in Figure 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.

[0106] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0110] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0111] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0112] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0113] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0114] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0116] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0118] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] Each of the multiple elements described above, including the collection unit, generation unit, and evaluation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects customer interaction history information using the camera 42 and microphone 238 of the smart glasses 214 and stores it in the database 24 by the control unit 46A. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and generates example questions based on the collected information. The evaluation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and evaluates the answers of new operators and provides advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0123] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0124] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0127] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0128] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the collection unit, generation unit, and evaluation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects customer interaction history information using the camera 42 and microphone 238 of the headset terminal 314 and stores it in the database 24 by the control unit 46A. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates example questions based on the collected information. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12 and evaluates the answers of new operators and provides advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0144] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the collection unit, generation unit, and evaluation unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects customer interaction history information using the camera 42 and microphone 238 of the robot 414 and stores it in the database 24 by the control unit 46A. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates example questions based on the collected information. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and evaluates the answers of new operators and provides advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0153] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0155] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0156] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0157] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0161] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0162] 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.

[0163] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0164] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0165] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0166] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0168] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0169] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0170] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0171] (Note 1) A collection unit that collects customer interaction history information, A generation unit generates example questions based on the information collected by the collection unit, The system includes an evaluation unit that evaluates the responses of new operators to example questions generated by the generation unit and provides advice. A system characterized by the following features. (Note 2) The generating unit is We categorize cases into patterns such as complaints, fee inquiries, and cancellation requests. The system described in Appendix 1, characterized by the features described herein. (Note 3) The evaluation unit, The evaluation will cover aspects such as the content of the greeting, the accuracy of the explanation, the speaking speed, and the volume of the voice. The system described in Appendix 1, characterized by the features described herein. (Note 4) The evaluation unit, Provide specific advice based on the evaluation results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Generate example questions that demonstrate applicability to other industries. The system described in Appendix 1, characterized by the features described herein. (Note 6) The evaluation unit, Set evaluation criteria that demonstrate applicability to other industries. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting interaction history information based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze past interaction history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting customer interaction history information, filtering is performed based on customer attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the user's emotions and prioritizes the collection of interaction history information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting customer interaction history information, the system prioritizes collecting highly relevant information by considering the customer's purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting customer interaction history information, we analyze the customer's social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is The system estimates the user's emotions and adjusts the wording of example questions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating example questions, the level of detail in the example questions is adjusted based on the importance of the interaction history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating example questions, different generation algorithms are applied depending on the category of the interaction history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is The system estimates the user's emotions and adjusts the length of example questions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating example questions, the priority of the example questions is determined based on when the response history was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating example questions, the order of the example questions is adjusted based on the relevance of the interaction history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit, The system estimates the user's emotions and adjusts the evaluation criteria for responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit, When evaluating responses, the evaluation algorithm is optimized by referring to past evaluation data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, When evaluating responses, the respondent's attribute information should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, The system estimates the user's emotions and adjusts how the evaluation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, When evaluating responses, the geographical location information of the respondents will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit, When evaluating responses, we improve the accuracy of the evaluation by referring to relevant literature and materials. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects customer interaction history information, A generation unit generates example questions based on the information collected by the collection unit, The system includes an evaluation unit that evaluates the responses of new operators to example questions generated by the generation unit and provides advice. A system characterized by the following features.

2. The generating unit is We categorize cases into patterns such as complaints, fee inquiries, and cancellation requests. The system according to feature 1.

3. The evaluation unit, The evaluation will cover aspects such as the content of the greeting, the accuracy of the explanation, the speaking speed, and the volume of the voice. The system according to feature 1.

4. The evaluation unit, Provide specific advice based on the evaluation results. The system according to feature 1.

5. The generating unit is Generate example questions that demonstrate applicability to other industries. The system according to feature 1.

6. The evaluation unit, Set evaluation criteria that demonstrate applicability to other industries. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting interaction history information based on the estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze past interaction history and select the optimal data collection method. The system according to feature 1.

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