System

Abstract

An object of the system according to the embodiment is to automate the process from conversion of speech data into text to response evaluation and feedback provision.SOLUTION: A system includes an acquisition unit, an evaluation unit, a feedback unit, and an automation unit. The acquisition unit converts the voice data into text. The evaluation unit analyzes the text data acquired by the acquisition unit and evaluates the quality of the dialogue. The feedback part clarifies the improvement point of the reception based on the evaluation result obtained by the evaluation part, and provides feedback to the employee. The automation unit automates a series of processes from the acquisition unit to the evaluation unit and the feedback unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the process of converting voice data into text and evaluating responses was often done manually, which resulted in low efficiency.

[0005] The system according to the embodiment aims to automate the process from converting voice data into text to evaluating the response and providing feedback. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an evaluation unit, a feedback unit, and an automation unit. The acquisition unit converts voice data into text. The evaluation unit analyzes the text data acquired by the acquisition unit and evaluates the quality of the service. The feedback unit identifies areas for improvement in the service based on the evaluation results obtained by the evaluation unit and provides feedback to the employee. The automation unit automates the entire process from the acquisition unit to the evaluation unit and feedback unit. [Effects of the Invention]

[0007] The system according to the embodiment can automate the process from converting voice data into text to evaluating the response and providing feedback. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An embodiment of the customer service evaluation system of the present invention is a system that acquires voice data converted to text using a voice recognition system and evaluates the quality of customer service using a chat generation AI. The customer service evaluation system acquires voice data converted to text using a voice recognition system and evaluates the quality of customer service using a chat generation AI. Furthermore, this evaluation process is automated using RPA to evaluate all customer service. For example, the customer service evaluation system converts customer service speech at a call center into text using a voice recognition system and acquires it as an L2 masked audio source. The acquired text data is then input into a chat generation AI to evaluate the quality of the customer service. For example, the system evaluates whether the content of the service is appropriate and whether appropriate answers are given to customer questions. Furthermore, this evaluation process is automated using RPA. For example, all customer service speech at a call center is converted into text and the chat generation AI automatically evaluates it. This allows the customer service evaluation system to evaluate all customer service responses. This allows the customer service evaluation system to quickly provide feedback to improve the quality of customer service. For example, based on the evaluation results, areas for improvement in customer service can be identified and appropriate guidance can be provided to employees.

[0029] The service evaluation system according to the embodiment includes an acquisition unit, an evaluation unit, a feedback unit, and an automation unit. The acquisition unit converts voice data into text. For example, the acquisition unit converts voice data into text using a voice recognition system. The acquisition unit can also use deep learning-based voice recognition technology to convert voice data into text with high accuracy. The acquisition unit can also select an appropriate text conversion method depending on the content of the voice data. For example, when converting conversational voice into text, the acquisition unit can use noise reduction technology to improve the quality of the voice data. The evaluation unit uses a chat generation AI to analyze the text data acquired by the acquisition unit and evaluate the quality of the service. The evaluation unit can also analyze the text data using, for example, a text generation AI (e.g., LLM). The evaluation unit can also evaluate whether the content of the service is appropriate, whether an appropriate answer is given to a customer's question, etc. The evaluation unit can also use natural language processing technology to evaluate the quality of the service. For example, the evaluation unit can understand the context of the text data and evaluate the appropriateness of the service. The feedback department identifies areas for improvement in customer service based on the evaluation results obtained by the evaluation department and provides feedback to employees. For example, the feedback department clarifies areas for improvement in customer service based on the evaluation results. The feedback department can also suggest specific improvement methods to employees. Furthermore, the feedback department can provide the evaluation results to employees in real time. For example, the feedback department notifies employees of the evaluation results through a web application or mobile application. The automation department automates the entire process from the acquisition department to the evaluation department and feedback department. For example, the automation department uses RPA to automatically perform the entire process from acquiring text data to evaluating customer service and providing feedback. The automation department can also schedule and manage the evaluation process to evaluate all customer service. Furthermore, the automation department can optimize the evaluation algorithm to improve the efficiency of the evaluation process. For example, the automation department adjusts the evaluation algorithm based on past evaluation data to improve the accuracy of the evaluation.As a result, the service evaluation system according to the embodiment automates a series of processes from acquiring voice data to evaluating the service and providing feedback, and can evaluate all services.

[0030] The acquisition unit can convert the voice data into text using a voice recognition system. Voice recognition systems include, but are not limited to, deep learning-based voice recognition technology and rule-based voice recognition technology. The acquisition unit can convert the voice data into text using, for example, a voice recognition system. The acquisition unit can also use deep learning-based voice recognition technology to convert the voice data into text with high accuracy. Furthermore, the acquisition unit can select an appropriate text conversion method depending on the content of the voice data. For example, when converting conversational voice into text, the acquisition unit can improve the quality of the voice data using noise reduction technology. This allows the voice data to be accurately converted into text using a voice recognition system. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input the voice data acquired by the voice recognition system into a generation AI and have the generation AI perform text conversion.

[0031] The evaluation unit can analyze the text data using a chat generation AI and evaluate the quality of the service. Examples of chat generation AI include, but are not limited to, text generation AI (e.g., LLM). The evaluation unit analyzes the text data using, for example, a text generation AI (e.g., LLM). The evaluation unit can also evaluate whether the content of the service is appropriate, whether an appropriate answer was given to a customer's question, and so on. Furthermore, the evaluation unit can use natural language processing technology to evaluate the quality of the service. For example, the evaluation unit can understand the context of the text data and evaluate the appropriateness of the service. This allows the use of the chat generation AI to accurately evaluate the quality of the service. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input text data into the generation AI and have the generation AI evaluate the quality of the service.

[0032] The feedback unit can identify areas for improvement in customer service based on the evaluation results and provide feedback to the employee. For example, the feedback unit can clarify areas for improvement in customer service based on the evaluation results. The feedback unit can also suggest specific improvement methods to the employee. Furthermore, the feedback unit can provide the evaluation results to the employee in real time. For example, the feedback unit can notify the employee of the evaluation results via a web application or a mobile application. This makes it possible to identify areas for improvement in customer service based on the evaluation results and provide appropriate feedback to the employee. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the evaluation results to a generation AI, which can identify areas for improvement in customer service and provide feedback.

[0033] The automation unit can automate a series of processes from the acquisition unit to the evaluation unit and feedback unit. The automation unit can, for example, use RPA to automatically perform a series of processes from acquiring text data to evaluating responses and providing feedback. The automation unit can also schedule and manage the evaluation process to evaluate all responses. The automation unit can also optimize the evaluation algorithm to improve the efficiency of the evaluation process. For example, the automation unit can adjust the evaluation algorithm based on past evaluation data to improve the accuracy of the evaluation. This allows evaluation to be performed for all responses by automating the series of processes. Some or all of the above-mentioned processing in the automation unit may be performed using, for example, AI, or may be performed without using AI. For example, the automation unit can have a generation AI perform schedule management for the evaluation process and optimize the evaluation algorithm.

[0034] When acquiring voice data, the acquisition unit can analyze the user's past response history and select the optimal acquisition method. For example, the acquisition unit may acquire voice data using a similar method, referring to a response method that the user has previously rated highly. The acquisition unit may also acquire voice data using a different method, avoiding a response method that the user has previously rated poorly. Furthermore, the acquisition unit can select the optimal acquisition method for a specific time period from the user's past response history. In this way, the optimal acquisition method can be selected by analyzing the user's past response history. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input the user's past response history data into a generation AI, causing the generation AI to select the optimal acquisition method.

[0035] When acquiring voice data, the acquisition unit can perform filtering based on the user's current situation and areas of interest. For example, the acquisition unit preferentially acquires voice data related to topics in which the user is currently interested. The acquisition unit can also acquire appropriate voice data depending on the user's current situation (e.g., at work, on a break, etc.). Furthermore, the acquisition unit can filter and acquire highly relevant voice data based on the user's areas of interest. In this way, highly relevant data can be acquired by filtering the voice data based on the user's current situation and areas of interest. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's current situation and area of ​​interest data to a generation AI, and have the generation AI filter the voice data.

[0036] When acquiring voice data, the acquisition unit can select the optimal acquisition means depending on the user's input method. For example, if the user is using voice input, the acquisition unit prioritizes acquiring voice data. Also, if the user is using text input, the acquisition unit can also prioritize acquiring text data. Furthermore, if the user is using image input, the acquisition unit can analyze the image data and acquire related voice data. This allows voice data to be acquired efficiently by selecting the optimal acquisition means depending on the user's input method. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit inputs the user's input method data into a generation AI, causing the generation AI to select the optimal acquisition means.

[0037] When acquiring voice data, the acquisition unit can prioritize acquiring highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the acquisition unit prioritizes acquiring voice data related to that area. Furthermore, when the user is moving, the acquisition unit can also acquire optimal voice data based on the user's current location. Furthermore, when the user is in a specific location, the acquisition unit can also prioritize acquiring voice data related to that location. In this way, highly relevant data can be acquired preferentially by taking the user's geographical location information into account. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information data to the generation AI, causing the generation AI to prioritize acquiring highly relevant data.

[0038] When acquiring the voice data, the acquisition unit can analyze the user's social media activity and acquire related data. For example, the acquisition unit can acquire voice data related to places where the user has checked in on social media. The acquisition unit can also analyze the content of the user's social media posts and acquire related voice data. Furthermore, the acquisition unit can also acquire related voice data by referring to the activities of the user's friends on social media. In this way, related data can be efficiently acquired by analyzing the user's social media activity. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's social media activity data into a generation AI and cause the generation AI to acquire related voice data.

[0039] When acquiring voice data, the acquisition unit can customize the acquisition method by reflecting the user's past feedback. For example, the acquisition unit preferentially uses an acquisition method that the user has previously given a high rating. The acquisition unit can also acquire voice data by a different method, avoiding an acquisition method that the user has previously given a low rating. Furthermore, the acquisition unit can customize the optimal acquisition method based on the user's past feedback. In this way, the optimal acquisition method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past feedback data into a generation AI and have the generation AI customize the acquisition method.

[0040] The evaluation unit can adjust the level of detail of the evaluation based on the importance of the response during evaluation. For example, the evaluation unit performs a detailed evaluation for important responses. The evaluation unit can also perform a concise evaluation for general responses. Furthermore, the evaluation unit can adjust the level of detail of the evaluation depending on the importance of the response. In this way, by adjusting the level of detail of the evaluation based on the importance of the response, detailed evaluation can be performed for important responses. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input response importance data into a generation AI and have the generation AI adjust the level of detail of the evaluation.

[0041] The evaluation unit can apply different evaluation algorithms depending on the response category during evaluation. For example, the evaluation unit can apply a specialized evaluation algorithm to responses to technical questions. The evaluation unit can also apply a simple evaluation algorithm to responses to general questions. Furthermore, the evaluation unit can select the optimal evaluation algorithm depending on the response category. This allows the accuracy of the evaluation to be improved by applying the optimal evaluation algorithm depending on the response category. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or can be performed without using AI. For example, the evaluation unit can input response category data into a generation AI and have the generation AI select the optimal evaluation algorithm.

[0042] The evaluation unit can improve the accuracy of the evaluation by referring to the user's past evaluation results when making an evaluation. For example, the evaluation unit can improve the accuracy of the evaluation by referring to response methods that the user has previously received high ratings. The evaluation unit can also improve the accuracy of the evaluation by avoiding response methods that the user has previously received low ratings. Furthermore, the evaluation unit can select an optimal evaluation method based on the user's past evaluation results. In this way, the accuracy of the evaluation can be improved by referring to the user's past evaluation results. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the user's past evaluation result data into the generation AI, which can improve the accuracy of the evaluation.

[0043] The evaluation unit can determine the priority of the evaluation based on the time of the response at the time of evaluation. For example, the evaluation unit prioritizes evaluation of recent responses. The evaluation unit can also postpone evaluation of past responses. Furthermore, the evaluation unit can adjust the priority of the evaluation depending on the time of the response. In this way, by determining the priority of the evaluation based on the time of the response, the most recent response can be prioritized for evaluation. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input data on the time of the response to the generation AI and have the generation AI determine the priority of the evaluation.

[0044] The evaluation unit can adjust the order of evaluation based on the relevance of the responses during evaluation. For example, the evaluation unit prioritizes evaluation of important responses. The evaluation unit can also postpone evaluation of general responses. Furthermore, the evaluation unit can adjust the order of evaluation according to the relevance of the responses. In this way, by adjusting the order of evaluation based on the relevance of the responses, important responses can be prioritized in evaluation. Some or all of the above-described processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input response relevance data into a generation AI and have the generation AI adjust the order of evaluation.

[0045] During evaluation, the evaluation unit can adjust the use of technical terminology in the evaluation according to the user's level of expertise. For example, if the user has technical expertise, the evaluation unit can use technical terminology to make the evaluation. Furthermore, if the user does not have technical expertise, the evaluation unit can also use simple terminology to make the evaluation. Furthermore, the evaluation unit can adjust the use of technical terminology in the evaluation according to the user's level of expertise. This allows for the provision of more understandable evaluation results by adjusting the use of technical terminology in the evaluation according to the user's level of expertise. Some or all of the above-described processing in the evaluation unit may be performed using, or without, AI, for example. For example, the evaluation unit can input the user's level of expertise data into the generation AI, causing the generation AI to adjust the use of technical terminology in the evaluation.

[0046] When providing feedback, the feedback unit can select the optimal feedback method by analyzing the user's past response behavior. For example, the feedback unit can provide feedback in a similar method by referring to response methods that the user has previously received high ratings. The feedback unit can also provide feedback in a different method, avoiding response methods that the user has previously received low ratings. Furthermore, the feedback unit can select the optimal feedback method based on the user's past response behavior. In this way, the optimal feedback method can be selected by analyzing the user's past response behavior. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input data on the user's past response behavior into a generation AI, causing the generation AI to select the optimal feedback method.

[0047] The feedback unit can customize the feedback means based on the user's current situation when providing feedback. For example, when the user is working, the feedback unit can provide brief feedback. Also, when the user is on a break, the feedback unit can provide detailed feedback. Furthermore, the feedback unit can select the optimal feedback means according to the user's current situation. In this way, by customizing the feedback means based on the user's current situation, more appropriate feedback can be provided. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's current situation data into a generation AI and have the generation AI customize the feedback means.

[0048] The feedback unit can improve the feedback method by reflecting the user's feedback when providing feedback. For example, the feedback unit preferentially uses feedback methods that the user has previously given a high rating. The feedback unit can also provide feedback using a different method, avoiding feedback methods that the user has previously given a low rating. Furthermore, the feedback unit can customize the optimal feedback method based on the user's past feedback. In this way, the feedback method can be improved by reflecting the user's feedback. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past feedback data into a generation AI, which can then improve the feedback method.

[0049] When providing feedback, the feedback unit can select the optimal feedback method by taking into account the user's geographical location information. For example, if the user is in a specific area, the feedback unit provides feedback related to that area. Furthermore, if the user is moving, the feedback unit can provide optimal feedback based on the user's current location. Furthermore, if the user is in a specific location, the feedback unit can provide feedback related to that location. In this way, the optimal feedback method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's geographical location information data into a generation AI, causing the generation AI to select the optimal feedback method.

[0050] At the time of providing feedback, the feedback unit can analyze the user's social media activity and suggest a means of providing feedback. For example, the feedback unit can provide feedback related to places where the user has checked in on social media. The feedback unit can also analyze the content of the user's social media posts and provide relevant feedback. Furthermore, the feedback unit can provide relevant feedback by referring to the activities of the user's friends on social media. In this way, relevant feedback can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's social media activity data into a generation AI and have the generation AI suggest a means of providing feedback.

[0051] When providing feedback, the feedback unit can customize the feedback method by reflecting the user's past feedback. For example, the feedback unit preferentially uses feedback methods that the user has previously given a high rating to. The feedback unit can also provide feedback by a different method, avoiding feedback methods that the user has previously given a low rating to. Furthermore, the feedback unit can customize the optimal feedback method based on the user's past feedback. In this way, the optimal feedback method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past feedback data into a generation AI and have the generation AI customize the feedback method.

[0052] During automation, the automation unit can optimize the automation algorithm by referring to past automation data. For example, the automation unit selects an optimal automation algorithm based on past automation data. The automation unit can also select an efficient automation algorithm from past automation data. Furthermore, the automation unit can analyze past automation data and select an optimal automation algorithm. In this way, the automation algorithm can be optimized by referring to past automation data. Some or all of the above-described processing in the automation unit may be performed using, for example, AI, or may be performed without using AI. For example, the automation unit can input past automation data into a generation AI and have the generation AI optimize the automation algorithm.

[0053] The automation unit can update the automation process by reflecting user feedback during automation. For example, the automation unit preferentially uses automation processes that the user has previously rated highly. The automation unit can also perform automation in a different way, avoiding automation processes that the user has previously rated poorly. Furthermore, the automation unit can customize an optimal automation process based on the user's past feedback. This allows the automation process to be updated by reflecting user feedback. Some or all of the above-described processing in the automation unit may be performed using AI, for example, or may be performed without using AI. For example, the automation unit can input the user's past feedback data into a generation AI and have the generation AI update the automation process.

[0054] During automation, the automation unit can weight the automation process based on the time when the response data was acquired. For example, the automation unit can prioritize executing the automation process for recent response data. The automation unit can also postpone executing the automation process for older response data. Furthermore, the automation unit can adjust the weighting of the automation process depending on the time when the response data was acquired. In this way, by weighting the automation process based on the time when the response data was acquired, the most recent data can be processed preferentially. Some or all of the above-mentioned processing in the automation unit may be performed using, for example, AI, or may be performed without using AI. For example, the automation unit can input data on the time when the response data was acquired into the generation AI and have the generation AI weight the automation process.

[0055] During automation, the automation unit can integrate information from different data sources to enhance the automation process. For example, the automation unit can integrate information from different data sources to execute an optimal automation process. The automation unit can also analyze information from different data sources to execute an efficient automation process. Furthermore, the automation unit can enhance the automation process based on information from different data sources. In this way, the automation process can be enhanced by integrating information from different data sources. Some or all of the above-described processing in the automation unit may be performed using, for example, AI, or may be performed without using AI. For example, the automation unit can input information from different data sources into a generation AI, which can enhance the automation process.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] When acquiring voice data, the acquisition unit can analyze the user's past response history and select the optimal acquisition method. For example, the acquisition unit can acquire voice data using a similar method, referring to response methods that the user has previously rated highly. The acquisition unit can also acquire voice data using a different method, avoiding response methods that the user has previously rated poorly. Furthermore, the acquisition unit can select the optimal acquisition method for a specific time period from the user's past response history. In this way, the optimal acquisition method can be selected by analyzing the user's past response history. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past response history data into a generation AI, causing the generation AI to select the optimal acquisition method.

[0058] During evaluation, the evaluation unit can adjust the level of detail of the evaluation based on the importance of the call. For example, a detailed evaluation is made for an important call. The evaluation unit can also make a brief evaluation for a general call. Furthermore, the evaluation unit can adjust the level of detail of the evaluation depending on the importance of the call. In this way, by adjusting the level of detail of the evaluation based on the importance of the call, a detailed evaluation can be made for an important call. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the importance data of the call into a generation AI and have the generation AI adjust the level of detail of the evaluation.

[0059] When providing feedback, the feedback unit can select the optimal feedback method by analyzing the user's past response behavior. For example, the feedback unit can provide feedback in a similar method by referring to response methods that the user has previously received high ratings. The feedback unit can also provide feedback in a different method, avoiding response methods that the user has previously received low ratings. Furthermore, the feedback unit can select the optimal feedback method based on the user's past response behavior. In this way, the optimal feedback method can be selected by analyzing the user's past response behavior. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input data on the user's past response behavior into a generation AI, causing the generation AI to select the optimal feedback method.

[0060] During automation, the automation unit can optimize the automation algorithm by referring to past automation data. For example, the automation unit selects an optimal automation algorithm based on past automation data. The automation unit can also select an efficient automation algorithm from past automation data. Furthermore, the automation unit can analyze past automation data and select an optimal automation algorithm. In this way, the automation algorithm can be optimized by referring to past automation data. Some or all of the above-described processing in the automation unit may be performed using, for example, AI, or may be performed without using AI. For example, the automation unit can input past automation data into a generation AI and have the generation AI optimize the automation algorithm.

[0061] When acquiring voice data, the acquisition unit can filter the voice data based on the user's current situation and areas of interest. For example, the acquisition unit can prioritize acquisition of voice data related to topics in which the user is currently interested. The acquisition unit can also acquire appropriate voice data depending on the user's current situation (e.g., at work, on a break, etc.). Furthermore, the acquisition unit can filter and acquire highly relevant voice data based on the user's areas of interest. In this way, highly relevant data can be acquired by filtering the voice data based on the user's current situation and areas of interest. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's current situation and area of ​​interest data to a generation AI, and have the generation AI filter the voice data.

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

[0063] Step 1: The acquisition unit converts the voice data into text. For example, the acquisition unit converts the voice data into text using a voice recognition system. The acquisition unit can also use deep learning-based voice recognition technology to convert the voice data into text with high accuracy. Furthermore, the acquisition unit can select an appropriate text conversion method depending on the content of the voice data. For example, when converting conversational voice into text, the acquisition unit can use noise reduction technology to improve the quality of the voice data. Step 2: The evaluation unit analyzes the text data acquired by the acquisition unit and evaluates the quality of the service. The evaluation unit analyzes the text data using, for example, a text generation AI (e.g., LLM). The evaluation unit can also evaluate whether the content of the service is appropriate and whether appropriate answers are given to customer questions. Furthermore, the evaluation unit can use natural language processing technology to evaluate the quality of the service. For example, the evaluation unit understands the context of the text data and evaluates the appropriateness of the service. Step 3: The feedback department identifies areas for improvement in customer service based on the evaluation results obtained by the evaluation department and provides feedback to the employee. For example, the feedback department clarifies areas for improvement in customer service based on the evaluation results. The feedback department can also suggest specific ways to improve to the employee. Furthermore, the feedback department can provide the evaluation results to the employee in real time. For example, the feedback department notifies the employee of the evaluation results via a web application or a mobile application. Step 4: The automation unit automates the entire process from the acquisition unit to the evaluation unit and feedback unit. For example, using RPA, the automation unit automates the entire process from acquiring text data to evaluating responses and providing feedback. The automation unit can also schedule and manage the evaluation process to evaluate all responses. Furthermore, the automation unit can optimize the evaluation algorithm to improve the efficiency of the evaluation process. For example, the automation unit can adjust the evaluation algorithm based on past evaluation data to improve the accuracy of the evaluation.

[0064] (Example 2) An embodiment of the customer service evaluation system of the present invention is a system that acquires voice data converted to text using a voice recognition system and evaluates the quality of customer service using a chat generation AI. The customer service evaluation system acquires voice data converted to text using a voice recognition system and evaluates the quality of customer service using a chat generation AI. Furthermore, this evaluation process is automated using RPA to evaluate all customer service. For example, the customer service evaluation system converts customer service speech at a call center into text using a voice recognition system and acquires it as an L2 masked audio source. The acquired text data is then input into a chat generation AI to evaluate the quality of the customer service. For example, the system evaluates whether the content of the service is appropriate and whether appropriate answers are given to customer questions. Furthermore, this evaluation process is automated using RPA. For example, all customer service speech at a call center is converted into text and the chat generation AI automatically evaluates it. This allows the customer service evaluation system to evaluate all customer service responses. This allows the customer service evaluation system to quickly provide feedback to improve the quality of customer service. For example, based on the evaluation results, areas for improvement in customer service can be identified and appropriate guidance can be provided to employees.

[0065] The service evaluation system according to the embodiment includes an acquisition unit, an evaluation unit, a feedback unit, and an automation unit. The acquisition unit converts voice data into text. For example, the acquisition unit converts voice data into text using a voice recognition system. The acquisition unit can also use deep learning-based voice recognition technology to convert voice data into text with high accuracy. The acquisition unit can also select an appropriate text conversion method depending on the content of the voice data. For example, when converting conversational voice into text, the acquisition unit can use noise reduction technology to improve the quality of the voice data. The evaluation unit uses a chat generation AI to analyze the text data acquired by the acquisition unit and evaluate the quality of the service. The evaluation unit can also analyze the text data using, for example, a text generation AI (e.g., LLM). The evaluation unit can also evaluate whether the content of the service is appropriate, whether an appropriate answer is given to a customer's question, etc. The evaluation unit can also use natural language processing technology to evaluate the quality of the service. For example, the evaluation unit can understand the context of the text data and evaluate the appropriateness of the service. The feedback department identifies areas for improvement in customer service based on the evaluation results obtained by the evaluation department and provides feedback to employees. For example, the feedback department clarifies areas for improvement in customer service based on the evaluation results. The feedback department can also suggest specific improvement methods to employees. Furthermore, the feedback department can provide the evaluation results to employees in real time. For example, the feedback department notifies employees of the evaluation results through a web application or mobile application. The automation department automates the entire process from the acquisition department to the evaluation department and feedback department. For example, the automation department uses RPA to automatically perform the entire process from acquiring text data to evaluating customer service and providing feedback. The automation department can also schedule and manage the evaluation process to evaluate all customer service. Furthermore, the automation department can optimize the evaluation algorithm to improve the efficiency of the evaluation process. For example, the automation department adjusts the evaluation algorithm based on past evaluation data to improve the accuracy of the evaluation.As a result, the service evaluation system according to the embodiment automates a series of processes from acquiring voice data to evaluating the service and providing feedback, and can evaluate all services.

[0066] The acquisition unit can convert the voice data into text using a voice recognition system. Voice recognition systems include, but are not limited to, deep learning-based voice recognition technology and rule-based voice recognition technology. The acquisition unit can convert the voice data into text using, for example, a voice recognition system. The acquisition unit can also use deep learning-based voice recognition technology to convert the voice data into text with high accuracy. Furthermore, the acquisition unit can select an appropriate text conversion method depending on the content of the voice data. For example, when converting conversational voice into text, the acquisition unit can improve the quality of the voice data using noise reduction technology. This allows the voice data to be accurately converted into text using a voice recognition system. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input the voice data acquired by the voice recognition system into a generation AI and have the generation AI perform text conversion.

[0067] The evaluation unit can analyze the text data using a chat generation AI and evaluate the quality of the service. Examples of chat generation AI include, but are not limited to, text generation AI (e.g., LLM). The evaluation unit analyzes the text data using, for example, a text generation AI (e.g., LLM). The evaluation unit can also evaluate whether the content of the service is appropriate, whether an appropriate answer was given to a customer's question, and so on. Furthermore, the evaluation unit can use natural language processing technology to evaluate the quality of the service. For example, the evaluation unit can understand the context of the text data and evaluate the appropriateness of the service. This allows the use of the chat generation AI to accurately evaluate the quality of the service. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input text data into the generation AI and have the generation AI evaluate the quality of the service.

[0068] The feedback unit can identify areas for improvement in customer service based on the evaluation results and provide feedback to the employee. For example, the feedback unit can clarify areas for improvement in customer service based on the evaluation results. The feedback unit can also suggest specific improvement methods to the employee. Furthermore, the feedback unit can provide the evaluation results to the employee in real time. For example, the feedback unit can notify the employee of the evaluation results via a web application or a mobile application. This makes it possible to identify areas for improvement in customer service based on the evaluation results and provide appropriate feedback to the employee. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the evaluation results to a generation AI, which can identify areas for improvement in customer service and provide feedback.

[0069] The automation unit can automate a series of processes from the acquisition unit to the evaluation unit and feedback unit. The automation unit can, for example, use RPA to automatically perform a series of processes from acquiring text data to evaluating responses and providing feedback. The automation unit can also schedule and manage the evaluation process to evaluate all responses. The automation unit can also optimize the evaluation algorithm to improve the efficiency of the evaluation process. For example, the automation unit can adjust the evaluation algorithm based on past evaluation data to improve the accuracy of the evaluation. This allows evaluation to be performed for all responses by automating the series of processes. Some or all of the above-mentioned processing in the automation unit may be performed using, for example, AI, or may be performed without using AI. For example, the automation unit can have a generation AI perform schedule management for the evaluation process and optimize the evaluation algorithm.

[0070] The acquisition unit can estimate the user's emotions and adjust the timing of voice data acquisition based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition unit delays the timing of voice data acquisition and acquires the voice data in a relaxed state. Furthermore, if the user is relaxed, the acquisition unit can immediately acquire voice data to maintain a natural conversation flow. Furthermore, if the user is in a hurry, the acquisition unit can quickly acquire voice data and quickly evaluate the response. This allows the voice data to be acquired at a more appropriate timing by adjusting the timing of voice data acquisition according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotion data into the generation AI, causing the generation AI to adjust the timing of voice data acquisition.

[0071] When acquiring voice data, the acquisition unit can analyze the user's past response history and select the optimal acquisition method. For example, the acquisition unit may acquire voice data using a similar method, referring to a response method that the user has previously rated highly. The acquisition unit may also acquire voice data using a different method, avoiding a response method that the user has previously rated poorly. Furthermore, the acquisition unit can select the optimal acquisition method for a specific time period from the user's past response history. In this way, the optimal acquisition method can be selected by analyzing the user's past response history. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input the user's past response history data into a generation AI, causing the generation AI to select the optimal acquisition method.

[0072] When acquiring voice data, the acquisition unit can perform filtering based on the user's current situation and areas of interest. For example, the acquisition unit preferentially acquires voice data related to topics in which the user is currently interested. The acquisition unit can also acquire appropriate voice data depending on the user's current situation (e.g., at work, on a break, etc.). Furthermore, the acquisition unit can filter and acquire highly relevant voice data based on the user's areas of interest. In this way, highly relevant data can be acquired by filtering the voice data based on the user's current situation and areas of interest. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's current situation and area of ​​interest data to a generation AI, and have the generation AI filter the voice data.

[0073] When acquiring voice data, the acquisition unit can select the optimal acquisition means depending on the user's input method. For example, if the user is using voice input, the acquisition unit prioritizes acquiring voice data. Also, if the user is using text input, the acquisition unit can also prioritize acquiring text data. Furthermore, if the user is using image input, the acquisition unit can analyze the image data and acquire related voice data. This allows voice data to be acquired efficiently by selecting the optimal acquisition means depending on the user's input method. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit inputs the user's input method data into a generation AI, causing the generation AI to select the optimal acquisition means.

[0074] The acquisition unit can estimate the user's emotions and determine the priority of voice data to be acquired based on the estimated user's emotions. For example, when the user is stressed, the acquisition unit prioritizes acquiring voice data in a relaxed state. Furthermore, when the user is relaxed, the acquisition unit can also quickly acquire voice data to maintain a natural conversation flow. Furthermore, when the user is in a hurry, the acquisition unit can quickly acquire voice data and quickly evaluate the response. This allows for prioritizing voice data according to the user's emotions, thereby enabling more appropriate data to be acquired preferentially. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotion data into the generation AI, causing the generation AI to determine the priority of the voice data.

[0075] When acquiring voice data, the acquisition unit can prioritize acquiring highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the acquisition unit prioritizes acquiring voice data related to that area. Furthermore, when the user is moving, the acquisition unit can also acquire optimal voice data based on the user's current location. Furthermore, when the user is in a specific location, the acquisition unit can also prioritize acquiring voice data related to that location. In this way, highly relevant data can be acquired preferentially by taking the user's geographical location information into account. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information data to the generation AI, causing the generation AI to prioritize acquiring highly relevant data.

[0076] When acquiring the voice data, the acquisition unit can analyze the user's social media activity and acquire related data. For example, the acquisition unit can acquire voice data related to places where the user has checked in on social media. The acquisition unit can also analyze the content of the user's social media posts and acquire related voice data. Furthermore, the acquisition unit can also acquire related voice data by referring to the activities of the user's friends on social media. In this way, related data can be efficiently acquired by analyzing the user's social media activity. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's social media activity data into a generation AI and cause the generation AI to acquire related voice data.

[0077] When acquiring voice data, the acquisition unit can customize the acquisition method by reflecting the user's past feedback. For example, the acquisition unit preferentially uses an acquisition method that the user has previously given a high rating. The acquisition unit can also acquire voice data by a different method, avoiding an acquisition method that the user has previously given a low rating. Furthermore, the acquisition unit can customize the optimal acquisition method based on the user's past feedback. In this way, the optimal acquisition method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past feedback data into a generation AI and have the generation AI customize the acquisition method.

[0078] The evaluation unit can estimate the user's emotions and adjust the way the evaluation is expressed based on the estimated user's emotions. For example, if the user is relaxed, the evaluation unit can provide a detailed evaluation result. Furthermore, if the user is nervous, the evaluation unit can provide a simple, highly visible evaluation result. Furthermore, if the user is in a hurry, the evaluation unit can provide a summary evaluation result. This allows for adjusting the way the evaluation is expressed based on the user's emotions, thereby providing a more appropriate evaluation result. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the evaluation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the evaluation unit can input the user's emotion data into the generation AI, causing the generation AI to adjust the way the evaluation is expressed.

[0079] The evaluation unit can adjust the level of detail of the evaluation based on the importance of the response during evaluation. For example, the evaluation unit performs a detailed evaluation for important responses. The evaluation unit can also perform a concise evaluation for general responses. Furthermore, the evaluation unit can adjust the level of detail of the evaluation depending on the importance of the response. In this way, by adjusting the level of detail of the evaluation based on the importance of the response, detailed evaluation can be performed for important responses. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input response importance data into a generation AI and have the generation AI adjust the level of detail of the evaluation.

[0080] The evaluation unit can apply different evaluation algorithms depending on the response category during evaluation. For example, the evaluation unit can apply a specialized evaluation algorithm to responses to technical questions. The evaluation unit can also apply a simple evaluation algorithm to responses to general questions. Furthermore, the evaluation unit can select the optimal evaluation algorithm depending on the response category. This allows the accuracy of the evaluation to be improved by applying the optimal evaluation algorithm depending on the response category. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or can be performed without using AI. For example, the evaluation unit can input response category data into a generation AI and have the generation AI select the optimal evaluation algorithm.

[0081] The evaluation unit can improve the accuracy of the evaluation by referring to the user's past evaluation results when making an evaluation. For example, the evaluation unit can improve the accuracy of the evaluation by referring to response methods that the user has previously received high ratings. The evaluation unit can also improve the accuracy of the evaluation by avoiding response methods that the user has previously received low ratings. Furthermore, the evaluation unit can select an optimal evaluation method based on the user's past evaluation results. In this way, the accuracy of the evaluation can be improved by referring to the user's past evaluation results. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the user's past evaluation result data into the generation AI, which can improve the accuracy of the evaluation.

[0082] The evaluation unit can estimate the user's emotions and adjust the length of the evaluation based on the estimated user's emotions. For example, if the user is relaxed, the evaluation unit can provide a detailed evaluation. If the user is nervous, the evaluation unit can also provide a concise evaluation. If the user is in a hurry, the evaluation unit can also provide a summary evaluation. This allows for adjusting the length of the evaluation according to the user's emotions, thereby providing a more appropriate evaluation result. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or without AI. For example, the evaluation unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the evaluation.

[0083] The evaluation unit can determine the priority of the evaluation based on the time of the response at the time of evaluation. For example, the evaluation unit prioritizes evaluation of recent responses. The evaluation unit can also postpone evaluation of past responses. Furthermore, the evaluation unit can adjust the priority of the evaluation depending on the time of the response. In this way, by determining the priority of the evaluation based on the time of the response, the most recent response can be prioritized for evaluation. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input data on the time of the response to the generation AI and have the generation AI determine the priority of the evaluation.

[0084] The evaluation unit can adjust the order of evaluation based on the relevance of the responses during evaluation. For example, the evaluation unit prioritizes evaluation of important responses. The evaluation unit can also postpone evaluation of general responses. Furthermore, the evaluation unit can adjust the order of evaluation according to the relevance of the responses. In this way, by adjusting the order of evaluation based on the relevance of the responses, important responses can be prioritized in evaluation. Some or all of the above-described processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input response relevance data into a generation AI and have the generation AI adjust the order of evaluation.

[0085] During evaluation, the evaluation unit can adjust the use of technical terminology in the evaluation according to the user's level of expertise. For example, if the user has technical expertise, the evaluation unit can use technical terminology to make the evaluation. Furthermore, if the user does not have technical expertise, the evaluation unit can also use simple terminology to make the evaluation. Furthermore, the evaluation unit can adjust the use of technical terminology in the evaluation according to the user's level of expertise. This allows for the provision of more understandable evaluation results by adjusting the use of technical terminology in the evaluation according to the user's level of expertise. Some or all of the above-described processing in the evaluation unit may be performed using, or without, AI, for example. For example, the evaluation unit can input the user's level of expertise data into the generation AI, causing the generation AI to adjust the use of technical terminology in the evaluation.

[0086] The feedback unit can estimate the user's emotions and adjust the feedback method based on the estimated user's emotions. For example, the feedback unit can provide detailed feedback when the user is relaxed. Furthermore, the feedback unit can provide simple, highly visible feedback when the user is nervous. Furthermore, the feedback unit can provide feedback that focuses on the main points when the user is in a hurry. This allows for more appropriate feedback to be provided by adjusting the feedback method according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the feedback unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the feedback unit can input the user's emotion data into the generation AI, causing the generation AI to adjust the feedback method.

[0087] When providing feedback, the feedback unit can select the optimal feedback method by analyzing the user's past response behavior. For example, the feedback unit can provide feedback in a similar method by referring to response methods that the user has previously received high ratings. The feedback unit can also provide feedback in a different method, avoiding response methods that the user has previously received low ratings. Furthermore, the feedback unit can select the optimal feedback method based on the user's past response behavior. In this way, the optimal feedback method can be selected by analyzing the user's past response behavior. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input data on the user's past response behavior into a generation AI, causing the generation AI to select the optimal feedback method.

[0088] The feedback unit can customize the feedback means based on the user's current situation when providing feedback. For example, when the user is working, the feedback unit can provide brief feedback. Also, when the user is on a break, the feedback unit can provide detailed feedback. Furthermore, the feedback unit can select the optimal feedback means according to the user's current situation. In this way, by customizing the feedback means based on the user's current situation, more appropriate feedback can be provided. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's current situation data into a generation AI and have the generation AI customize the feedback means.

[0089] The feedback unit can improve the feedback method by reflecting the user's feedback when providing feedback. For example, the feedback unit preferentially uses feedback methods that the user has previously given a high rating. The feedback unit can also provide feedback using a different method, avoiding feedback methods that the user has previously given a low rating. Furthermore, the feedback unit can customize the optimal feedback method based on the user's past feedback. In this way, the feedback method can be improved by reflecting the user's feedback. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past feedback data into a generation AI, which can then improve the feedback method.

[0090] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. For example, if the user is feeling stressed, the feedback unit can provide feedback in a relaxed state. Furthermore, if the user is relaxed, the feedback unit can also provide feedback immediately. Furthermore, if the user is in a hurry, the feedback unit can also provide feedback quickly. Thus, by determining the priority of feedback according to the user's emotions, feedback can be provided at a more appropriate time. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the feedback unit can input the user's emotion data into the generation AI, causing the generation AI to determine the priority of feedback.

[0091] When providing feedback, the feedback unit can select the optimal feedback method by taking into account the user's geographical location information. For example, if the user is in a specific area, the feedback unit provides feedback related to that area. Furthermore, if the user is moving, the feedback unit can provide optimal feedback based on the user's current location. Furthermore, if the user is in a specific location, the feedback unit can provide feedback related to that location. In this way, the optimal feedback method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's geographical location information data into a generation AI, causing the generation AI to select the optimal feedback method.

[0092] At the time of providing feedback, the feedback unit can analyze the user's social media activity and suggest a means of providing feedback. For example, the feedback unit can provide feedback related to places where the user has checked in on social media. The feedback unit can also analyze the content of the user's social media posts and provide relevant feedback. Furthermore, the feedback unit can provide relevant feedback by referring to the activities of the user's friends on social media. In this way, relevant feedback can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's social media activity data into a generation AI and have the generation AI suggest a means of providing feedback.

[0093] When providing feedback, the feedback unit can customize the feedback method by reflecting the user's past feedback. For example, the feedback unit preferentially uses feedback methods that the user has previously given a high rating to. The feedback unit can also provide feedback by a different method, avoiding feedback methods that the user has previously given a low rating to. Furthermore, the feedback unit can customize the optimal feedback method based on the user's past feedback. In this way, the optimal feedback method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past feedback data into a generation AI and have the generation AI customize the feedback method.

[0094] The automation unit can estimate the user's emotions and select an automation process based on the estimated user's emotions. For example, if the user is relaxed, the automation unit can select a detailed automation process. If the user is nervous, the automation unit can also select a simple automation process. If the user is in a hurry, the automation unit can also select a quick automation process. This allows for more appropriate automation by selecting an automation process according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the automation unit can be performed using, for example, AI, or without AI. For example, the automation unit can input the user's emotion data into the generation AI and have the generation AI select an automation process.

[0095] During automation, the automation unit can optimize the automation algorithm by referring to past automation data. For example, the automation unit selects an optimal automation algorithm based on past automation data. The automation unit can also select an efficient automation algorithm from past automation data. Furthermore, the automation unit can analyze past automation data and select an optimal automation algorithm. In this way, the automation algorithm can be optimized by referring to past automation data. Some or all of the above-described processing in the automation unit may be performed using, for example, AI, or may be performed without using AI. For example, the automation unit can input past automation data into a generation AI and have the generation AI optimize the automation algorithm.

[0096] The automation unit can update the automation process by reflecting user feedback during automation. For example, the automation unit preferentially uses automation processes that the user has previously rated highly. The automation unit can also perform automation in a different way, avoiding automation processes that the user has previously rated poorly. Furthermore, the automation unit can customize an optimal automation process based on the user's past feedback. This allows the automation process to be updated by reflecting user feedback. Some or all of the above-described processing in the automation unit may be performed using AI, for example, or may be performed without using AI. For example, the automation unit can input the user's past feedback data into a generation AI and have the generation AI update the automation process.

[0097] The automation unit can estimate the user's emotions and adjust the frequency of automation based on the estimated user emotions. For example, the automation unit can execute automation processes more frequently when the user is relaxed. Furthermore, the automation unit can also execute automation processes more quickly when the user is tense. By adjusting the frequency of automation according to the user's emotions, automation can be executed at a more appropriate frequency. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the automation unit can be performed using AI, or without AI. For example, the automation unit can input user emotion data into the generation AI and have the generation AI adjust the frequency of automation.

[0098] During automation, the automation unit can weight the automation process based on the time when the response data was acquired. For example, the automation unit can prioritize executing the automation process for recent response data. The automation unit can also postpone executing the automation process for older response data. Furthermore, the automation unit can adjust the weighting of the automation process depending on the time when the response data was acquired. In this way, by weighting the automation process based on the time when the response data was acquired, the most recent data can be processed preferentially. Some or all of the above-mentioned processing in the automation unit may be performed using, for example, AI, or may be performed without using AI. For example, the automation unit can input data on the time when the response data was acquired into the generation AI and have the generation AI weight the automation process.

[0099] During automation, the automation unit can integrate information from different data sources to enhance the automation process. For example, the automation unit can integrate information from different data sources to execute an optimal automation process. The automation unit can also analyze information from different data sources to execute an efficient automation process. Furthermore, the automation unit can enhance the automation process based on information from different data sources. In this way, the automation process can be enhanced by integrating information from different data sources. Some or all of the above-described processing in the automation unit may be performed using, for example, AI, or may be performed without using AI. For example, the automation unit can input information from different data sources into a generation AI, which can enhance the automation process. === Hard Collateral 1-1 === Each of the multiple elements, including the acquisition unit, evaluation unit, feedback unit, and automation unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires voice data using the camera 42 or microphone 38B of the smart device 14 and converts the voice data into text by the specific processing unit 290 of the data processing device 12. The evaluation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the acquired text data and evaluates the quality of the customer service. The feedback unit, realized, for example, by the control unit 46A of the smart device 14, provides feedback to the employee based on the evaluation results. The automation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, automates a series of processes from the acquisition unit to the evaluation unit and feedback unit. === Hard Collateral 1-2 === Each of the multiple elements, including the acquisition unit, evaluation unit, feedback unit, and automation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit acquires voice data using the camera 42 and microphone 238 of the smart glasses 214 and converts the voice data into text by the specific processing unit 290 of the data processing device 12. The evaluation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the acquired text data and evaluates the quality of the customer service. The feedback unit, realized, for example, by the control unit 46A of the smart glasses 214, provides feedback to the employee based on the evaluation results. The automation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, automates a series of processes from the acquisition unit to the evaluation unit and feedback unit. === Hard Collateral 1-3 === Each of the multiple elements, including the acquisition unit, evaluation unit, feedback unit, and automation unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the acquisition unit acquires voice data using the camera 42 or microphone 238 of the headset terminal 314 and converts the voice data into text by the specific processing unit 290 of the data processing device 12. The evaluation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the acquired text data and evaluates the quality of the service. The feedback unit, realized, for example, by the control unit 46A of the headset terminal 314, provides feedback to the employee based on the evaluation results. The automation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, automates a series of processes from the acquisition unit to the evaluation unit and feedback unit. === Hard Collateral 1-4 === Each of the multiple elements, including the acquisition unit, evaluation unit, feedback unit, and automation unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires voice data using the camera 42 or microphone 238 of the robot 414 and converts the voice data into text by the specific processing unit 290 of the data processing device 12. The evaluation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the acquired text data and evaluates the quality of the service. The feedback unit, realized, for example, by the control unit 46A of the robot 414, provides feedback to the employee based on the evaluation results. The automation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, automates a series of processes from the acquisition unit to the evaluation unit and feedback unit.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] When acquiring voice data, the acquisition unit can analyze the user's past response history and select the optimal acquisition method. For example, the acquisition unit can acquire voice data using a similar method, referring to response methods that the user has previously rated highly. The acquisition unit can also acquire voice data using a different method, avoiding response methods that the user has previously rated poorly. Furthermore, the acquisition unit can select the optimal acquisition method for a specific time period from the user's past response history. In this way, the optimal acquisition method can be selected by analyzing the user's past response history. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past response history data into a generation AI, causing the generation AI to select the optimal acquisition method.

[0102] During evaluation, the evaluation unit can adjust the level of detail of the evaluation based on the importance of the call. For example, a detailed evaluation is made for an important call. The evaluation unit can also make a brief evaluation for a general call. Furthermore, the evaluation unit can adjust the level of detail of the evaluation depending on the importance of the call. In this way, by adjusting the level of detail of the evaluation based on the importance of the call, a detailed evaluation can be made for an important call. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the importance data of the call into a generation AI and have the generation AI adjust the level of detail of the evaluation.

[0103] When providing feedback, the feedback unit can select the optimal feedback method by analyzing the user's past response behavior. For example, the feedback unit can provide feedback in a similar method by referring to response methods that the user has previously received high ratings. The feedback unit can also provide feedback in a different method, avoiding response methods that the user has previously received low ratings. Furthermore, the feedback unit can select the optimal feedback method based on the user's past response behavior. In this way, the optimal feedback method can be selected by analyzing the user's past response behavior. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input data on the user's past response behavior into a generation AI, causing the generation AI to select the optimal feedback method.

[0104] During automation, the automation unit can optimize the automation algorithm by referring to past automation data. For example, the automation unit selects an optimal automation algorithm based on past automation data. The automation unit can also select an efficient automation algorithm from past automation data. Furthermore, the automation unit can analyze past automation data and select an optimal automation algorithm. In this way, the automation algorithm can be optimized by referring to past automation data. Some or all of the above-described processing in the automation unit may be performed using, for example, AI, or may be performed without using AI. For example, the automation unit can input past automation data into a generation AI and have the generation AI optimize the automation algorithm.

[0105] When acquiring voice data, the acquisition unit can filter the voice data based on the user's current situation and areas of interest. For example, the acquisition unit can prioritize acquisition of voice data related to topics in which the user is currently interested. The acquisition unit can also acquire appropriate voice data depending on the user's current situation (e.g., at work, on a break, etc.). Furthermore, the acquisition unit can filter and acquire highly relevant voice data based on the user's areas of interest. In this way, highly relevant data can be acquired by filtering the voice data based on the user's current situation and areas of interest. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's current situation and area of ​​interest data to a generation AI, and have the generation AI filter the voice data.

[0106] The evaluation unit can estimate the user's emotions and adjust the way the evaluation is expressed based on the estimated user's emotions. For example, if the user is relaxed, the evaluation unit can provide a detailed evaluation result. If the user is nervous, the evaluation unit can also provide a simple, highly visible evaluation result. If the user is in a hurry, the evaluation unit can also provide a summary evaluation result. This allows for adjusting the way the evaluation is expressed based on the user's emotions, thereby providing a more appropriate evaluation result. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the evaluation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the evaluation unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the evaluation is expressed.

[0107] The feedback unit can estimate the user's emotions and adjust the feedback method based on the estimated user's emotions. For example, if the user is relaxed, the feedback unit can provide detailed feedback. If the user is nervous, the feedback unit can also provide simple, highly visible feedback. If the user is in a hurry, the feedback unit can also provide feedback that focuses on the main points. This allows for more appropriate feedback to be provided by adjusting the feedback method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the feedback unit can input the user's emotion data into the generation AI, which can then adjust the feedback method.

[0108] The automation unit can estimate the user's emotions and select an automation process based on the estimated user's emotions. For example, if the user is relaxed, a detailed automation process can be selected. The automation unit can also select a simple automation process if the user is nervous. Furthermore, the automation unit can also select a quick automation process if the user is in a hurry. This allows for more appropriate automation by selecting an automation process according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the automation unit can be performed using, for example, AI, or without AI. For example, the automation unit can input user emotion data into the generation AI and have the generation AI select an automation process.

[0109] The acquisition unit can estimate the user's emotions and prioritize the voice data to be acquired based on the estimated user's emotions. For example, if the user is feeling stressed, the acquisition unit can prioritize acquiring voice data in a relaxed state. Furthermore, if the user is relaxed, the acquisition unit can also quickly acquire voice data to maintain a natural conversation flow. Furthermore, if the user is in a hurry, the acquisition unit can quickly acquire voice data and quickly evaluate the response. This allows for prioritizing the voice data according to the user's emotions, thereby enabling more appropriate data to be acquired preferentially. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input the user's emotion data into the generation AI, causing the generation AI to prioritize the voice data.

[0110] The evaluation unit can estimate the user's emotions and adjust the length of the evaluation based on the estimated user's emotions. For example, if the user is relaxed, the evaluation unit can provide a detailed evaluation. If the user is nervous, the evaluation unit can also provide a concise evaluation. If the user is in a hurry, the evaluation unit can also provide an evaluation that focuses on the main points. This allows the evaluation length to be adjusted according to the user's emotions, thereby providing a more appropriate evaluation result. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or without AI. For example, the evaluation unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the evaluation.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The acquisition unit converts the voice data into text. For example, the acquisition unit converts the voice data into text using a voice recognition system. The acquisition unit can also use deep learning-based voice recognition technology to convert the voice data into text with high accuracy. Furthermore, the acquisition unit can select an appropriate text conversion method depending on the content of the voice data. For example, when converting conversational voice into text, the acquisition unit can use noise reduction technology to improve the quality of the voice data. Step 2: The evaluation unit analyzes the text data acquired by the acquisition unit and evaluates the quality of the service. The evaluation unit analyzes the text data using, for example, a text generation AI (e.g., LLM). The evaluation unit can also evaluate whether the content of the service is appropriate and whether appropriate answers are given to customer questions. Furthermore, the evaluation unit can use natural language processing technology to evaluate the quality of the service. For example, the evaluation unit understands the context of the text data and evaluates the appropriateness of the service. Step 3: The feedback department identifies areas for improvement in customer service based on the evaluation results obtained by the evaluation department and provides feedback to the employee. For example, the feedback department clarifies areas for improvement in customer service based on the evaluation results. The feedback department can also suggest specific ways to improve to the employee. Furthermore, the feedback department can provide the evaluation results to the employee in real time. For example, the feedback department notifies the employee of the evaluation results via a web application or a mobile application. Step 4: The automation unit automates the entire process from the acquisition unit to the evaluation unit and feedback unit. For example, using RPA, the automation unit automates the entire process from acquiring text data to evaluating responses and providing feedback. The automation unit can also schedule and manage the evaluation process to evaluate all responses. Furthermore, the automation unit can optimize the evaluation algorithm to improve the efficiency of the evaluation process. For example, the automation unit can adjust the evaluation algorithm based on past evaluation data to improve the accuracy of the evaluation.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0156] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0157] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0169] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0170] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0174] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0175] 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.

[0176] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0177] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0178] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0179] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0182] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit that converts voice data into text; an evaluation unit that analyzes the text data acquired by the acquisition unit and evaluates the quality of the response; a feedback section that clarifies areas for improvement in customer service based on the evaluation results obtained by the evaluation section and provides feedback to the employees; an automation unit that automates a series of processes from the acquisition unit to the evaluation unit and the feedback unit. A system characterized by:

2. The acquisition unit Converting voice data into text using a voice recognition system The system of claim 1 .

3. The evaluation unit Chat generation AI analyzes text data and evaluates the quality of the response. The system of claim 1 .

4. The feedback unit Identify areas for improvement based on the evaluation results and provide feedback to employees The system of claim 1 .

5. The automation unit A series of processes from the acquisition unit to the evaluation unit and the feedback unit are automated. The system of claim 1 .

6. The acquisition unit The user's emotions are estimated, and the timing of acquiring voice data is adjusted based on the estimated user's emotions. The system of claim 1 .

7. The acquisition unit When acquiring voice data, analyze the user's past response history and select the optimal acquisition method. The system of claim 1 .

8. The acquisition unit When capturing voice data, it filters it based on the user's current situation and areas of interest. The system of claim 1 .

9. The acquisition unit When acquiring voice data, select the optimal acquisition method depending on the user's input method. The system of claim 1 .

10. The acquisition unit The user's emotions are estimated, and the priority of the voice data to be acquired is determined based on the estimated user's emotions. The system of claim 1 .

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