system

Abstract

The system according to the embodiment aims to analyze the operation data of operators and optimize shifts and rest times. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and an optimization unit. The collection unit collects operation data of operators. The analysis unit analyzes the data collected by the collection unit. The optimization unit adjusts the shifts and break times of operators based on the analysis results obtained by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not efficiently collect and analyze operator operation data and do not adequately optimize shifts and rest times, leaving room for improvement.

[0005] The system according to the embodiment aims to analyze the operation data of operators and optimize shifts and rest times. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and an optimization unit. The collection unit collects operation data of operators. The analysis unit analyzes the data collected by the collection unit. The optimization unit adjusts the shifts and break times of operators based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the operation data of operators and optimize shifts and rest times. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention uses AI to improve the operational management of agents at corporate contact centers. This system collects operational data for each agent, analyzes it using AI, grasps the agent's operational status in real time, and optimizes the agent's shifts and break times based on the analysis results. For example, detailed data such as each agent's login time, logout time, call time, and break time are collected. By collecting data such as the time the agent logged into the system, the time the call started, and the time the call ended, the agent's operational status can be accurately grasped. Next, AI analyzes the collected data. Based on the collected data, the AI ​​analyzes the operational status of each agent in real time. For example, the AI ​​detects abnormal operational status, such as when an agent's call time is too long or when the break time is too short. This allows the agent's operational status to be grasped in real time. Furthermore, the AI ​​optimizes the agent's shifts and break times based on the analysis results. For example, if an agent's call time is too long, the AI ​​can automatically suggest a break. Furthermore, optimizing the agent's shifts improves operational efficiency. This reduces the agent's workload and improves operational productivity. This mechanism enables efficient agent operational management. By understanding the operating status of agents in real time and optimizing shifts and break times, work productivity can be improved. For example, if an agent is on the phone for too long, AI can automatically suggest taking a break, thereby reducing the agent's workload. In addition, optimizing agent shifts can improve work efficiency. This improves agent satisfaction and improves work productivity. As a result, a system that uses AI to improve agent operating management at corporate contact centers can improve work productivity by understanding the operating status of agents in real time and optimizing shifts and break times.

[0029] An operation management system according to an embodiment includes a collection unit, an analysis unit, and an optimization unit. The collection unit collects operation data of agents. The operation data of agents includes, but is not limited to, login times, logout times, call times, and break times. The collection unit collects data such as the time an agent logs into the system, the time a call starts, and the time a call ends. The collection unit can collect detailed data to accurately grasp the operation status of agents. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the operation status of each agent in real time, for example, based on the collected data. The analysis unit can detect abnormal operation status, such as when an agent's call time is too long or when a break time is too short. The analysis unit can detect abnormal operation status based on the collected data using AI. The optimization unit optimizes agent shifts and break times based on the analysis results obtained by the analysis unit. For example, if an agent's call time is too long, the AI ​​can automatically suggest a break. The optimization unit can improve work efficiency by optimizing agent shifts. The optimization unit can use AI to optimize operator shifts and break times based on the analysis results. As a result, the operation management system according to the embodiment can grasp the operating status of operators in real time and optimize shifts and break times, thereby improving work productivity.

[0030] The collection unit can collect detailed data on the login time, logout time, call time, and break time of the operator. The collection unit collects, for example, data such as the time when the operator logged into the system, the time when the call started, and the time when the call ended. The collection unit can collect detailed data to accurately grasp the operating status of the operator. For example, the collection unit collects detailed data such as the login time, logout time, call time, and break time of the operator. By collecting detailed operating data in this way, the operating status of the operator can be accurately grasped. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data such as the operator's login time and call time into AI, which can analyze and collect the data.

[0031] The analysis unit can analyze the operating status of each agent in real time based on the collected data. For example, the analysis unit analyzes the operating status of each agent in real time based on the collected data. The analysis unit can detect abnormal operating status, such as when an agent's call time is too long or when their break time is too short. The analysis unit can detect abnormal operating status based on the collected data using AI. For example, the analysis unit detects abnormal operating status when an agent's call time exceeds a certain time or when their break time is less than a certain time. In this way, by analyzing the operating status in real time, abnormal operating status can be immediately detected. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the collected data into AI, which analyzes the data and detects abnormal operating status.

[0032] The analysis unit can detect an abnormal operating condition when an operator's call time exceeds a certain time or when a break time is less than a certain time. The analysis unit can detect an abnormal operating condition, for example, when an operator's call time exceeds a certain time or when a break time is less than a certain time. The analysis unit can detect an abnormal operating condition based on collected data using AI. For example, the analysis unit can detect an abnormal operating condition when an operator's call time is too long or when a break time is too short. By detecting an abnormal operating condition, the burden on the operator can be reduced. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input collected data into AI, which can analyze the data and detect an abnormal operating condition.

[0033] The optimization unit can optimize the shifts and break times of the operators based on the analysis results. The optimization unit, for example, optimizes the shifts and break times of the operators based on the analysis results. The optimization unit can optimize the shifts and break times of the operators based on the analysis results using AI. For example, the optimization unit can automatically suggest a break if the operator's call time is too long. The optimization unit can improve work efficiency by optimizing the operator's shifts. As a result, work efficiency can be improved by optimizing the shifts and break times. Some or all of the above-mentioned processing in the optimization unit may be performed using AI, for example, or may be performed without using AI. For example, the optimization unit can input the analysis results to AI, which can optimize the shifts and break times.

[0034] The optimization unit can have the AI ​​automatically suggest a break when an operator's call time exceeds a certain time. For example, the optimization unit can have the AI ​​automatically suggest a break when an operator's call time exceeds a certain time. The optimization unit can use the AI ​​to optimize the operator's shifts and break times based on the analysis results. For example, the optimization unit can have the AI ​​automatically suggest a break when an operator's call time is too long. This can reduce the operator's workload by automatically suggesting a break when the call time is too long. Some or all of the above-mentioned processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input the analysis results into the AI, which can then optimize the shifts and break times.

[0035] The collection unit can analyze the operator's past operation data and select the optimal collection method. The collection unit, for example, selects the most efficient collection timing from the operator's past operation data. The collection unit can analyze the operator's past operation data and select the optimal collection method using AI. For example, the collection unit adjusts the collection frequency based on the operator's past operation data. The collection unit can analyze the operator's past operation data and concentrate collection during specific time periods. This allows the optimal collection method to be selected by analyzing the past operation data. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the operator's past operation data into AI, which can select the optimal collection method.

[0036] The collection unit can filter the operation data based on the operator's current work status and areas of interest when collecting the operation data. For example, when the operator is busy, the collection unit prioritizes collecting only important data. The collection unit can use AI to filter the operation data based on the operator's current work status and areas of interest when collecting the operation data. For example, when the operator is concentrating on a specific task, the collection unit prioritizes collecting data related to that task. The collection unit can filter and collect related data based on the operator's areas of interest. This allows important data to be collected preferentially by filtering the data based on the work status and areas of interest. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input data on the operator's work status and areas of interest into AI, which can then perform the filtering.

[0037] When collecting operation data, the collection unit can prioritize collecting important data by taking into account the operator's geographical location information. For example, if the operator is working in a specific area, the collection unit prioritizes collecting data related to that area. When collecting operation data, the collection unit can use AI to prioritize collecting highly relevant data by taking into account the operator's geographical location information. For example, if the operator is moving, the collection unit collects relevant data based on the operator's current location. If the operator stays in a specific location for a long time, the collection unit can prioritize collecting data related to that location. In this way, highly relevant data can be collected preferentially by taking into account the geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the operator's geographical location information into AI, which can then prioritize collecting highly relevant data.

[0038] The collection unit can analyze the social media activities of the operator when collecting operation data and collect related data. The collection unit can collect related data based on, for example, information shared by the operator on social media. The collection unit can use AI to analyze the social media activities of the operator when collecting operation data and collect related data. For example, the collection unit extracts and collects work-related data from the operator's social media activities. The collection unit can collect related data based on topics in which the operator has shown interest on social media. This allows for efficient collection of related data by analyzing social media activities. Some or all of the above-described processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the operator's social media activity data into AI, which can collect related data.

[0039] The analysis unit can adjust the level of detail of the analysis based on the priority of the operational data during analysis. The analysis unit, for example, performs a detailed analysis on data with high importance. The analysis unit can adjust the level of detail of the analysis based on the priority of the operational data during analysis using AI. For example, the analysis unit performs a simplified analysis on data with low importance. The analysis unit can analyze data with an appropriate level of detail on data with medium importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the operational data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the priority of the operational data to AI, which can adjust the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the category of operation data during analysis. For example, the analysis unit applies a call quality analysis algorithm to call time data. The analysis unit can use AI to apply different analysis algorithms depending on the category of operation data during analysis. For example, the analysis unit can apply a rest pattern analysis algorithm to break time data. The analysis unit can apply an operation time analysis algorithm to login / logout time data. This enables highly accurate analysis by applying different analysis algorithms depending on the category of operation data. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the category of operation data into AI, which can then apply different analysis algorithms.

[0041] During analysis, the analysis unit can determine the order of analysis based on the collection date of the operation data. The analysis unit, for example, prioritizes analyzing the most recent data. During analysis, the analysis unit can use AI to determine the order of analysis based on the collection date of the operation data. For example, the analysis unit prioritizes analyzing current data while referring to past data. The analysis unit can prioritize analyzing data collected during a specific period. This allows the most recent data to be analyzed preferentially by determining the analysis priority based on the collection date of the operation data. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the collection date of the operation data into AI, and the AI ​​can determine the order of analysis.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the operational data during analysis. The analysis unit, for example, prioritizes analysis of highly relevant data. The analysis unit can adjust the order of analysis based on the relevance of the operational data during analysis using AI. For example, the analysis unit analyzes data with low relevance later. The analysis unit can analyze data with medium relevance in an appropriate order. This enables efficient analysis by adjusting the order of analysis based on the relevance of the operational data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the relevance of the operational data to AI, which can adjust the order of analysis.

[0043] During optimization, the optimization unit can analyze the operator's past operation data to select the optimal optimization method. For example, the optimization unit selects the most efficient shift pattern from the operator's past operation data. During optimization, the optimization unit can use AI to analyze the operator's past operation data to select the optimal optimization method. For example, the optimization unit selects the optimal timing for breaks based on the operator's past operation data. The optimization unit can analyze the operator's past operation data and propose optimal work allocation. In this way, the optimal optimization method can be selected by analyzing the past operation data. Some or all of the above-mentioned processing in the optimization unit may be performed using AI, for example, or may be performed without using AI. For example, the optimization unit can input the operator's past operation data into AI, which can select the optimal optimization method.

[0044] The optimization unit can adjust the optimization measures based on the current work situation of the operator during optimization. For example, if the operator is busy, the optimization unit performs optimization to increase break times. The optimization unit can use AI to adjust the optimization measures based on the current work situation of the operator during optimization. For example, if the operator is concentrating on a specific task, the optimization unit performs optimization related to that task. The optimization unit can customize shifts and work assignments according to the operator's work situation. This enables more efficient optimization by customizing the optimization measures based on the current work situation. Some or all of the above-mentioned processing in the optimization unit may be performed using AI, for example, or may be performed without using AI. For example, the optimization unit can input the operator's work situation data into AI, which can adjust the optimization measures.

[0045] The optimization unit can select an appropriate optimization method by taking into account the geographical location information of the operator during optimization. For example, if the operator works in a specific area, the optimization unit performs optimization related to that area. The optimization unit can use AI to select the optimal optimization method by taking into account the geographical location information of the operator during optimization. For example, if the operator is traveling, the optimization unit selects the optimal optimization method based on the operator's current location. If the operator stays in a specific location for a long time, the optimization unit can perform optimization related to that location. This makes it possible to select the optimal optimization method by taking into account the geographical location information. Some or all of the above-mentioned processing in the optimization unit may be performed using AI, for example, or may be performed without using AI. For example, the optimization unit can input the geographical location information of the operator into AI, which then selects the optimal optimization method.

[0046] The optimization unit can analyze the social media activity of the operator during optimization and propose optimization measures. The optimization unit can propose optimal optimization measures based on, for example, information shared by the operator on social media. The optimization unit can use AI to analyze the social media activity of the operator during optimization and propose optimization measures. For example, the optimization unit can propose business-related optimization measures from the operator's social media activity. The optimization unit can propose optimal optimization measures based on topics in which the operator has shown interest on social media. In this way, optimal optimization measures can be proposed by analyzing social media activity. Some or all of the above-mentioned processing in the optimization unit may be performed using AI, for example, or may be performed without using AI. For example, the optimization unit can input the operator's social media activity data into AI, which then proposes optimization measures.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The analysis unit can analyze the past performance data of agents and predict their future performance. For example, based on past call duration and break time data, it can grasp the performance trend of agents and predict future workloads. Furthermore, the analysis unit can evaluate the suitability of agents for specific tasks based on the past performance data of agents. This makes it possible to allocate tasks according to the suitability of each agent. The analysis unit can also propose training programs to improve the performance of agents based on past data.

[0049] The collection unit collects health data of operators, and the analysis unit can evaluate their health status based on that data. For example, vital data such as heart rate and stress level is collected, and the health status of operators is monitored in real time. The collection unit can detect health risks early based on the operator's health data. Furthermore, the collection unit can suggest breaks and shift adjustments based on the operator's health data. This supports the health management of operators and improves work efficiency.

[0050] The analysis unit can predict future workloads based on the operators' past work data and propose optimal resource allocation. For example, it can analyze past call volume and break time data to predict future workloads. The analysis unit can optimize the operators' shifts and break times based on the predicted workloads. Furthermore, the analysis unit can also propose training programs according to future workloads. This makes it possible to allocate resources according to future workloads, thereby improving work efficiency.

[0051] The collection unit collects data on the operator's work environment, and the analysis unit can make suggestions for improving the work environment based on that data. For example, the collection unit collects data on the temperature and lighting in the operator's workspace and makes suggestions to provide a comfortable work environment. The collection unit can optimize the work environment based on the operator's work environment data. Furthermore, the collection unit can also detect health risks early based on the operator's work environment data. This can improve the operator's work environment and increase work efficiency.

[0052] The optimization department can propose optimal work allocation based on the operator's past work data. For example, it analyzes past work data to understand the operator's strengths and weaknesses. The optimization department can propose work allocation according to the operator's strengths and weaknesses. Furthermore, the optimization department can also propose training programs for the operator's weaknesses. This makes it possible to make the most of the operator's skills and improve work efficiency.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The collection unit collects the operator's operation data. The operator's operation data includes login time, logout time, call time, break time, etc. The collection unit collects data such as the time the operator logged into the system, the time the call started, and the time the call ended, and collects detailed data to accurately grasp the operator's operation status. Step 2: The analysis unit analyzes the data collected by the collection unit. Based on the collected data, the analysis unit analyzes the operating status of each operator in real time and detects abnormal operating conditions, such as when call times are too long or break times are too short. The analysis unit can detect abnormal operating conditions using AI. Step 3: The optimization unit optimizes the operator's shifts and break times based on the analysis results obtained by the analysis unit. The optimization unit aims to improve work efficiency by using AI to automatically suggest breaks when an operator's call time is too long and optimizing the operator's shifts. The optimization unit uses AI to optimize the operator's shifts and break times based on the analysis results.

[0055] (Example 2) A system according to an embodiment of the present invention uses AI to improve the operational management of agents at corporate contact centers. This system collects operational data for each agent, analyzes it using AI, grasps the agent's operational status in real time, and optimizes the agent's shifts and break times based on the analysis results. For example, detailed data such as each agent's login time, logout time, call time, and break time are collected. By collecting data such as the time the agent logged into the system, the time the call started, and the time the call ended, the agent's operational status can be accurately grasped. Next, AI analyzes the collected data. Based on the collected data, the AI ​​analyzes the operational status of each agent in real time. For example, the AI ​​detects abnormal operational status, such as when an agent's call time is too long or when the break time is too short. This allows the agent's operational status to be grasped in real time. Furthermore, the AI ​​optimizes the agent's shifts and break times based on the analysis results. For example, if an agent's call time is too long, the AI ​​can automatically suggest a break. Furthermore, optimizing the agent's shifts improves operational efficiency. This reduces the agent's workload and improves operational productivity. This mechanism enables efficient agent operational management. By understanding the operating status of agents in real time and optimizing shifts and break times, work productivity can be improved. For example, if an agent is on the phone for too long, AI can automatically suggest taking a break, thereby reducing the agent's workload. In addition, optimizing agent shifts can improve work efficiency. This improves agent satisfaction and improves work productivity. As a result, a system that uses AI to improve agent operating management at corporate contact centers can improve work productivity by understanding the operating status of agents in real time and optimizing shifts and break times.

[0056] An operation management system according to an embodiment includes a collection unit, an analysis unit, and an optimization unit. The collection unit collects operation data of agents. The operation data of agents includes, but is not limited to, login times, logout times, call times, and break times. The collection unit collects data such as the time an agent logs into the system, the time a call starts, and the time a call ends. The collection unit can collect detailed data to accurately grasp the operation status of agents. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the operation status of each agent in real time, for example, based on the collected data. The analysis unit can detect abnormal operation status, such as when an agent's call time is too long or when a break time is too short. The analysis unit can detect abnormal operation status based on the collected data using AI. The optimization unit optimizes agent shifts and break times based on the analysis results obtained by the analysis unit. For example, if an agent's call time is too long, the AI ​​can automatically suggest a break. The optimization unit can improve work efficiency by optimizing agent shifts. The optimization unit can use AI to optimize operator shifts and break times based on the analysis results. As a result, the operation management system according to the embodiment can grasp the operating status of operators in real time and optimize shifts and break times, thereby improving work productivity.

[0057] The collection unit can collect detailed data on the login time, logout time, call time, and break time of the operator. The collection unit collects, for example, data such as the time when the operator logged into the system, the time when the call started, and the time when the call ended. The collection unit can collect detailed data to accurately grasp the operating status of the operator. For example, the collection unit collects detailed data such as the login time, logout time, call time, and break time of the operator. By collecting detailed operating data in this way, the operating status of the operator can be accurately grasped. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data such as the operator's login time and call time into AI, which can analyze and collect the data.

[0058] The analysis unit can analyze the operating status of each agent in real time based on the collected data. For example, the analysis unit analyzes the operating status of each agent in real time based on the collected data. The analysis unit can detect abnormal operating status, such as when an agent's call time is too long or when their break time is too short. The analysis unit can detect abnormal operating status based on the collected data using AI. For example, the analysis unit detects abnormal operating status when an agent's call time exceeds a certain time or when their break time is less than a certain time. In this way, by analyzing the operating status in real time, abnormal operating status can be immediately detected. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the collected data into AI, which analyzes the data and detects abnormal operating status.

[0059] The analysis unit can detect an abnormal operating condition when an operator's call time exceeds a certain time or when a break time is less than a certain time. The analysis unit can detect an abnormal operating condition, for example, when an operator's call time exceeds a certain time or when a break time is less than a certain time. The analysis unit can detect an abnormal operating condition based on collected data using AI. For example, the analysis unit can detect an abnormal operating condition when an operator's call time is too long or when a break time is too short. By detecting an abnormal operating condition, the burden on the operator can be reduced. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input collected data into AI, which can analyze the data and detect an abnormal operating condition.

[0060] The optimization unit can optimize the shifts and break times of the operators based on the analysis results. The optimization unit, for example, optimizes the shifts and break times of the operators based on the analysis results. The optimization unit can optimize the shifts and break times of the operators based on the analysis results using AI. For example, the optimization unit can automatically suggest a break if the operator's call time is too long. The optimization unit can improve work efficiency by optimizing the operator's shifts. As a result, work efficiency can be improved by optimizing the shifts and break times. Some or all of the above-mentioned processing in the optimization unit may be performed using AI, for example, or may be performed without using AI. For example, the optimization unit can input the analysis results to AI, which can optimize the shifts and break times.

[0061] The optimization unit can have the AI ​​automatically suggest a break when an operator's call time exceeds a certain time. For example, the optimization unit can have the AI ​​automatically suggest a break when an operator's call time exceeds a certain time. The optimization unit can use the AI ​​to optimize the operator's shifts and break times based on the analysis results. For example, the optimization unit can have the AI ​​automatically suggest a break when an operator's call time is too long. This can reduce the operator's workload by automatically suggesting a break when the call time is too long. Some or all of the above-mentioned processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input the analysis results into the AI, which can then optimize the shifts and break times.

[0062] The collection unit can estimate the operator's emotions and adjust the timing of collecting operation data based on the estimated operator's emotions. For example, if the operator is feeling stressed, the collection unit delays the collection timing to reduce the burden on the operator. The collection unit can use AI to estimate the operator's emotions and adjust the timing of collecting operation data based on the estimated operator's emotions. For example, if the operator is relaxed, the collection unit collects data at the normal collection timing. If the operator is tired, the collection unit can shorten the collection timing to quickly collect data. This reduces the burden on the operator by adjusting the collection timing according to the operator's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the operator's emotion data into AI, which can adjust the collection timing.

[0063] The collection unit can analyze the operator's past operation data and select the optimal collection method. The collection unit, for example, selects the most efficient collection timing from the operator's past operation data. The collection unit can analyze the operator's past operation data and select the optimal collection method using AI. For example, the collection unit adjusts the collection frequency based on the operator's past operation data. The collection unit can analyze the operator's past operation data and concentrate collection during specific time periods. This allows the optimal collection method to be selected by analyzing the past operation data. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the operator's past operation data into AI, which can select the optimal collection method.

[0064] The collection unit can filter the operation data based on the operator's current work status and areas of interest when collecting the operation data. For example, when the operator is busy, the collection unit prioritizes collecting only important data. The collection unit can use AI to filter the operation data based on the operator's current work status and areas of interest when collecting the operation data. For example, when the operator is concentrating on a specific task, the collection unit prioritizes collecting data related to that task. The collection unit can filter and collect related data based on the operator's areas of interest. This allows important data to be collected preferentially by filtering the data based on the work status and areas of interest. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input data on the operator's work status and areas of interest into AI, which can then perform the filtering.

[0065] The collection unit can estimate the operator's emotions and determine the priority of data to be collected based on the estimated operator's emotions. For example, when the operator is stressed, the collection unit prioritizes collecting data of high importance. The collection unit can estimate the operator's emotions using AI and determine the priority of data to be collected based on the estimated operator's emotions. For example, when the operator is relaxed, the collection unit collects data with normal priority. When the operator is tired, the collection unit can prioritize collecting data that can be collected quickly. This allows important data to be collected preferentially by determining the priority of data according to the operator'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 collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the operator's emotion data into AI, which then prioritizes the data.

[0066] When collecting operation data, the collection unit can prioritize collecting important data by taking into account the operator's geographical location information. For example, if the operator is working in a specific area, the collection unit prioritizes collecting data related to that area. When collecting operation data, the collection unit can use AI to prioritize collecting highly relevant data by taking into account the operator's geographical location information. For example, if the operator is moving, the collection unit collects relevant data based on the operator's current location. If the operator stays in a specific location for a long time, the collection unit can prioritize collecting data related to that location. In this way, highly relevant data can be collected preferentially by taking into account the geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the operator's geographical location information into AI, which can then prioritize collecting highly relevant data.

[0067] The collection unit can analyze the social media activities of the operator when collecting operation data and collect related data. The collection unit can collect related data based on, for example, information shared by the operator on social media. The collection unit can use AI to analyze the social media activities of the operator when collecting operation data and collect related data. For example, the collection unit extracts and collects work-related data from the operator's social media activities. The collection unit can collect related data based on topics in which the operator has shown interest on social media. This allows for efficient collection of related data by analyzing social media activities. Some or all of the above-described processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the operator's social media activity data into AI, which can collect related data.

[0068] The analysis unit can estimate the operator's emotions and adjust the way the analysis is presented based on the estimated operator's emotions. For example, if the operator is stressed, the analysis unit provides a simple, highly visible analysis result. The analysis unit can use AI to estimate the operator's emotions and adjust the way the analysis is presented based on the estimated operator's emotions. For example, if the operator is relaxed, the analysis unit can provide a detailed analysis result. If the operator is tired, the analysis unit can provide a concise analysis result that focuses on the main points. This allows for highly visible analysis results to be provided by adjusting the way the analysis is presented based on the operator'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 analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the operator's emotion data into AI, which can then adjust the way the analysis is presented.

[0069] The analysis unit can adjust the level of detail of the analysis based on the priority of the operational data during analysis. The analysis unit, for example, performs a detailed analysis on data with high importance. The analysis unit can adjust the level of detail of the analysis based on the priority of the operational data during analysis using AI. For example, the analysis unit performs a simplified analysis on data with low importance. The analysis unit can analyze data with an appropriate level of detail on data with medium importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the operational data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the priority of the operational data to AI, which can adjust the level of detail of the analysis.

[0070] The analysis unit can apply different analysis algorithms depending on the category of operation data during analysis. For example, the analysis unit applies a call quality analysis algorithm to call time data. The analysis unit can use AI to apply different analysis algorithms depending on the category of operation data during analysis. For example, the analysis unit can apply a rest pattern analysis algorithm to break time data. The analysis unit can apply an operation time analysis algorithm to login / logout time data. This enables highly accurate analysis by applying different analysis algorithms depending on the category of operation data. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the category of operation data into AI, which can then apply different analysis algorithms.

[0071] The analysis unit can estimate the operator's emotions and adjust the length of the analysis based on the estimated operator's emotions. For example, if the operator is stressed, the analysis unit provides a short and concise analysis result. The analysis unit can use AI to estimate the operator's emotions and adjust the length of the analysis based on the estimated operator's emotions. For example, if the operator is relaxed, the analysis unit can provide a detailed analysis result. If the operator is tired, the analysis unit can provide a concise analysis result. This allows appropriate analysis results to be provided by adjusting the length of the analysis according to the operator'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 analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the operator's emotion data into AI, which can then adjust the length of the analysis.

[0072] During analysis, the analysis unit can determine the order of analysis based on the collection date of the operation data. The analysis unit, for example, prioritizes analyzing the most recent data. During analysis, the analysis unit can use AI to determine the order of analysis based on the collection date of the operation data. For example, the analysis unit prioritizes analyzing current data while referring to past data. The analysis unit can prioritize analyzing data collected during a specific period. This allows the most recent data to be analyzed preferentially by determining the analysis priority based on the collection date of the operation data. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the collection date of the operation data into AI, and the AI ​​can determine the order of analysis.

[0073] The analysis unit can adjust the order of analysis based on the relevance of the operational data during analysis. The analysis unit, for example, prioritizes analysis of highly relevant data. The analysis unit can adjust the order of analysis based on the relevance of the operational data during analysis using AI. For example, the analysis unit analyzes data with low relevance later. The analysis unit can analyze data with medium relevance in an appropriate order. This enables efficient analysis by adjusting the order of analysis based on the relevance of the operational data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the relevance of the operational data to AI, which can adjust the order of analysis.

[0074] The optimization unit can estimate the operator's emotions and adjust the optimization method based on the estimated operator's emotions. For example, if the operator is stressed, the optimization unit can optimize by increasing the operator's rest time. The optimization unit can estimate the operator's emotions using AI and adjust the optimization method based on the estimated operator's emotions. For example, if the operator is relaxed, the optimization unit can apply a normal optimization method. If the operator is tired, the optimization unit can optimize by shortening the shift. This enables more appropriate optimization by adjusting the optimization method according to the operator's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the optimization unit can be performed using AI, for example, or without AI. For example, the optimization unit can input the operator's emotion data into AI, which can then adjust the optimization method.

[0075] During optimization, the optimization unit can analyze the operator's past operation data to select the optimal optimization method. For example, the optimization unit selects the most efficient shift pattern from the operator's past operation data. During optimization, the optimization unit can use AI to analyze the operator's past operation data to select the optimal optimization method. For example, the optimization unit selects the optimal timing for breaks based on the operator's past operation data. The optimization unit can analyze the operator's past operation data and propose optimal work allocation. In this way, the optimal optimization method can be selected by analyzing the past operation data. Some or all of the above-mentioned processing in the optimization unit may be performed using AI, for example, or may be performed without using AI. For example, the optimization unit can input the operator's past operation data into AI, which can select the optimal optimization method.

[0076] The optimization unit can adjust the optimization measures based on the current work situation of the operator during optimization. For example, if the operator is busy, the optimization unit performs optimization to increase break times. The optimization unit can use AI to adjust the optimization measures based on the current work situation of the operator during optimization. For example, if the operator is concentrating on a specific task, the optimization unit performs optimization related to that task. The optimization unit can customize shifts and work assignments according to the operator's work situation. This enables more efficient optimization by customizing the optimization measures based on the current work situation. Some or all of the above-mentioned processing in the optimization unit may be performed using AI, for example, or may be performed without using AI. For example, the optimization unit can input the operator's work situation data into AI, which can adjust the optimization measures.

[0077] The optimization unit can estimate the operator's emotions and determine the optimization priority based on the estimated operator's emotions. For example, if the operator is feeling stressed, the optimization unit can prioritize optimization of break times. The optimization unit can estimate the operator's emotions using AI and determine the optimization priority based on the estimated operator's emotions. For example, if the operator is relaxed, the optimization unit can perform normal optimization. If the operator is tired, the optimization unit can prioritize shift optimization. This enables more appropriate optimization by determining the optimization priority based on the operator's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 optimization unit can be performed using AI, for example, or without AI. For example, the optimization unit can input the operator's emotion data into AI, which can then determine the optimization priority.

[0078] The optimization unit can select an appropriate optimization method by taking into account the geographical location information of the operator during optimization. For example, if the operator works in a specific area, the optimization unit performs optimization related to that area. The optimization unit can use AI to select the optimal optimization method by taking into account the geographical location information of the operator during optimization. For example, if the operator is traveling, the optimization unit selects the optimal optimization method based on the operator's current location. If the operator stays in a specific location for a long time, the optimization unit can perform optimization related to that location. This makes it possible to select the optimal optimization method by taking into account the geographical location information. Some or all of the above-mentioned processing in the optimization unit may be performed using AI, for example, or may be performed without using AI. For example, the optimization unit can input the geographical location information of the operator into AI, which then selects the optimal optimization method.

[0079] The optimization unit can analyze the social media activity of the operator during optimization and propose optimization measures. The optimization unit can propose optimal optimization measures based on, for example, information shared by the operator on social media. The optimization unit can use AI to analyze the social media activity of the operator during optimization and propose optimization measures. For example, the optimization unit can propose business-related optimization measures from the operator's social media activity. The optimization unit can propose optimal optimization measures based on topics in which the operator has shown interest on social media. In this way, optimal optimization measures can be proposed by analyzing social media activity. Some or all of the above-mentioned processing in the optimization unit may be performed using AI, for example, or may be performed without using AI. For example, the optimization unit can input the operator's social media activity data into AI, which then proposes optimization measures. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and optimization unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 and collects operation data such as the operator's login time and call time. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the operator's operation status in real time based on the collected data. The optimization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and optimizes the operator's shifts and break times based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and optimization unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 and collects operation data such as the operator's login time and call time. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the operator's operation status in real time based on the collected data. The optimization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and optimizes the operator's shifts and break times based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and optimization unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314 and collects operation data such as the operator's login time and call time. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the operator's operation status in real time based on the collected data. The optimization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and optimizes the operator's shifts and break times based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and optimization unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 and collects operation data such as the operator's login time and call time. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the operator's operation status in real time based on the collected data. The optimization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and optimizes the operator's shifts and break times based on the analysis results.

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

[0081] The analysis unit can analyze the past performance data of agents and predict their future performance. For example, based on past call duration and break time data, it can grasp the performance trend of agents and predict future workloads. Furthermore, the analysis unit can evaluate the suitability of agents for specific tasks based on the past performance data of agents. This makes it possible to allocate tasks according to the suitability of each agent. The analysis unit can also propose training programs to improve the performance of agents based on past data.

[0082] The collection unit collects health data of operators, and the analysis unit can evaluate their health status based on that data. For example, vital data such as heart rate and stress level is collected, and the health status of operators is monitored in real time. The collection unit can detect health risks early based on the operator's health data. Furthermore, the collection unit can suggest breaks and shift adjustments based on the operator's health data. This supports the health management of operators and improves work efficiency.

[0083] The analysis unit can estimate the operator's emotions and evaluate the operator's stress level based on the estimated emotions. For example, it can estimate the operator's stress level by analyzing the tone of voice and choice of words during a call. If the operator's stress level is high, the analysis unit can suggest actions to reduce stress. Furthermore, the analysis unit can also suggest training programs for stress management based on the operator's emotion data. This supports the operator's stress management and improves work efficiency.

[0084] The optimization unit can estimate the emotions of the agents and suggest actions to improve the motivation of the agents based on the estimated emotions. For example, if the agent is tired, it can suggest a break to refresh the agent. The optimization unit can also suggest an incentive program to improve motivation based on the agent's emotion data. Furthermore, the optimization unit can suggest team building activities based on the agent's emotion data. This can improve the motivation of the agents and improve work efficiency.

[0085] The collection unit can estimate the emotion of the agent and adjust the timing for collecting the agent's feedback based on the estimated emotion. For example, if the agent is relaxed, the timing for collecting the feedback is selected. The collection unit can also adjust the method for collecting the feedback based on the agent's emotion data. Furthermore, the collection unit can customize the content of the feedback based on the agent's emotion data. This makes it possible to collect feedback according to the agent's emotion, which can be useful for improving work.

[0086] The analysis unit can predict future workloads based on the operators' past work data and propose optimal resource allocation. For example, it can analyze past call volume and break time data to predict future workloads. The analysis unit can optimize the operators' shifts and break times based on the predicted workloads. Furthermore, the analysis unit can also propose training programs according to future workloads. This makes it possible to allocate resources according to future workloads, thereby improving work efficiency.

[0087] The collection unit collects data on the operator's work environment, and the analysis unit can make suggestions for improving the work environment based on that data. For example, the collection unit collects data on the temperature and lighting in the operator's workspace and makes suggestions to provide a comfortable work environment. The collection unit can optimize the work environment based on the operator's work environment data. Furthermore, the collection unit can also detect health risks early based on the operator's work environment data. This can improve the operator's work environment and increase work efficiency.

[0088] The optimization department can propose optimal work allocation based on the operator's past work data. For example, it analyzes past work data to understand the operator's strengths and weaknesses. The optimization department can propose work allocation according to the operator's strengths and weaknesses. Furthermore, the optimization department can also propose training programs for the operator's weaknesses. This makes it possible to make the most of the operator's skills and improve work efficiency.

[0089] The analysis unit can estimate the emotion of the agent and evaluate the agent's performance based on the estimated emotion. For example, if the agent is feeling stressed, the performance evaluation can be adjusted. The analysis unit can also adjust the performance evaluation criteria based on the agent's emotion data. Furthermore, the analysis unit can provide feedback to improve performance based on the agent's emotion data. This makes it possible to evaluate the agent's performance according to their emotion, which can be useful for improving work.

[0090] The optimization unit can estimate the emotions of the operators and adjust the shifts and break times of the operators based on the estimated emotions. For example, if the operator is tired, it can suggest increasing the break time. The optimization unit can also adjust the shifts based on the emotion data of the operators. Furthermore, the optimization unit can make suggestions to reduce the workload based on the emotion data of the operators. This makes it possible to adjust the shifts and break times according to the emotions of the operators, thereby improving work efficiency.

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

[0092] Step 1: The collection unit collects the operator's operation data. The operator's operation data includes login time, logout time, call time, break time, etc. The collection unit collects data such as the time the operator logged into the system, the time the call started, and the time the call ended, and collects detailed data to accurately grasp the operator's operation status. Step 2: The analysis unit analyzes the data collected by the collection unit. Based on the collected data, the analysis unit analyzes the operating status of each operator in real time and detects abnormal operating conditions, such as when call times are too long or break times are too short. The analysis unit can detect abnormal operating conditions using AI. Step 3: The optimization unit optimizes the operator's shifts and break times based on the analysis results obtained by the analysis unit. The optimization unit aims to improve work efficiency by using AI to automatically suggest breaks when an operator's call time is too long and optimizing the operator's shifts. The optimization unit uses AI to optimize the operator's shifts and break times based on the analysis results.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0098] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0127] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0137] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0144] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0146] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0147] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0148] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0149] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0150] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0151] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0152] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0153] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0154] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0155] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0156] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0157] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0158] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0159] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0160] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0161] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0162] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, 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.

[0163] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0164] [Explanation of symbols]

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

Claims

1. a collection unit that collects operation data of operators; an analysis unit that analyzes the data collected by the collection unit; an optimization unit that adjusts the shifts and rest times of operators based on the analysis results obtained by the analysis unit; Equipped with A system characterized by:

2. The collecting unit Collect detailed data on operator login times, logout times, call times, and break times 2. The system of claim 1.

3. The analysis unit Based on the collected data, the operating status of each operator is analyzed in real time.

2. The system of claim 1.

4. The analysis unit Detect abnormal operating conditions when an operator's call time exceeds a certain time or when their break time is less than a certain time.

2. The system of claim 1.

5. The optimization unit Optimize operator shifts and break times based on analysis results 2. The system of claim 1.

6. The optimization unit If an operator's call time exceeds a certain amount of time, AI will automatically suggest a break.

2. The system of claim 1.

7. The collecting unit Estimate the operator's emotions and adjust the timing of collecting operation data based on the estimated operator emotions.

2. The system of claim 1.

8. The collecting unit Analyze the operator's past operating data and select the optimal collection method 2. The system of claim 1.

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