system

Abstract

The system according to the embodiment aims to automatically update and efficiently provide talk scripts and success stories used by operators at customer centers. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and an access unit. The collection unit automatically collects talk scripts used by customer center operators or successful cases in interactions with customers. The analysis unit analyzes the information collected by the collection unit and identifies effective talk scripts or successful cases. The generation unit generates the latest talk scripts or successful cases based on the analysis results identified by the analysis unit. The access unit integrates the information generated by the generation unit into the customer center's system, making it accessible to operators and customers as needed.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, updates to the talk scripts and success stories used by customer center operators were done manually, making it difficult to efficiently provide the latest information.

[0005] The system according to the embodiment aims to automatically update and efficiently provide talk scripts and success stories used by operators at customer centers. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and an access unit. The collection unit automatically collects talk scripts used by operators at the customer center or successful cases in interactions with customers. The analysis unit analyzes the information collected by the collection unit and identifies effective talk scripts or successful cases. The generation unit generates the latest talk scripts or successful cases based on the analysis results identified by the analysis unit. The access unit integrates the information generated by the generation unit into the customer center's system, allowing operators and customers to access it as needed. [Effects of the Invention]

[0007] The system according to the embodiment can automatically update and efficiently provide talk scripts and success stories used by operators at customer centers. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An information provision system specialized for MNP porting counters according to an embodiment of the present invention uses a generation AI to chronologically track the latest talk scripts and success stories specialized for MNP (Mobile Number Portability) porting counters and make them accessible to customer centers and customers as needed. In this system, the generation AI collects talk scripts and success stories used at MNP porting counters and organizes them chronologically. Next, the generation AI analyzes the collected information and generates the latest talk scripts and success stories. This allows customer center operators and customers to access the latest information as needed. First, the generation AI collects talk scripts and success stories used at MNP porting counters. In this process, the generation AI automatically collects talk scripts used by customer center operators and successful cases in interactions with customers. For example, this includes explanations given by operators to customers and specific interactions when customers decide to port. Next, the generation AI analyzes the collected information and organizes it chronologically. The generation AI analyzes the collected talk scripts and success stories to identify which talk scripts were effective and which cases were successful. This allows the latest talk scripts and success stories to be organized chronologically. Furthermore, the generation AI generates the latest talk scripts and success stories based on the information analyzed. Based on the analysis results, the generation AI generates the latest talk scripts for use by customer center operators and success stories for customers to refer to. For example, these may include explanations that operators should give to customers and specific approaches when customers decide to transfer. Finally, the latest generated talk scripts and success stories are made accessible to customer center operators and customers as needed. The generation AI integrates the generated information into the customer center system, allowing operators to access it as needed. Customers can also refer to the latest success stories according to their own circumstances. This allows customer center operators to always provide customer support based on the latest information, and customers can obtain the information that is most appropriate for them.This allows the MNP transfer window specialized information provision system to provide customer center operators and customers with access to the latest talk scripts and success stories.

[0029] The MNP porting-out window specialized information provision system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and an access unit. The collection unit automatically collects talk scripts used by customer center operators and successful cases in their interactions with customers. The collection unit can collect, for example, explanations given by operators to customers and specific interactions when customers decide to port out. The collection unit can also use a generation AI to analyze the content of the operator's conversation in real time to identify effective talk scripts and success cases. For example, the collection unit causes the generation AI to analyze the content of the operator's conversation and extract effective talk scripts. The collection unit can also identify and collect successful cases in the generation AI's interactions with customers. The analysis unit analyzes the information collected by the collection unit to identify effective talk scripts and success cases. For example, the analysis unit can analyze the collected talk scripts and success cases to identify which talk scripts were effective and which cases were successful. The analysis unit can also use the generation AI to analyze the collected information to identify effective talk scripts and success cases. For example, the analysis unit analyzes the information collected by the generation AI and identifies effective talk scripts. The generation unit generates the latest talk scripts and success stories based on the analysis results identified by the analysis unit. For example, the generation unit can generate the latest talk scripts to be used by customer center operators and success stories for customers to refer to based on the analysis results. The generation unit can also use the generation AI to generate the latest talk scripts and success stories based on the analysis results. For example, the generation unit generates the latest talk scripts based on the analysis results. The access unit integrates the information generated by the generation unit into the customer center system so that operators and customers can access them as needed. For example, the access unit can integrate the generated talk scripts and success stories into the customer center system so that operators can access them as needed. The access unit can also make the generated information available to customers.For example, the access unit allows customers to refer to the generated success stories. As a result, the MNP porting-out specialized information providing system according to the embodiment allows customer center operators and customers to access the latest talk scripts and success stories.

[0030] The collection unit can analyze the operator's past performance data at the time of collection and select an appropriate collection method. For example, the collection unit can analyze the operator's past success stories and select a collection method for similar situations. The collection unit can also analyze the operator's past failure stories and select a collection method to avoid similar mistakes. Furthermore, the collection unit can select the most effective collection timing based on the operator's past performance data. This improves the accuracy of collection by selecting the optimal collection method based on the operator's past performance data. 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 the operator's performance data into the generation AI and have the generation AI select the optimal collection method.

[0031] The collection unit can filter talk scripts or success stories based on customer attribute information when collecting them. For example, the collection unit collects appropriate talk scripts based on the customer's age group. The collection unit can also collect region-specific success stories based on the customer's regional information. Furthermore, the collection unit can collect related talk scripts and success stories based on the customer's purchase history. This allows for filtering based on customer attribute information, thereby collecting more relevant information. Some or all of the above-described processing by the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input customer attribute information into a generation AI and have the generation AI perform filtering.

[0032] The collection unit can prioritize collecting highly relevant information based on the operator's geographical location information during collection. For example, if the operator is in an urban area, the collection unit can prioritize collecting talk scripts specific to the urban area. Also, if the operator is in a rural area, the collection unit can prioritize collecting success stories specific to that region. Furthermore, if the operator is overseas, the collection unit can prioritize collecting talk scripts suitable for the culture of that region. In this way, by taking the operator's geographical location information into consideration, region-specific information can be collected preferentially. 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 the generation AI and cause the generation AI to collect highly relevant information.

[0033] The collection unit can analyze the operator's social media activity during collection and collect related talk scripts or success stories. For example, the collection unit can collect success stories shared by the operator on social media. The collection unit can also collect talk scripts including areas for improvement based on feedback received by the operator on social media. Furthermore, the collection unit can collect the latest talk scripts based on industry trends followed by the operator on social media. In this way, the latest talk scripts and success stories can be collected by analyzing the operator's social media activity. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the operator's social media activity data into a generation AI and cause the generation AI to collect related talk scripts and success stories.

[0034] The analysis unit can adjust the level of detail of the analysis based on the importance of the talk script or success story during analysis. For example, the analysis unit performs a detailed analysis of talk scripts with high importance. The analysis unit can also perform a simplified analysis of talk scripts with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the success story. This improves the accuracy of the analysis by adjusting the level of detail of the analysis based on the importance of the talk script or success story. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the talk script or success story into the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0035] The analysis unit can apply different analysis algorithms depending on the category of the talk script or success story during analysis. The analysis unit selects an appropriate analysis algorithm, for example, depending on the category of the talk script. The analysis unit can also apply different analysis algorithms depending on the category of the success story. Furthermore, the analysis unit can select the optimal analysis algorithm for each category and perform the analysis. By applying the optimal analysis algorithm for each category, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the talk script or success story into the generation AI and cause the generation AI to apply different analysis algorithms.

[0036] The analysis unit can determine the analysis priority based on the submission date of the talk script or success story during analysis. For example, the analysis unit prioritizes analysis of the most recent talk script. The analysis unit can also postpone analysis of talk scripts that were submitted earlier. Furthermore, the analysis unit can also determine the analysis priority based on the submission date of the success story. In this way, by determining the analysis priority based on the submission date, the most recent information can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission date of the talk script or success story to the generation AI and have the generation AI determine the analysis priority.

[0037] The analysis unit can adjust the order of analysis based on the relevance of talk scripts or success stories during analysis. For example, the analysis unit prioritizes analysis of highly relevant talk scripts. The analysis unit can also postpone analysis of less relevant talk scripts. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of success stories. By adjusting the order of analysis based on relevance, more relevant information can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of talk scripts and success stories into the generation AI and have the generation AI adjust the order of analysis.

[0038] The generation unit can adjust the level of detail of the generation based on the importance of the analysis result during generation. For example, the generation unit generates a detailed talk script for an analysis result with high importance. The generation unit can also generate a simplified talk script for an analysis result with low importance. Furthermore, the generation unit can determine the priority of generation based on the importance of success stories. This improves the accuracy of generation by adjusting the level of detail of generation based on the importance of the analysis result. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input importance data of the analysis result to the generation AI and cause the generation AI to adjust the level of detail of generation.

[0039] The generation unit can apply different generation algorithms depending on the category of the analysis results during generation. The generation unit selects an appropriate generation algorithm depending on, for example, the category of the talk script. The generation unit can also apply different generation algorithms depending on the category of the success story. Furthermore, the generation unit can select the optimal generation algorithm for each category and perform generation. By applying the optimal generation algorithm for each category, the accuracy of generation is improved. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input category data of the analysis results into the generation AI and cause the generation AI to apply different generation algorithms.

[0040] The generation unit can determine the generation priority based on the submission time of the analysis results at the time of generation. For example, the generation unit prioritizes the generation of the most recent analysis results. The generation unit can also postpone analysis results that were submitted earlier. Furthermore, the generation unit can determine the generation priority based on the submission time of success stories. In this way, by determining the generation priority based on the submission time, the most recent information can be generated preferentially. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the submission time of the analysis results into the generation AI and have the generation AI determine the generation priority.

[0041] The generation unit can adjust the order of generation based on the relevance of the analysis results during generation. For example, the generation unit prioritizes the generation of highly relevant analysis results. The generation unit can also postpone analysis results with low relevance. Furthermore, the generation unit can adjust the order of generation based on the relevance of success stories. In this way, by adjusting the order of generation based on relevance, more relevant information can be generated preferentially. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input relevance data of the analysis results into the generation AI and cause the generation AI to adjust the order of generation.

[0042] The access unit can select an appropriate display method by referring to the user's past operation history when accessing the device. For example, the access unit can prioritize providing a display method that the user has used in the past. The access unit can also suggest an optimal display method based on the user's past operation history. Furthermore, the access unit can also provide a customized display method based on the user's past operation history. This improves the user experience by providing an optimal display method based on the user's past operation history. Some or all of the above-described processing in the access unit may be performed using, for example, AI, or may be performed without using AI. For example, the access unit can input the user's operation history data into a generation AI and cause the generation AI to select an optimal display method.

[0043] The access unit can select an appropriate display method based on the user's device information at the time of access. For example, if the user is using a smartphone, the access unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the access unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a desktop, the access unit can also provide a display method that includes detailed information. This makes it possible to provide a display method optimized for the device by taking the user's device information into consideration. Some or all of the above-described processing in the access unit may be performed using AI, for example, or may be performed without using AI. For example, the access unit can input the user's device information into the generation AI and cause the generation AI to select the optimal display method.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The analysis unit can predict customer needs and customize analysis results based on the customer's purchase history and past inquiries. For example, if a customer has shown interest in a particular service in the past, it can prioritize analyzing talk scripts related to that service. Also, if a customer has had a particular problem in the past, it can prioritize analyzing success stories that include solutions to that problem. Furthermore, it can analyze information about new services and products that the customer may be interested in based on the customer's purchase history. This makes it possible to provide more effective information by providing analysis results that meet the customer's needs.

[0046] The generation unit can generate optimal talk scripts based on the operator's past performance data. For example, it can analyze talk scripts that the operator has used successfully in the past and generate new talk scripts that incorporate those patterns. It can also analyze talk scripts that the operator has used unsuccessfully in the past and generate talk scripts that incorporate improvements to avoid those mistakes. Furthermore, it can determine the most effective timing for generating talk scripts based on the operator's past performance data. This improves the accuracy of generation by generating optimal talk scripts based on the operator's past performance data.

[0047] The collection unit can dynamically adjust the conversation scripts and success stories it collects based on real-time customer behavior data. For example, if a customer is viewing a specific page on a website, it can collect conversation scripts related to that page. Also, if a customer takes a specific action (for example, submitting an inquiry form), it can collect success stories related to that action. Furthermore, if a customer is active during a specific time period, it can collect conversation scripts that are effective during that time period. This makes it possible to collect more relevant information based on the customer's real-time behavior data.

[0048] The generator can adjust the complexity of the talk script based on the skill level of the operator when generating it. For example, it can generate a simple and basic talk script for a novice operator, and a more complex and advanced talk script for an experienced operator. Furthermore, it can adjust the level of detail of the information included in the talk script depending on the operator's skill level. This makes it possible to maximize the performance of the operator by generating a talk script that suits the operator's skill level.

[0049] The analysis unit can incorporate operator feedback during analysis to improve the analysis results. For example, if an operator provides positive feedback on a particular talk script, it will prioritize analyzing that talk script. Also, if an operator provides negative feedback on a particular success story, it can reanalyze that success story to identify areas for improvement. Furthermore, it can adjust the analysis algorithm based on operator feedback to provide more accurate analysis results. In this way, incorporating operator feedback improves the accuracy and reliability of the analysis results.

[0050] When accessing, the access unit can prioritize displaying the most appropriate information based on the user's past access history. For example, if the user has frequently referred to a specific talk script in the past, it can prioritize displaying that talk script. Also, if the user has referred to a specific success story in the past, it can prioritize displaying that success story. Furthermore, it can also suggest new related information based on the user's past access history. This improves the user experience by providing the most appropriate information based on the user's past access history.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The collection unit automatically collects the talk scripts used by customer center operators and successful examples of customer interactions. For example, it collects information such as explanations given by operators to customers and specific interactions when customers decide to transfer. The collection unit also uses a generation AI to analyze the content of operators' conversations in real time, identifying and collecting effective talk scripts and success examples. Step 2: The analysis unit analyzes the information collected by the collection unit and identifies effective talk scripts and success cases. For example, it analyzes the collected talk scripts and success cases and identifies which talk scripts were effective and which cases were successful. The analysis unit also uses a generative AI to analyze the collected information and identify effective talk scripts and success cases. Step 3: The generation unit generates the latest talk scripts and success stories based on the analysis results identified by the analysis unit. For example, based on the analysis results, the generation unit generates the latest talk scripts to be used by customer center operators and success stories for customers to refer to. The generation unit also uses generation AI to generate the latest talk scripts and success stories based on the analysis results. Step 4: The access unit integrates the information generated by the generation unit into the customer center system, making it accessible to operators and customers as needed. For example, the access unit integrates the generated talk scripts and success stories into the customer center system, making them accessible to operators as needed. The access unit also makes the generated information available to customers.

[0053] (Example 2) An information provision system specialized for MNP porting counters according to an embodiment of the present invention uses a generation AI to chronologically track the latest talk scripts and success stories specialized for MNP (Mobile Number Portability) porting counters and make them accessible to customer centers and customers as needed. In this system, the generation AI collects talk scripts and success stories used at MNP porting counters and organizes them chronologically. Next, the generation AI analyzes the collected information and generates the latest talk scripts and success stories. This allows customer center operators and customers to access the latest information as needed. First, the generation AI collects talk scripts and success stories used at MNP porting counters. In this process, the generation AI automatically collects talk scripts used by customer center operators and successful cases in interactions with customers. For example, this includes explanations given by operators to customers and specific interactions when customers decide to port. Next, the generation AI analyzes the collected information and organizes it chronologically. The generation AI analyzes the collected talk scripts and success stories to identify which talk scripts were effective and which cases were successful. This allows the latest talk scripts and success stories to be organized chronologically. Furthermore, the generation AI generates the latest talk scripts and success stories based on the information analyzed. Based on the analysis results, the generation AI generates the latest talk scripts for use by customer center operators and success stories for customers to refer to. For example, these may include explanations that operators should give to customers and specific approaches when customers decide to transfer. Finally, the latest generated talk scripts and success stories are made accessible to customer center operators and customers as needed. The generation AI integrates the generated information into the customer center system, allowing operators to access it as needed. Customers can also refer to the latest success stories according to their own circumstances. This allows customer center operators to always provide customer support based on the latest information, and customers can obtain the information that is most appropriate for them.This allows the MNP transfer window specialized information provision system to provide customer center operators and customers with access to the latest talk scripts and success stories.

[0054] The MNP porting-out window specialized information provision system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and an access unit. The collection unit automatically collects talk scripts used by customer center operators and successful cases in their interactions with customers. The collection unit can collect, for example, explanations given by operators to customers and specific interactions when customers decide to port out. The collection unit can also use a generation AI to analyze the content of the operator's conversation in real time to identify effective talk scripts and success cases. For example, the collection unit causes the generation AI to analyze the content of the operator's conversation and extract effective talk scripts. The collection unit can also identify and collect successful cases in the generation AI's interactions with customers. The analysis unit analyzes the information collected by the collection unit to identify effective talk scripts and success cases. For example, the analysis unit can analyze the collected talk scripts and success cases to identify which talk scripts were effective and which cases were successful. The analysis unit can also use the generation AI to analyze the collected information to identify effective talk scripts and success cases. For example, the analysis unit analyzes the information collected by the generation AI and identifies effective talk scripts. The generation unit generates the latest talk scripts and success stories based on the analysis results identified by the analysis unit. For example, the generation unit can generate the latest talk scripts to be used by customer center operators and success stories for customers to refer to based on the analysis results. The generation unit can also use the generation AI to generate the latest talk scripts and success stories based on the analysis results. For example, the generation unit generates the latest talk scripts based on the analysis results. The access unit integrates the information generated by the generation unit into the customer center system so that operators and customers can access them as needed. For example, the access unit can integrate the generated talk scripts and success stories into the customer center system so that operators can access them as needed. The access unit can also make the generated information available to customers.For example, the access unit allows customers to refer to the generated success stories. As a result, the MNP porting-out specialized information providing system according to the embodiment allows customer center operators and customers to access the latest talk scripts and success stories.

[0055] The collection unit can estimate the user's emotions and adjust the timing of collecting talk scripts or success stories based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit delays the collection timing to collect information when the user is relaxed. Furthermore, if the user is relaxed, the collection unit can immediately start collecting information to obtain detailed information. Furthermore, if the user is in a hurry, the collection unit can collect necessary information in a short time. This enables more appropriate information collection by adjusting the collection timing according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0056] The collection unit can analyze the operator's past performance data at the time of collection and select an appropriate collection method. For example, the collection unit can analyze the operator's past success stories and select a collection method for similar situations. The collection unit can also analyze the operator's past failure stories and select a collection method to avoid similar mistakes. Furthermore, the collection unit can select the most effective collection timing based on the operator's past performance data. This improves the accuracy of collection by selecting the optimal collection method based on the operator's past performance data. 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 the operator's performance data into the generation AI and have the generation AI select the optimal collection method.

[0057] The collection unit can filter talk scripts or success stories based on customer attribute information when collecting them. For example, the collection unit collects appropriate talk scripts based on the customer's age group. The collection unit can also collect region-specific success stories based on the customer's regional information. Furthermore, the collection unit can collect related talk scripts and success stories based on the customer's purchase history. This allows for filtering based on customer attribute information, thereby collecting more relevant information. Some or all of the above-described processing by the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input customer attribute information into a generation AI and have the generation AI perform filtering.

[0058] The collection unit can estimate the user's emotions and determine the priority of talk scripts or success stories to be collected based on the estimated user emotions. For example, if the user is feeling anxious, the collection unit can prioritize collecting talk scripts that provide a sense of security. Furthermore, if the user is excited, the collection unit can prioritize collecting success stories to calm the user's excitement. Furthermore, if the user is relaxed, the collection unit can prioritize collecting detailed talk scripts. This enables more effective information collection by determining the priority of information to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0059] The collection unit can prioritize collecting highly relevant information based on the operator's geographical location information during collection. For example, if the operator is in an urban area, the collection unit can prioritize collecting talk scripts specific to the urban area. Also, if the operator is in a rural area, the collection unit can prioritize collecting success stories specific to that region. Furthermore, if the operator is overseas, the collection unit can prioritize collecting talk scripts suitable for the culture of that region. In this way, by taking the operator's geographical location information into consideration, region-specific information can be collected preferentially. 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 the generation AI and cause the generation AI to collect highly relevant information.

[0060] The collection unit can analyze the operator's social media activity during collection and collect related talk scripts or success stories. For example, the collection unit can collect success stories shared by the operator on social media. The collection unit can also collect talk scripts including areas for improvement based on feedback received by the operator on social media. Furthermore, the collection unit can collect the latest talk scripts based on industry trends followed by the operator on social media. In this way, the latest talk scripts and success stories can be collected by analyzing the operator's social media activity. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the operator's social media activity data into a generation AI and cause the generation AI to collect related talk scripts and success stories.

[0061] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a summary analysis result. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0062] The analysis unit can adjust the level of detail of the analysis based on the importance of the talk script or success story during analysis. For example, the analysis unit performs a detailed analysis of talk scripts with high importance. The analysis unit can also perform a simplified analysis of talk scripts with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the success story. This improves the accuracy of the analysis by adjusting the level of detail of the analysis based on the importance of the talk script or success story. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the talk script or success story into the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0063] The analysis unit can apply different analysis algorithms depending on the category of the talk script or success story during analysis. The analysis unit selects an appropriate analysis algorithm, for example, depending on the category of the talk script. The analysis unit can also apply different analysis algorithms depending on the category of the success story. Furthermore, the analysis unit can select the optimal analysis algorithm for each category and perform the analysis. By applying the optimal analysis algorithm for each category, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the talk script or success story into the generation AI and cause the generation AI to apply different analysis algorithms.

[0064] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0065] The analysis unit can determine the analysis priority based on the submission date of the talk script or success story during analysis. For example, the analysis unit prioritizes analysis of the most recent talk script. The analysis unit can also postpone analysis of talk scripts that were submitted earlier. Furthermore, the analysis unit can also determine the analysis priority based on the submission date of the success story. In this way, by determining the analysis priority based on the submission date, the most recent information can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission date of the talk script or success story to the generation AI and have the generation AI determine the analysis priority.

[0066] The analysis unit can adjust the order of analysis based on the relevance of talk scripts or success stories during analysis. For example, the analysis unit prioritizes analysis of highly relevant talk scripts. The analysis unit can also postpone analysis of less relevant talk scripts. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of success stories. By adjusting the order of analysis based on relevance, more relevant information can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of talk scripts and success stories into the generation AI and have the generation AI adjust the order of analysis.

[0067] The generation unit can estimate the user's emotions and adjust the expression method of the generated talk script or success story based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate a talk script that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can also generate a talk script that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a talk script that adds visually stimulating effects. This allows for the generation of more appropriate talk scripts and success stories by adjusting the expression method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the expression method.

[0068] The generation unit can adjust the level of detail of the generation based on the importance of the analysis result during generation. For example, the generation unit generates a detailed talk script for an analysis result with high importance. The generation unit can also generate a simplified talk script for an analysis result with low importance. Furthermore, the generation unit can determine the priority of generation based on the importance of success stories. This improves the accuracy of generation by adjusting the level of detail of generation based on the importance of the analysis result. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input importance data of the analysis result to the generation AI and cause the generation AI to adjust the level of detail of generation.

[0069] The generation unit can apply different generation algorithms depending on the category of the analysis results during generation. The generation unit selects an appropriate generation algorithm depending on, for example, the category of the talk script. The generation unit can also apply different generation algorithms depending on the category of the success story. Furthermore, the generation unit can select the optimal generation algorithm for each category and perform generation. By applying the optimal generation algorithm for each category, the accuracy of generation is improved. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input category data of the analysis results into the generation AI and cause the generation AI to apply different generation algorithms.

[0070] The generation unit can estimate the user's emotions and adjust the length of the generated talk script or success story based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point talk script. If the user is relaxed, the generation unit can also generate a longer talk script with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a talk script with visually stimulating effects. This allows for adjusting the length of the generated information according to the user's emotions, thereby generating more appropriate talk scripts and success stories. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the information.

[0071] The generation unit can determine the generation priority based on the submission time of the analysis results at the time of generation. For example, the generation unit prioritizes the generation of the most recent analysis results. The generation unit can also postpone analysis results that were submitted earlier. Furthermore, the generation unit can determine the generation priority based on the submission time of success stories. In this way, by determining the generation priority based on the submission time, the most recent information can be generated preferentially. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the submission time of the analysis results into the generation AI and have the generation AI determine the generation priority.

[0072] The generation unit can adjust the order of generation based on the relevance of the analysis results during generation. For example, the generation unit prioritizes the generation of highly relevant analysis results. The generation unit can also postpone analysis results with low relevance. Furthermore, the generation unit can adjust the order of generation based on the relevance of success stories. In this way, by adjusting the order of generation based on relevance, more relevant information can be generated preferentially. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input relevance data of the analysis results into the generation AI and cause the generation AI to adjust the order of generation.

[0073] The access unit can estimate the user's emotions and adjust the display method of the access based on the estimated user's emotions. For example, if the user is nervous, the access unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the access unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the access unit can provide a display method that focuses on the main points. This allows for more appropriate information provision by adjusting the display method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the access unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the access unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0074] The access unit can select an appropriate display method by referring to the user's past operation history when accessing the device. For example, the access unit can prioritize providing a display method that the user has used in the past. The access unit can also suggest an optimal display method based on the user's past operation history. Furthermore, the access unit can also provide a customized display method based on the user's past operation history. This improves the user experience by providing an optimal display method based on the user's past operation history. Some or all of the above-described processing in the access unit may be performed using, for example, AI, or may be performed without using AI. For example, the access unit can input the user's operation history data into a generation AI and cause the generation AI to select an optimal display method.

[0075] The access unit can estimate the user's emotions and adjust the access operation procedure based on the estimated user emotions. For example, if the user is nervous, the access unit can simplify the operation procedure to allow intuitive operation. Furthermore, if the user is relaxed, the access unit can provide detailed operation procedures and increase customizable options. Furthermore, if the user is in a hurry, the access unit can provide the shortest operation procedure to allow quick access. This allows for more intuitive operation by adjusting the operation procedure according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the access unit can be performed using, for example, AI, or without AI. For example, the access unit can input the user's emotion data into the generation AI and have the generation AI adjust the operation procedure.

[0076] The access unit can select an appropriate display method based on the user's device information at the time of access. For example, if the user is using a smartphone, the access unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the access unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a desktop, the access unit can also provide a display method that includes detailed information. This makes it possible to provide a display method optimized for the device by taking the user's device information into consideration. Some or all of the above-described processing in the access unit may be performed using AI, for example, or may be performed without using AI. For example, the access unit can input the user's device information into the generation AI and cause the generation AI to select the optimal display method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and access unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the content of the operator's conversation using the camera 42 and microphone 38B of the smart device 14 and analyzes it in real time using the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information to identify effective talk scripts and success stories. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates the latest talk scripts and success stories based on the analysis results. The access unit, realized, for example, by the control unit 46A of the smart device 14, integrates the generated information into the customer center system so that operators and customers can access it as needed. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and access unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the content of the operator's conversation using the camera 42 and microphone 238 of the smart glasses 214 and analyzes it in real time using the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information to identify effective talk scripts and success stories. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates the latest talk scripts and success stories based on the analysis results. The access unit, realized, for example, by the control unit 46A of the smart glasses 214, integrates the generated information into the customer center system so that operators and customers can access it as needed. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and access unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects the content of the operator's conversation using the camera 42 and microphone 238 of the headset terminal 314 and analyzes it in real time by the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information to identify effective talk scripts and success stories. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates the latest talk scripts and success stories based on the analysis results. The access unit, realized, for example, by the control unit 46A of the headset terminal 314, integrates the generated information into the customer center system so that operators and customers can access it as needed. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and access unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the content of the operator's conversation using the camera 42 and microphone 238 of the robot 414 and analyzes it in real time by the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information to identify effective talk scripts and success stories. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates the latest talk scripts and success stories based on the analysis results. The access unit, realized, for example, by the control unit 46A of the robot 414, integrates the generated information into the customer center system so that operators and customers can access it as needed.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The collection unit can analyze the tone of an agent's voice and speaking patterns to predict customer reactions. For example, if an agent speaks in a calm tone, it can predict that the customer will be likely to relax, and collect a talk script appropriate to that situation. Also, if an agent speaks quickly, it can predict that the customer is in a hurry, and collect an effective talk script in a short amount of time. Furthermore, if an agent speaks in an emotional tone, it can predict that the customer will react emotionally, and collect success stories appropriate to that situation. This makes it possible to collect more appropriate information based on the agent's speaking patterns.

[0079] The analysis unit can predict customer needs and customize analysis results based on the customer's purchase history and past inquiries. For example, if a customer has shown interest in a particular service in the past, it can prioritize analyzing talk scripts related to that service. Also, if a customer has had a particular problem in the past, it can prioritize analyzing success stories that include solutions to that problem. Furthermore, it can analyze information about new services and products that the customer may be interested in based on the customer's purchase history. This makes it possible to provide more effective information by providing analysis results that meet the customer's needs.

[0080] The generation unit can generate optimal talk scripts based on the operator's past performance data. For example, it can analyze talk scripts that the operator has used successfully in the past and generate new talk scripts that incorporate those patterns. It can also analyze talk scripts that the operator has used unsuccessfully in the past and generate talk scripts that incorporate improvements to avoid those mistakes. Furthermore, it can determine the most effective timing for generating talk scripts based on the operator's past performance data. This improves the accuracy of generation by generating optimal talk scripts based on the operator's past performance data.

[0081] The collection unit can dynamically adjust the conversation scripts and success stories it collects based on real-time customer behavior data. For example, if a customer is viewing a specific page on a website, it can collect conversation scripts related to that page. Also, if a customer takes a specific action (for example, submitting an inquiry form), it can collect success stories related to that action. Furthermore, if a customer is active during a specific time period, it can collect conversation scripts that are effective during that time period. This makes it possible to collect more relevant information based on the customer's real-time behavior data.

[0082] The analysis unit can estimate the user's emotions and adjust the visual presentation of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide simple, visually easy-to-understand analysis results. If the user is relaxed, the analysis unit can provide analysis results including detailed graphs and charts. Furthermore, if the user is excited, the analysis unit can provide analysis results with visually stimulating effects. In this way, by adjusting the visual presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided.

[0083] The generator can adjust the complexity of the talk script based on the skill level of the operator when generating it. For example, it can generate a simple and basic talk script for a novice operator, and a more complex and advanced talk script for an experienced operator. Furthermore, it can adjust the level of detail of the information included in the talk script depending on the operator's skill level. This makes it possible to maximize the performance of the operator by generating a talk script that suits the operator's skill level.

[0084] The collection unit can estimate the user's emotions and select the type of information to collect based on the estimated user emotions. For example, if the user is feeling anxious, it can prioritize collecting success stories that give a sense of security. Also, if the user is excited, it can prioritize collecting talk scripts to calm the user. Furthermore, if the user is relaxed, it can prioritize collecting success stories that include detailed information. This allows for more effective information collection by selecting the type of information to collect according to the user's emotions.

[0085] The analysis unit can incorporate operator feedback during analysis to improve the analysis results. For example, if an operator provides positive feedback on a particular talk script, it will prioritize analyzing that talk script. Also, if an operator provides negative feedback on a particular success story, it can reanalyze that success story to identify areas for improvement. Furthermore, it can adjust the analysis algorithm based on operator feedback to provide more accurate analysis results. In this way, incorporating operator feedback improves the accuracy and reliability of the analysis results.

[0086] The generation unit can estimate the user's emotions and adjust the language style of the generated talk script based on the estimated user emotions. For example, if the user is relaxed, a casual and friendly language style can be used. If the user is nervous, a formal and reassuring language style can be used. Furthermore, if the user is excited, an energetic and positive language style can be used. In this way, by adjusting the language style according to the user's emotions, a more effective talk script can be generated.

[0087] When accessing, the access unit can prioritize displaying the most appropriate information based on the user's past access history. For example, if the user has frequently referred to a specific talk script in the past, it can prioritize displaying that talk script. Also, if the user has referred to a specific success story in the past, it can prioritize displaying that success story. Furthermore, it can also suggest new related information based on the user's past access history. This improves the user experience by providing the most appropriate information based on the user's past access history.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The collection unit automatically collects the talk scripts used by customer center operators and successful examples of customer interactions. For example, it collects information such as explanations given by operators to customers and specific interactions when customers decide to transfer. The collection unit also uses a generation AI to analyze the content of operators' conversations in real time, identifying and collecting effective talk scripts and success examples. Step 2: The analysis unit analyzes the information collected by the collection unit and identifies effective talk scripts and success cases. For example, it analyzes the collected talk scripts and success cases and identifies which talk scripts were effective and which cases were successful. The analysis unit also uses a generative AI to analyze the collected information and identify effective talk scripts and success cases. Step 3: The generation unit generates the latest talk scripts and success stories based on the analysis results identified by the analysis unit. For example, based on the analysis results, the generation unit generates the latest talk scripts to be used by customer center operators and success stories for customers to refer to. The generation unit also uses generation AI to generate the latest talk scripts and success stories based on the analysis results. Step 4: The access unit integrates the information generated by the generation unit into the customer center system, making it accessible to operators and customers as needed. For example, the access unit integrates the generated talk scripts and success stories into the customer center system, making them accessible to operators as needed. The access unit also makes the generated information available to customers.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] [Explanation of symbols]

[0162] 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 automatically collects talk scripts used by customer center operators or successful cases in interactions with customers; an analysis unit that analyzes the information collected by the collection unit and identifies effective talk scripts or success stories; a generation unit that generates an updated talk script or a success story based on the analysis result identified by the analysis unit; an access unit that integrates the information generated by the generation unit into a customer center system and enables operators and customers to access the information as needed; A system characterized by:

2. The collecting unit Estimate the user's emotions and adjust the timing of collecting talk scripts or success stories based on the estimated user emotions.

2. The system of claim 1.

3. The collecting unit When collecting data, analyze the operator's past performance data and select the appropriate collection method.

2. The system of claim 1.

4. The collecting unit Filtering based on customer attributes when collecting talk scripts or success stories 2. The system of claim 1.

5. The collecting unit Estimate the user's emotions and prioritize the talk scripts or success stories to be collected based on the estimated user emotions.

2. The system of claim 1.

6. The collecting unit Prioritize collection of relevant information based on the operator's geographic location at the time of collection 2. The system of claim 1.

7. The collecting unit Analyze operators' social media activity at the time of collection and collect relevant talk scripts or success stories 2. The system of claim 1.

8. The analysis unit Inferring user emotions and adjusting the presentation of analysis based on the estimated user emotions 2. The system of claim 1.

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