System

Abstract

An object of a system according to an embodiment is to improve motivation by effectively transmitting a praise comment of a customer to a crew.SOLUTION: A system according to an embodiment includes a comment collection unit, a comment analysis unit, a comment generation unit, and a comment transmission unit. A comment collection part collects praise comments of customers. The comment analysis unit analyzes the praise comment collected by the comment collection unit. A comment generation part generates an appropriate praise comment on the basis of the content analyzed by the comment analysis part. The comment transmission unit transmits the praise comment generated by the comment generation unit to the crew.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has room for improvement in terms of effectively conveying customer praise comments to crew members and improving their motivation.

[0005] The system according to the embodiment aims to effectively convey customers' praise comments to crew members and improve their motivation. [Means for solving the problem]

[0006] The system according to the embodiment includes a comment collection unit, a comment analysis unit, a comment generation unit, and a comment transmission unit. The comment collection unit collects praise comments from customers. The comment analysis unit analyzes the praise comments collected by the comment collection unit. The comment generation unit generates appropriate praise comments based on the content analyzed by the comment analysis unit. The comment transmission unit transmits the praise comments generated by the comment generation unit to the crew. [Effects of the Invention]

[0007] The system according to the embodiment can effectively convey customers' praise comments to crew members, thereby improving their motivation. [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) The praise comment generation system according to the embodiment of the present invention is a system in which a generation AI automatically generates praise comments to improve the motivation of crew members. As a result, the praise comment generation system can improve the motivation of crew members and provide better customer service.

[0029] A praise comment generation system according to an embodiment includes a comment collection unit, a comment analysis unit, a comment generation unit, and a comment transmission unit. The comment collection unit collects customer praise comments. For example, the praise comments may be collected from surveys, feedback forms, social media posts, etc. The comment collection unit can also collect praise comments through in-store digital signage or interactive displays. For example, a praise comment input screen may be displayed on the digital signage, allowing customers to directly input their comments. The comment analysis unit analyzes the praise comments collected by the comment collection unit. For example, the comment analysis unit may use natural language processing technology to deeply understand the context of the praise comments and extract information for generating appropriate praise comments. The comment analysis unit may also use an emotion estimation function to analyze the emotions of customers when they input their praise comments in real time, and prioritize the collection of comments with strong positive emotions. The comment generation unit generates appropriate praise comments based on the content analyzed by the comment analysis unit. For example, the generation AI may refer to a database of past praise comments and learn the most effective expressions to generate praise comments. The generation AI can also generate personalized praise comments by taking into account customer profile information (such as age, gender, and purchase history). The comment transmission unit transmits the praise comments generated by the comment generation unit to the crew. For example, the generated praise comments can be immediately transmitted by sending a push notification to the crew's individual smartphone app. The comment transmission unit can also share the praise comments with all crew members through digital signage or interactive displays in the store. This allows the praise comment generation system according to the embodiment to improve crew motivation and provide better customer service. For example, quickly transmitting the praise comments generated by the generation AI to the crew members can motivate them and improve the service quality throughout the store.

[0030] The comment collection unit can automatically collect comments from telephone or face-to-face conversations using voice recognition technology. For example, when a customer makes an inquiry to a store by telephone, the comment collection unit uses voice recognition technology to analyze the content of the conversation and automatically extract praise comments. For example, a comment such as "The service was very kind" is converted from voice data into text data and collected. The comment collection unit also uses voice recognition technology to collect customers' praise comments in real time during face-to-face customer service in the store. For example, when a customer says "The service was great," the text is automatically converted into text and stored in a database. The comment collection unit also uses voice recognition technology to constantly monitor conversations in the store and automatically extract parts that include praise comments. For example, the comment "I'd like to come again" is detected and collected. In this way, by using voice recognition technology, praise comments can be automatically collected from telephone or face-to-face conversations.

[0031] The comment collection unit can analyze customer purchase history or behavioral data and preferentially collect comments from specific customers. For example, the comment collection unit analyzes customer purchase history and preferentially collects praise comments from customers who make frequent purchases or who have purchased high-priced items. For example, it focuses on collecting comments from VIP customers. The comment collection unit also analyzes customer behavioral data and preferentially collects praise comments from customers who spend a long time in the store or have a high repeat rate. For example, it places emphasis on comments from regular customers. The comment collection unit also preferentially collects praise comments from customers who participated in a specific campaign or event based on the purchase history or behavioral data. For example, it collects comments from customers who visited the store during a specific promotional period. In this way, by analyzing customer purchase history and behavioral data, it is possible to preferentially collect praise comments from specific customers.

[0032] The comment collection unit can collect praise comments through digital signage or interactive displays in the store, allowing customers to input them directly. For example, the comment collection unit displays a praise comment input screen on digital signage installed in the store, allowing customers to input comments directly. For example, it provides an interface that allows easy input using a touch panel. The comment collection unit also provides visual feedback when customers input praise comments using an interactive display. For example, it displays animations or effects according to the input content. The comment collection unit also provides a voice input function when customers input praise comments through the digital signage or interactive display. For example, it allows comments to be input by voice using a microphone. This allows customers to input praise comments directly using the digital signage or interactive display.

[0033] The comment collection unit can link with customers' social media accounts and automatically collect positive posts on social media. For example, the comment collection unit links with customers' social media accounts and automatically collects positive posts that include specific hashtags or keywords. For example, it collects posts that include the hashtag "#GreatService." The comment collection unit also analyzes customers' posts on social media and automatically collects posts that have a strong positive sentiment. For example, it detects and collects posts that include "It was the best experience!" The comment collection unit also automatically collects positive posts by tagging the store's official account when customers post on social media. For example, it collects posts that include "@StoreName." In this way, by linking with social media accounts, positive posts on social media can be automatically collected.

[0034] The comment analysis unit uses natural language processing technology to deeply understand the context of the praise comment and generate a more appropriate praise comment. For example, when the generation AI analyzes a praise comment, the comment analysis unit uses natural language processing technology to deeply understand the context of the comment and generate an appropriate praise comment. For example, in response to a comment such as "The customer service was excellent," the comment analysis unit generates "I'm glad that the customer was pleased." The comment analysis unit also uses natural language processing technology to analyze the context of the praise comment and understand the intention and emotion of the comment. For example, in response to a comment such as "I'd like to come again," the comment analysis unit generates "We look forward to your next visit." The comment analysis unit also uses natural language processing technology to understand the background information of the comment and generate a more appropriate praise comment. For example, in response to a comment such as "The staff's response was excellent," the comment analysis unit generates "We would like to express our sincere gratitude to all the staff." In this way, by using natural language processing technology, the context of the praise comment can be deeply understood and a more appropriate praise comment can be generated.

[0035] The comment generation unit can accommodate different languages ​​and cultures, making it usable in international stores. For example, when the generation AI generates praise comments, the comment generation unit accommodates different languages ​​and cultures. For example, it can translate Japanese comments into English or Chinese, making them usable in international stores. The comment generation unit also takes cultural backgrounds and customs into account to generate praise comments that are appropriate for different cultures. For example, an American store might generate "Thank you for your excellent service," while a Chinese store might generate "Impressive?" The comment generation unit also references a database that accommodates different languages ​​and cultures when the generation AI generates praise comments. For example, it generates praise comments that are tailored to the language and culture of each country. This allows the comments to be adapted to different languages ​​and cultures, making them usable in international stores.

[0036] The comment generation unit can generate personalized comments by taking into account customer profile information. For example, when the generation AI generates a praise comment, the comment generation unit generates a personalized comment by taking into account customer profile information. For example, for a young customer, it may generate, "I felt your youthful energy." The comment generation unit also generates personalized praise comments based on the customer's purchase history. For example, for a customer who visits the store frequently, it may generate, "Thank you for always patronizing us." The comment generation unit also generates appropriate praise comments by taking into account the customer's age and gender. For example, for a female customer, it may generate, "Thank you for your lovely smile." In this way, personalized comments can be generated by taking into account customer profile information.

[0037] The comment transmission unit can instantly transmit the generated praise comments by sending them to the crew's individual smartphone app via push notification. For example, the comment transmission unit builds a system that sends praise comments generated by the generation AI to the crew's individual smartphone app via push notification. For example, the crew can instantly receive the praise comments while they are on duty. The comment transmission unit also transmits the generated praise comments to the crew in real time using the smartphone app. For example, the crew can check the praise comments even during their break. The comment transmission unit also uses a push notification function to instantly transmit the generated praise comments to the crew to improve their motivation. For example, the crew can receive a notification every time they receive a praise comment. This allows the generated praise comments to be instantly transmitted by sending them to the crew's smartphone app via push notification.

[0038] The comment transmission unit can transmit the praise comment at the optimal timing, taking into account the crew's work shift and work situation. The comment transmission unit, for example, builds a system that transmits the praise comment at the optimal timing, taking into account the crew's work shift. For example, the comment transmission unit is configured to receive the praise comment immediately after the crew starts work or during their break. The comment transmission unit also monitors the crew's work situation in real time and transmits the praise comment at the optimal timing. For example, the praise comment is sent so as to avoid times when the crew is busy. The comment transmission unit also develops a system that adjusts the timing of transmitting the praise comment based on the work shift and work situation. For example, the praise comment is received after the crew has finished their work. In this way, the praise comment can be transmitted at the optimal timing, taking into account the crew's work shift and work situation.

[0039] The comment transmission unit can transmit the praise comments through digital signage or interactive displays in the store and share them with all crew members. The comment transmission unit, for example, uses digital signage in the store to build a system that shares the generated praise comments with all crew members. For example, the praise comments are displayed on a display installed at the store entrance or in a break room. The comment transmission unit also uses an interactive display to share the generated praise comments with all crew members in real time. For example, the crew members can touch the display to check the praise comments. The comment transmission unit also shares the generated praise comments with all crew members through digital signage or interactive displays to improve motivation. For example, the crew members can be encouraged by seeing the praise comments. In this way, the praise comments can be transmitted through digital signage or interactive displays in the store and shared with all crew members.

[0040] The comment transmission unit can link with the crew's social media account and share the praise comments as positive feedback on social media. The comment transmission unit, for example, links with the crew's social media account and builds a system for sharing the generated praise comments on social media. For example, it posts the praise comments to the crew's personal account. The comment transmission unit also shares the generated praise comments as positive feedback on social media. For example, it posts a comment such as "That was great customer service" to the crew's social media account. The comment transmission unit also links with the crew's social media account and develops a system for sharing the generated praise comments in real time. For example, it can automatically post to social media every time the crew receives a praise comment. This allows the praise comments to be linked with the crew's social media account and shared as positive feedback on social media.

[0041] The comment generation unit collects crew work performance data after the generation AI transmits praise comments, and can quantitatively evaluate the effect of improving motivation. The comment generation unit, for example, collects crew work performance data after the generation AI transmits praise comments, and builds a system that quantitatively evaluates the effect of improving motivation. For example, it measures the crew's customer service time and customer satisfaction. The comment generation unit also collects crew work performance data and analyzes changes before and after the transmission of praise comments. For example, it evaluates whether the work efficiency of the crew who received the praise comments has improved. The comment generation unit also collects crew work performance data in real time after the generation AI transmits praise comments, and quantitatively evaluates the effect of improving motivation. For example, it analyzes whether the crew's work performance has improved. In this way, it is possible to collect crew work performance data after the generation AI transmits praise comments, and quantitatively evaluate the effect of improving motivation.

[0042] The comment generation unit can present specific areas for improvement and next goals in addition to praise comments to improve crew motivation. For example, the comment generation unit builds a system in which the generation AI presents specific areas for improvement and next goals in addition to praise comments to improve crew motivation. For example, the comment generation unit may comment, "That was great customer service. Next time, let's aim to respond even faster." The comment generation unit also improves crew motivation by including specific areas for improvement in praise comments. For example, the comment generation unit may comment, "The customer was pleased. Next time, let's deepen our product knowledge." The comment generation unit also presents next goals in praise comments to improve crew motivation. For example, the comment generation unit may comment, "That was great service. Next time, let's demonstrate leadership and lead the team." This makes it possible to present specific areas for improvement and next goals in addition to praise comments to improve crew motivation.

[0043] The comment generation unit can introduce incentives and reward systems in addition to praise comments to improve crew motivation. For example, the comment generation unit builds a system in which the generation AI introduces incentives and reward systems in addition to praise comments to improve crew motivation. For example, points are awarded to crew members who receive praise comments, and rewards are provided when a certain number of points are accumulated. The comment generation unit also improves crew motivation by combining incentives with praise comments. For example, the comment generation unit may comment, "That was great customer service. We'll give you a special bonus on your next shift." The comment generation unit also introduces reward systems into praise comments to improve crew motivation. For example, the comment generation unit may comment, "The customer was pleased. We'll give you a special allowance with your next paycheck." This makes it possible to introduce incentives and reward systems in addition to praise comments to improve crew motivation.

[0044] The comment generation unit can hold regular feedback sessions to improve crew motivation and hold discussions based on the data collected by the generation AI. The comment generation unit, for example, builds a system to hold regular feedback sessions to improve crew motivation and hold discussions based on the data collected by the generation AI. For example, praise comments and areas for improvement are shared at a monthly meeting. The comment generation unit also holds regular feedback sessions regarding crew performance and motivation based on the data collected by the generation AI. For example, the comment generation unit evaluates the work performance of crew members after they receive praise comments. The comment generation unit also discusses measures to improve crew motivation based on the data collected by the generation AI through regular feedback sessions. For example, the effectiveness of praise comments and areas for improvement are discussed. This makes it possible to hold regular feedback sessions to improve crew motivation and hold discussions based on the data collected by the generation AI.

[0045] The comment generation unit can periodically conduct customer satisfaction surveys to evaluate the effectiveness of the praise comments made by the generation AI and quantitatively evaluate the quality of customer service. For example, the comment generation unit periodically conducts customer satisfaction surveys to evaluate the effectiveness of the praise comments made by the generation AI and builds a system to quantitatively evaluate the quality of customer service. For example, a questionnaire survey is conducted once a month to measure customer satisfaction. The comment generation unit also evaluates the effectiveness of the praise comments made by the generation AI through the customer satisfaction survey. For example, it analyzes how the service of the crew member who received the praise comments affected the customer. The comment generation unit also conducts regular customer satisfaction surveys to quantitatively evaluate the effectiveness of the praise comments made by the generation AI. For example, it evaluates the quality of customer service based on customer feedback and identifies areas for improvement. As a result, the customer satisfaction survey can be periodically conducted to evaluate the effectiveness of the praise comments made by the generation AI and quantitatively evaluate the quality of customer service.

[0046] The comment generation unit enables the generation AI to automatically propose and implement crew training programs in order to provide better customer service. The comment generation unit, for example, builds a system in which the generation AI automatically proposes and implements crew training programs. For example, it proposes individually appropriate training programs based on crew work performance data. The comment generation unit also enables the generation AI to automatically propose and implement crew training programs. For example, it proposes online courses and workshops to improve customer service skills. The comment generation unit also develops a system in which the generation AI automatically proposes and implements crew training programs. For example, it proposes and implements training programs to compensate for crew weaknesses. This allows the generation AI to automatically propose and implement crew training programs in order to provide better customer service.

[0047] The comment generation unit enables the generation AI to automatically update the customer service manual based on customer feedback in order to provide better customer service. The comment generation unit, for example, builds a system in which the generation AI automatically updates the customer service manual based on customer feedback. For example, it generates an updated customer service manual that reflects customer opinions and requests. The comment generation unit also enables the generation AI to automatically update the customer service manual based on customer feedback. For example, it generates a customer service manual that reflects positive feedback from customers. The comment generation unit also develops a system in which the generation AI automatically updates the customer service manual based on customer feedback. For example, it generates a customer service manual that meets customer requests and provides it to the crew. This allows the generation AI to automatically update the customer service manual based on customer feedback in order to provide better customer service.

[0048] The comment generation unit allows the generation AI to analyze crew performance data and propose optimal shift schedules in order to provide better customer service. The comment generation unit, for example, builds a system in which the generation AI analyzes crew performance data and proposes optimal shift schedules. For example, it proposes optimal shifts based on the crew's work efficiency and fatigue level. The comment generation unit also proposes optimal shift schedules based on the crew's performance data. For example, it proposes shifts that take into account the crew's strengths and weaknesses. The comment generation unit also develops a system in which the generation AI analyzes crew performance data and proposes optimal shift schedules. For example, it proposes shifts that optimize the crew's working hours and break times. This allows the generation AI to analyze crew performance data and propose optimal shift schedules in order to provide better customer service.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The comment collection unit can analyze customer purchase history and behavioral data and prioritize the collection of comments from specific customers. For example, it can focus on collecting praise comments from customers who visit the store frequently or who have purchased expensive items. The comment collection unit can also prioritize the collection of praise comments from customers who spend a long time in the store or who have a high repeat rate, based on customer behavioral data. It can also prioritize the collection of praise comments from customers who have participated in specific campaigns or events. In this way, by analyzing customer purchase history and behavioral data, it is possible to prioritize the collection of praise comments from specific customers.

[0051] The comment collection unit can automatically collect comments from telephone or face-to-face conversations using voice recognition technology. For example, when a customer makes an inquiry to a store by telephone, the voice recognition technology is used to analyze the content of the conversation and automatically extract praise comments. It is also possible to use voice recognition technology to collect praise comments from customers in real time during face-to-face customer service in the store. Furthermore, it is also possible to constantly monitor conversations in the store and automatically extract parts that include praise comments. In this way, by using voice recognition technology, praise comments can be automatically collected from telephone or face-to-face conversations.

[0052] The comment collection unit can link with customers' social media accounts and automatically collect positive posts on social media. For example, it can automatically collect positive posts that include specific hashtags or keywords. It can also analyze customers' posts on social media and automatically collect posts with strong positive sentiments. Furthermore, when customers post on social media, they can tag the store's official account, which can automatically collect positive posts. In this way, by linking with social media accounts, it is possible to automatically collect positive posts on social media.

[0053] The comment collection unit can collect praise comments through in-store digital signage or interactive displays, allowing customers to input them directly. For example, a praise comment input screen can be displayed on digital signage installed in the store, providing an interface that allows easy input using a touch panel. Visual feedback can also be provided using an interactive display. Furthermore, a voice input function can be provided, allowing comments to be input by voice using a microphone. This allows customers to directly input praise comments using digital signage or interactive displays.

[0054] The comment analysis unit uses natural language processing technology to deeply understand the context of the praise comment and generate a more appropriate praise comment. For example, in response to a comment such as "Your customer service was excellent," it can generate "I'm glad the customer was pleased." It is also possible to analyze the context of the praise comment and understand the intention and emotion behind the comment. Furthermore, it is possible to understand the background information of the comment and generate a more appropriate praise comment. In this way, by using natural language processing technology, it is possible to deeply understand the context of the praise comment and generate a more appropriate praise comment.

[0055] The comment generation unit can accommodate different languages ​​and cultures, making it usable in international stores. For example, Japanese comments can be translated into English or Chinese, making them usable in international stores. It is also possible to take cultural backgrounds and customs into consideration in order to generate praise comments that are compatible with different cultures. Furthermore, it is also possible to refer to a database for accommodating different languages ​​and cultures and generate praise comments that are tailored to the language and culture of each country. This makes it possible to accommodate different languages ​​and cultures, making it usable in international stores.

[0056] The comment generation unit can generate personalized comments by taking into account customer profile information. For example, for a young customer, it can generate a comment such as "I felt your youthful energy." It is also possible to generate personalized praise comments based on the customer's purchase history. Furthermore, it is also possible to generate appropriate praise comments by taking into account the customer's age and gender. In this way, personalized comments can be generated by taking into account the customer's profile information.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The comment collection department collects customer praise comments. For example, this can be done through surveys, feedback forms, social media posts, in-store digital signage, or interactive displays. A praise comment input screen is displayed on the digital signage, allowing customers to enter their comments directly. Step 2: The comment analysis unit analyzes the praise comments collected by the comment collection unit. For example, it uses natural language processing technology to deeply understand the context of the praise comments and extract information to generate appropriate praise comments. It also uses an emotion estimation function to analyze the emotions customers feel when they enter praise comments in real time and prioritize the collection of comments with strong positive emotions. Step 3: The comment generation unit generates appropriate praise comments based on the content analyzed by the comment analysis unit. For example, the generation AI references a database of past praise comments and learns the most effective expressions to generate praise comments. It also takes into account customer profile information (age, gender, purchasing history, etc.) to generate personalized praise comments. Step 4: The comment transmission unit transmits the praise comments generated by the comment generation unit to the crew. For example, the generated praise comments can be sent to each crew member's individual smartphone app via push notification for immediate transmission. The praise comments can also be shared with all crew members via in-store digital signage or interactive displays.

[0059] (Example 2) The praise comment generation system according to the embodiment of the present invention is a system in which a generation AI automatically generates praise comments to improve the motivation of crew members. As a result, the praise comment generation system can improve the motivation of crew members and provide better customer service.

[0060] A praise comment generation system according to an embodiment includes a comment collection unit, a comment analysis unit, a comment generation unit, and a comment transmission unit. The comment collection unit collects customer praise comments. For example, the praise comments may be collected from surveys, feedback forms, social media posts, etc. The comment collection unit can also collect praise comments through in-store digital signage or interactive displays. For example, a praise comment input screen may be displayed on the digital signage, allowing customers to directly input their comments. The comment analysis unit analyzes the praise comments collected by the comment collection unit. For example, the comment analysis unit may use natural language processing technology to deeply understand the context of the praise comments and extract information for generating appropriate praise comments. The comment analysis unit may also use an emotion estimation function to analyze the emotions of customers when they input their praise comments in real time, and prioritize the collection of comments with strong positive emotions. The comment generation unit generates appropriate praise comments based on the content analyzed by the comment analysis unit. For example, the generation AI may refer to a database of past praise comments and learn the most effective expressions to generate praise comments. The generation AI can also generate personalized praise comments by taking into account customer profile information (such as age, gender, and purchase history). The comment transmission unit transmits the praise comments generated by the comment generation unit to the crew. For example, the generated praise comments can be immediately transmitted by sending a push notification to the crew's individual smartphone app. The comment transmission unit can also share the praise comments with all crew members through digital signage or interactive displays in the store. This allows the praise comment generation system according to the embodiment to improve crew motivation and provide better customer service. For example, quickly transmitting the praise comments generated by the generation AI to the crew members can motivate them and improve the service quality throughout the store.

[0061] The comment collection unit can automatically collect comments from telephone or face-to-face conversations using voice recognition technology. For example, when a customer makes an inquiry to a store by telephone, the comment collection unit uses voice recognition technology to analyze the content of the conversation and automatically extract praise comments. For example, a comment such as "The service was very kind" is converted from voice data into text data and collected. The comment collection unit also uses voice recognition technology to collect customers' praise comments in real time during face-to-face customer service in the store. For example, when a customer says "The service was great," the text is automatically converted into text and stored in a database. The comment collection unit also uses voice recognition technology to constantly monitor conversations in the store and automatically extract parts that include praise comments. For example, the comment "I'd like to come again" is detected and collected. In this way, by using voice recognition technology, praise comments can be automatically collected from telephone or face-to-face conversations.

[0062] The comment collection unit can analyze customer purchase history or behavioral data and preferentially collect comments from specific customers. For example, the comment collection unit analyzes customer purchase history and preferentially collects praise comments from customers who make frequent purchases or who have purchased high-priced items. For example, it focuses on collecting comments from VIP customers. The comment collection unit also analyzes customer behavioral data and preferentially collects praise comments from customers who spend a long time in the store or have a high repeat rate. For example, it places emphasis on comments from regular customers. The comment collection unit also preferentially collects praise comments from customers who participated in a specific campaign or event based on the purchase history or behavioral data. For example, it collects comments from customers who visited the store during a specific promotional period. In this way, by analyzing customer purchase history and behavioral data, it is possible to preferentially collect praise comments from specific customers.

[0063] The comment collection unit uses the emotion estimation function to analyze the emotions of customers when they enter praise comments in real time, and can prioritize collecting comments with strong positive emotions. For example, when a customer enters a praise comment in a feedback form, the comment collection unit uses the emotion estimation function to analyze the emotion at the time of entry, and prioritize collecting comments with strong positive emotions. For example, it detects smiling facial expressions and a bright tone of voice. The comment collection unit also analyzes posts on social media and automatically collects posts with strong positive emotions using the emotion estimation function. For example, it detects and collects posts such as, "That was the best service!" The comment collection unit also uses the emotion estimation function to analyze the emotions of customers when they answer a questionnaire in real time, and prioritize collecting comments with strong positive emotions. For example, it prioritizes collecting responses with high satisfaction. In this way, by using the emotion estimation function, it is possible to prioritize collecting praise comments with strong positive emotions.

[0064] The comment collection unit can collect praise comments through digital signage or interactive displays in the store, allowing customers to input them directly. For example, the comment collection unit displays a praise comment input screen on digital signage installed in the store, allowing customers to input comments directly. For example, it provides an interface that allows easy input using a touch panel. The comment collection unit also provides visual feedback when customers input praise comments using an interactive display. For example, it displays animations or effects according to the input content. The comment collection unit also provides a voice input function when customers input praise comments through the digital signage or interactive display. For example, it allows comments to be input by voice using a microphone. This allows customers to input praise comments directly using the digital signage or interactive display.

[0065] The comment collection unit can link with customers' social media accounts and automatically collect positive posts on social media. For example, the comment collection unit links with customers' social media accounts and automatically collects positive posts that include specific hashtags or keywords. For example, it collects posts that include the hashtag "#GreatService." The comment collection unit also analyzes customers' posts on social media and automatically collects posts that have a strong positive sentiment. For example, it detects and collects posts that include "It was the best experience!" The comment collection unit also automatically collects positive posts by tagging the store's official account when customers post on social media. For example, it collects posts that include "@StoreName." In this way, by linking with social media accounts, positive posts on social media can be automatically collected.

[0066] The comment collection unit can use the emotion estimation function to analyze the emotion of a customer when inputting a praise comment and provide an interface for eliciting positive emotions. For example, when a customer inputs a praise comment, the comment collection unit uses the emotion estimation function to analyze the emotion at the time of input and provides an interface for eliciting positive emotions. For example, an encouraging message is displayed on the input screen. Furthermore, when a customer inputs a praise comment, the comment collection unit uses the emotion estimation function to analyze the emotion in real time and provides music or video for eliciting positive emotions. For example, relaxing music is played. Furthermore, when a customer inputs a praise comment, the comment collection unit uses the emotion estimation function to analyze the emotion at the time of input and displays an interactive animation for eliciting positive emotions. For example, a smiling character appears. In this way, an interface for eliciting positive emotions can be provided by using the emotion estimation function.

[0067] The comment analysis unit uses natural language processing technology to deeply understand the context of the praise comment and generate a more appropriate praise comment. For example, when the generation AI analyzes a praise comment, the comment analysis unit uses natural language processing technology to deeply understand the context of the comment and generate an appropriate praise comment. For example, in response to a comment such as "The customer service was excellent," the comment analysis unit generates "I'm glad that the customer was pleased." The comment analysis unit also uses natural language processing technology to analyze the context of the praise comment and understand the intention and emotion of the comment. For example, in response to a comment such as "I'd like to come again," the comment analysis unit generates "We look forward to your next visit." The comment analysis unit also uses natural language processing technology to understand the background information of the comment and generate a more appropriate praise comment. For example, in response to a comment such as "The staff's response was excellent," the comment analysis unit generates "We would like to express our sincere gratitude to all the staff." In this way, by using natural language processing technology, the context of the praise comment can be deeply understood and a more appropriate praise comment can be generated.

[0068] The comment generation unit can use the emotion estimation function to predict the emotional impact that the generated praise comment will have on the crew and select the optimal comment. The comment generation unit, for example, uses the emotion estimation function to predict the emotional impact that the generated praise comment will have on the crew and select the optimal comment. For example, it analyzes whether a comment such as "I'm so glad that the customer was pleased" will have a positive impact on the crew. The comment generation unit also predicts the crew's emotional reaction to the generated praise comment and selects the optimal comment. For example, it analyzes the emotional impact that a comment such as "We would like to express our sincere gratitude to all our staff" will have on the crew. The comment generation unit also uses the emotion estimation function to predict in real time the emotional impact that the generated praise comment will have on the crew and select the optimal comment. For example, it analyzes whether a comment such as "We look forward to seeing you again" will have a positive impact on the crew. In this way, by using the emotion estimation function, it is possible to predict the emotional impact that the generated praise comment will have on the crew and select the optimal comment.

[0069] The comment generation unit can accommodate different languages ​​and cultures, making it usable in international stores. For example, when the generation AI generates praise comments, the comment generation unit accommodates different languages ​​and cultures. For example, it can translate Japanese comments into English or Chinese, making them usable in international stores. The comment generation unit also takes cultural backgrounds and customs into account to generate praise comments that are appropriate for different cultures. For example, an American store might generate "Thank you for your excellent service," while a Chinese store might generate "Impressive?" The comment generation unit also references a database that accommodates different languages ​​and cultures when the generation AI generates praise comments. For example, it generates praise comments that are tailored to the language and culture of each country. This allows the comments to be adapted to different languages ​​and cultures, making them usable in international stores.

[0070] The comment generation unit can generate personalized comments by taking into account customer profile information. For example, when the generation AI generates a praise comment, the comment generation unit generates a personalized comment by taking into account customer profile information. For example, for a young customer, it may generate, "I felt your youthful energy." The comment generation unit also generates personalized praise comments based on the customer's purchase history. For example, for a customer who visits the store frequently, it may generate, "Thank you for always patronizing us." The comment generation unit also generates appropriate praise comments by taking into account the customer's age and gender. For example, for a female customer, it may generate, "Thank you for your lovely smile." In this way, personalized comments can be generated by taking into account customer profile information.

[0071] The comment generation unit uses the emotion estimation function to provide feedback on the emotional impact of the generated praise comments on the crew in real time, thereby continuously improving the quality of the comments. The comment generation unit, for example, uses the emotion estimation function to build a system that provides feedback on the emotional impact of the generated praise comments on the crew in real time. For example, the comment generation unit analyzes the crew's facial expressions and reactions to improve the quality of the comments. The comment generation unit also collects the crew's emotional reactions to the generated praise comments in real time and continuously improves the quality of the comments based on the data. For example, the comment generation unit prioritizes the generation of comments with a large number of positive reactions. The comment generation unit also uses the emotion estimation function to analyze the emotional impact of the generated praise comments on the crew in real time and improves the quality of the comments based on the feedback. For example, the comment generation unit generates comments with a small number of negative reactions. In this way, by using the emotion estimation function, the emotional impact of the generated praise comments on the crew can be provided as feedback in real time, thereby continuously improving the quality of the comments.

[0072] The comment transmission unit can instantly transmit the generated praise comments by sending them to the crew's individual smartphone app via push notification. For example, the comment transmission unit builds a system that sends praise comments generated by the generation AI to the crew's individual smartphone app via push notification. For example, the crew can instantly receive the praise comments while they are on duty. The comment transmission unit also transmits the generated praise comments to the crew in real time using the smartphone app. For example, the crew can check the praise comments even during their break. The comment transmission unit also uses a push notification function to instantly transmit the generated praise comments to the crew to improve their motivation. For example, the crew can receive a notification every time they receive a praise comment. This allows the generated praise comments to be instantly transmitted by sending them to the crew's smartphone app via push notification.

[0073] The comment transmission unit can transmit the praise comment at the optimal timing, taking into account the crew's work shift and work situation. The comment transmission unit, for example, builds a system that transmits the praise comment at the optimal timing, taking into account the crew's work shift. For example, the comment transmission unit is configured to receive the praise comment immediately after the crew starts work or during their break. The comment transmission unit also monitors the crew's work situation in real time and transmits the praise comment at the optimal timing. For example, the praise comment is sent so as to avoid times when the crew is busy. The comment transmission unit also develops a system that adjusts the timing of transmitting the praise comment based on the work shift and work situation. For example, the praise comment is received after the crew has finished their work. In this way, the praise comment can be transmitted at the optimal timing, taking into account the crew's work shift and work situation.

[0074] The comment transmission unit can use the emotion estimation function to analyze the emotional reaction of the crew when they receive a praise comment and select the optimal transmission method. The comment transmission unit, for example, uses the emotion estimation function to analyze the emotional reaction of the crew when they receive a praise comment and build a system that selects the optimal transmission method. For example, it analyzes the crew's facial expressions and voice and selects a method that will elicit a positive reaction. The comment transmission unit also collects the emotional reaction of the crew when they receive a praise comment in real time and selects the optimal transmission method based on that data. For example, it transmits the praise comment at a time when the crew will be most pleased. The comment transmission unit also uses the emotion estimation function to analyze the emotional reaction of the crew when they receive a praise comment and improve the transmission method. For example, it transmits the praise comment in a way that makes the crew feel positive emotions. In this way, by using the emotion estimation function, it is possible to analyze the emotional reaction of the crew when they receive a praise comment and select the optimal transmission method.

[0075] The comment transmission unit can transmit the praise comments through digital signage or interactive displays in the store and share them with all crew members. The comment transmission unit, for example, uses digital signage in the store to build a system that shares the generated praise comments with all crew members. For example, the praise comments are displayed on a display installed at the store entrance or in a break room. The comment transmission unit also uses an interactive display to share the generated praise comments with all crew members in real time. For example, the crew members can touch the display to check the praise comments. The comment transmission unit also shares the generated praise comments with all crew members through digital signage or interactive displays to improve motivation. For example, the crew members can be encouraged by seeing the praise comments. In this way, the praise comments can be transmitted through digital signage or interactive displays in the store and shared with all crew members.

[0076] The comment transmission unit can link with the crew's social media account and share the praise comments as positive feedback on social media. The comment transmission unit, for example, links with the crew's social media account and builds a system for sharing the generated praise comments on social media. For example, it posts the praise comments to the crew's personal account. The comment transmission unit also shares the generated praise comments as positive feedback on social media. For example, it posts a comment such as "That was great customer service" to the crew's social media account. The comment transmission unit also links with the crew's social media account and develops a system for sharing the generated praise comments in real time. For example, it can automatically post to social media every time the crew receives a praise comment. This allows the praise comments to be linked with the crew's social media account and shared as positive feedback on social media.

[0077] The comment generation unit collects crew work performance data after the generation AI transmits praise comments, and can quantitatively evaluate the effect of improving motivation. The comment generation unit, for example, collects crew work performance data after the generation AI transmits praise comments, and builds a system that quantitatively evaluates the effect of improving motivation. For example, it measures the crew's customer service time and customer satisfaction. The comment generation unit also collects crew work performance data and analyzes changes before and after the transmission of praise comments. For example, it evaluates whether the work efficiency of the crew who received the praise comments has improved. The comment generation unit also collects crew work performance data in real time after the generation AI transmits praise comments, and quantitatively evaluates the effect of improving motivation. For example, it analyzes whether the crew's work performance has improved. In this way, it is possible to collect crew work performance data after the generation AI transmits praise comments, and quantitatively evaluate the effect of improving motivation.

[0078] The comment generation unit can present specific areas for improvement and next goals in addition to praise comments to improve crew motivation. For example, the comment generation unit builds a system in which the generation AI presents specific areas for improvement and next goals in addition to praise comments to improve crew motivation. For example, the comment generation unit may comment, "That was great customer service. Next time, let's aim to respond even faster." The comment generation unit also improves crew motivation by including specific areas for improvement in praise comments. For example, the comment generation unit may comment, "The customer was pleased. Next time, let's deepen our product knowledge." The comment generation unit also presents next goals in praise comments to improve crew motivation. For example, the comment generation unit may comment, "That was great service. Next time, let's demonstrate leadership and lead the team." This makes it possible to present specific areas for improvement and next goals in addition to praise comments to improve crew motivation.

[0079] The comment generation unit can use the emotion estimation function to continuously monitor the emotional state of the crew and generate appropriate praise comments when their motivation drops. The comment generation unit, for example, uses the emotion estimation function to build a system that continuously monitors the emotional state of the crew and generates appropriate praise comments when their motivation drops. For example, the comment generation unit analyzes the crew's facial expressions and voice and generates encouraging comments when their motivation drops. The comment generation unit also monitors the crew's emotional state in real time and generates appropriate praise comments when their motivation drops. For example, when a crew member is tired, the comment generation unit may comment, "Thank you for your hard work. You did a great job." The comment generation unit also uses the emotion estimation function to continuously monitor the emotional state of the crew and generate appropriate praise comments when their motivation drops. For example, when a crew member is depressed, the comment generation unit may comment, "Your efforts are amazing." In this way, by using the emotion estimation function, the emotional state of the crew can be continuously monitored and appropriate praise comments can be generated when their motivation drops.

[0080] The comment generation unit can introduce incentives and reward systems in addition to praise comments to improve crew motivation. For example, the comment generation unit builds a system in which the generation AI introduces incentives and reward systems in addition to praise comments to improve crew motivation. For example, points are awarded to crew members who receive praise comments, and rewards are provided when a certain number of points are accumulated. The comment generation unit also improves crew motivation by combining incentives with praise comments. For example, the comment generation unit may comment, "That was great customer service. We'll give you a special bonus on your next shift." The comment generation unit also introduces reward systems into praise comments to improve crew motivation. For example, the comment generation unit may comment, "The customer was pleased. We'll give you a special allowance with your next paycheck." This makes it possible to introduce incentives and reward systems in addition to praise comments to improve crew motivation.

[0081] The comment generation unit can hold regular feedback sessions to improve crew motivation and hold discussions based on the data collected by the generation AI. The comment generation unit, for example, builds a system to hold regular feedback sessions to improve crew motivation and hold discussions based on the data collected by the generation AI. For example, praise comments and areas for improvement are shared at a monthly meeting. The comment generation unit also holds regular feedback sessions regarding crew performance and motivation based on the data collected by the generation AI. For example, the comment generation unit evaluates the work performance of crew members after they receive praise comments. The comment generation unit also discusses measures to improve crew motivation based on the data collected by the generation AI through regular feedback sessions. For example, the effectiveness of praise comments and areas for improvement are discussed. This makes it possible to hold regular feedback sessions to improve crew motivation and hold discussions based on the data collected by the generation AI.

[0082] The comment generation unit can use the emotion estimation function to analyze the emotional state of the crew in real time and provide a personalized approach to improving motivation. The comment generation unit, for example, uses the emotion estimation function to analyze the emotional state of the crew in real time and build a system that provides a personalized approach to improving motivation. For example, the comment generation unit analyzes the crew's facial expressions and voice and generates praise comments that are individually appropriate. The comment generation unit also analyzes the crew's emotional state in real time and provides a personalized approach to improving motivation. For example, when the crew is tired, the comment generation unit provides advice on how to refresh themselves. The comment generation unit also uses the emotion estimation function to develop a system that analyzes the crew's emotional state in real time and provides a personalized approach to improving motivation. For example, when the crew is feeling down, the comment generation unit sends an encouraging message. In this way, the emotion estimation function can be used to analyze the crew's emotional state in real time and provide a personalized approach to improving motivation.

[0083] The comment generation unit can periodically conduct customer satisfaction surveys to evaluate the effectiveness of the praise comments made by the generation AI and quantitatively evaluate the quality of customer service. For example, the comment generation unit periodically conducts customer satisfaction surveys to evaluate the effectiveness of the praise comments made by the generation AI and builds a system to quantitatively evaluate the quality of customer service. For example, a questionnaire survey is conducted once a month to measure customer satisfaction. The comment generation unit also evaluates the effectiveness of the praise comments made by the generation AI through the customer satisfaction survey. For example, it analyzes how the service of the crew member who received the praise comments affected the customer. The comment generation unit also conducts regular customer satisfaction surveys to quantitatively evaluate the effectiveness of the praise comments made by the generation AI. For example, it evaluates the quality of customer service based on customer feedback and identifies areas for improvement. As a result, the customer satisfaction survey can be periodically conducted to evaluate the effectiveness of the praise comments made by the generation AI and quantitatively evaluate the quality of customer service.

[0084] The comment generation unit enables the generation AI to automatically propose and implement crew training programs in order to provide better customer service. The comment generation unit, for example, builds a system in which the generation AI automatically proposes and implements crew training programs. For example, it proposes individually appropriate training programs based on crew work performance data. The comment generation unit also enables the generation AI to automatically propose and implement crew training programs. For example, it proposes online courses and workshops to improve customer service skills. The comment generation unit also develops a system in which the generation AI automatically proposes and implements crew training programs. For example, it proposes and implements training programs to compensate for crew weaknesses. This allows the generation AI to automatically propose and implement crew training programs in order to provide better customer service.

[0085] The comment generation unit enables the generation AI to automatically update the customer service manual based on customer feedback in order to provide better customer service. The comment generation unit, for example, builds a system in which the generation AI automatically updates the customer service manual based on customer feedback. For example, it generates an updated customer service manual that reflects customer opinions and requests. The comment generation unit also enables the generation AI to automatically update the customer service manual based on customer feedback. For example, it generates a customer service manual that reflects positive feedback from customers. The comment generation unit also develops a system in which the generation AI automatically updates the customer service manual based on customer feedback. For example, it generates a customer service manual that meets customer requests and provides it to the crew. This allows the generation AI to automatically update the customer service manual based on customer feedback in order to provide better customer service.

[0086] The comment generation unit allows the generation AI to analyze crew performance data and propose optimal shift schedules in order to provide better customer service. The comment generation unit, for example, builds a system in which the generation AI analyzes crew performance data and proposes optimal shift schedules. For example, it proposes optimal shifts based on the crew's work efficiency and fatigue level. The comment generation unit also proposes optimal shift schedules based on the crew's performance data. For example, it proposes shifts that take into account the crew's strengths and weaknesses. The comment generation unit also develops a system in which the generation AI analyzes crew performance data and proposes optimal shift schedules. For example, it proposes shifts that optimize the crew's working hours and break times. This allows the generation AI to analyze crew performance data and propose optimal shift schedules in order to provide better customer service.

[0087] The comment generation unit can use the emotion estimation function to analyze the emotional state of the crew in real time and provide an interface for eliciting positive emotions while serving customers. The comment generation unit, for example, uses the emotion estimation function to build a system that analyzes the emotional state of the crew in real time and provides an interface for eliciting positive emotions while serving customers. For example, the comment generation unit analyzes the crew's facial expressions and voice and provides an interface for eliciting positive emotions. The comment generation unit also analyzes the crew's emotional state in real time and provides an interface for eliciting positive emotions while serving customers. For example, the comment generation unit provides music or videos that allow the crew to relax. The comment generation unit also uses the emotion estimation function to develop a system that analyzes the crew's emotional state in real time and provides an interface for eliciting positive emotions while serving customers. For example, the comment generation unit displays an encouraging message to encourage the crew to serve customers with a smile. In this way, the emotion estimation function can be used to analyze the crew's emotional state in real time and provide an interface for eliciting positive emotions while serving customers.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The comment collection unit can analyze customer purchase history and behavioral data and prioritize the collection of comments from specific customers. For example, it can focus on collecting praise comments from customers who visit the store frequently or who have purchased expensive items. The comment collection unit can also prioritize the collection of praise comments from customers who spend a long time in the store or who have a high repeat rate, based on customer behavioral data. It can also prioritize the collection of praise comments from customers who have participated in specific campaigns or events. In this way, by analyzing customer purchase history and behavioral data, it is possible to prioritize the collection of praise comments from specific customers.

[0090] The comment collection unit can automatically collect comments from telephone or face-to-face conversations using voice recognition technology. For example, when a customer makes an inquiry to a store by telephone, the voice recognition technology is used to analyze the content of the conversation and automatically extract praise comments. It is also possible to use voice recognition technology to collect praise comments from customers in real time during face-to-face customer service in the store. Furthermore, it is also possible to constantly monitor conversations in the store and automatically extract parts that include praise comments. In this way, by using voice recognition technology, praise comments can be automatically collected from telephone or face-to-face conversations.

[0091] The comment collection unit can link with customers' social media accounts and automatically collect positive posts on social media. For example, it can automatically collect positive posts that include specific hashtags or keywords. It can also analyze customers' posts on social media and automatically collect posts with strong positive sentiments. Furthermore, when customers post on social media, they can tag the store's official account, which can automatically collect positive posts. In this way, by linking with social media accounts, it is possible to automatically collect positive posts on social media.

[0092] The comment collection unit uses the emotion estimation function to analyze the emotions of customers when they enter praise comments in real time, and can prioritize the collection of comments with strong positive emotions. For example, when entering praise comments into a feedback form, it can detect smiling expressions and bright tones of voice. It can also analyze posts on social media and automatically collect posts with strong positive emotions. Furthermore, when answering a survey, it can also prioritize the collection of responses with high satisfaction. In this way, the emotion estimation function can prioritize the collection of praise comments with strong positive emotions.

[0093] The comment collection unit can collect praise comments through in-store digital signage or interactive displays, allowing customers to input them directly. For example, a praise comment input screen can be displayed on digital signage installed in the store, providing an interface that allows easy input using a touch panel. Visual feedback can also be provided using an interactive display. Furthermore, a voice input function can be provided, allowing comments to be input by voice using a microphone. This allows customers to directly input praise comments using digital signage or interactive displays.

[0094] The comment analysis unit uses natural language processing technology to deeply understand the context of the praise comment and generate a more appropriate praise comment. For example, in response to a comment such as "Your customer service was excellent," it can generate "I'm glad the customer was pleased." It is also possible to analyze the context of the praise comment and understand the intention and emotion behind the comment. Furthermore, it is possible to understand the background information of the comment and generate a more appropriate praise comment. In this way, by using natural language processing technology, it is possible to deeply understand the context of the praise comment and generate a more appropriate praise comment.

[0095] The comment generation unit can use the emotion estimation function to predict the emotional impact that the generated praise comment will have on the crew and select the most appropriate comment. For example, it analyzes whether the comment "I'm glad the customer was pleased" will have a positive impact on the crew. It is also possible to predict the crew's emotional reaction to the generated praise comment and select the most appropriate comment. Furthermore, it is also possible to predict the emotional impact that the generated praise comment will have on the crew in real time and select the most appropriate comment. In this way, by using the emotion estimation function, it is possible to predict the emotional impact that the generated praise comment will have on the crew and select the most appropriate comment.

[0096] The comment generation unit can accommodate different languages ​​and cultures, making it usable in international stores. For example, Japanese comments can be translated into English or Chinese, making them usable in international stores. It is also possible to take cultural backgrounds and customs into consideration in order to generate praise comments that are compatible with different cultures. Furthermore, it is also possible to refer to a database for accommodating different languages ​​and cultures and generate praise comments that are tailored to the language and culture of each country. This makes it possible to accommodate different languages ​​and cultures, making it usable in international stores.

[0097] The comment generation unit can generate personalized comments by taking into account customer profile information. For example, for a young customer, it can generate a comment such as "I felt your youthful energy." It is also possible to generate personalized praise comments based on the customer's purchase history. Furthermore, it is also possible to generate appropriate praise comments by taking into account the customer's age and gender. In this way, personalized comments can be generated by taking into account the customer's profile information.

[0098] The comment generation unit can use the emotion estimation function to continuously monitor the crew's emotional state and generate appropriate praise comments when their motivation drops. For example, it can analyze the crew's facial expressions and voice and generate encouraging comments when their motivation drops. It is also possible to monitor the crew's emotional state in real time and generate appropriate praise comments when their motivation drops. Furthermore, it can comment, "Your efforts are great," when a crew member is feeling down. In this way, the emotion estimation function can continuously monitor the crew's emotional state and generate appropriate praise comments when their motivation drops.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The comment collection department collects customer praise comments. For example, this can be done through surveys, feedback forms, social media posts, in-store digital signage, or interactive displays. A praise comment input screen is displayed on the digital signage, allowing customers to enter their comments directly. Step 2: The comment analysis unit analyzes the praise comments collected by the comment collection unit. For example, it uses natural language processing technology to deeply understand the context of the praise comments and extract information to generate appropriate praise comments. It also uses an emotion estimation function to analyze the emotions customers feel when they enter praise comments in real time and prioritize the collection of comments with strong positive emotions. Step 3: The comment generation unit generates appropriate praise comments based on the content analyzed by the comment analysis unit. For example, the generation AI references a database of past praise comments and learns the most effective expressions to generate praise comments. It also takes into account customer profile information (age, gender, purchasing history, etc.) to generate personalized praise comments. Step 4: The comment transmission unit transmits the praise comments generated by the comment generation unit to the crew. For example, the generated praise comments can be sent to each crew member's individual smartphone app via push notification for immediate transmission. The praise comments can also be shared with all crew members via in-store digital signage or interactive displays.

[0101] 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.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] 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.

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

[0105] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] The data processing device 12 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The 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.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] In the robot 414, 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 robot 414 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.

[0146] 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.

[0147] 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.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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. [Explanation of symbols]

[0168] 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 comment collection department that collects customer praise comments; a comment analysis unit that analyzes the praise comments collected by the comment collection unit; a comment generation unit that generates an appropriate praise comment based on the content analyzed by the comment analysis unit; a comment transmission unit that transmits the praise comment generated by the comment generation unit to the crew. A system characterized by:

2. The comment collection unit Use voice recognition technology to automatically collect comments from phone or in-person conversations 2. The system of claim 1.

3. The comment collection unit Analyzing customer purchase history or behavioral data and preferentially collecting comments from specific customers 2. The system of claim 1.

4. The comment collection unit The emotions of customers when they enter the praise comments are analyzed in real time, and comments with strong positive emotions are collected preferentially.

2. The system of claim 1.

5. The comment collection unit Collect the praise comments through in-store digital signage or interactive displays, allowing customers to enter them directly.

2. The system of claim 1.

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