System

Abstract

An object of a system according to an embodiment is to simultaneously improve a knowledge level and a service skill level of a crew.SOLUTION: A system includes a customer situation input unit, a talk generation unit, and an information inquiry generation unit. The customer situation input unit inputs a situation of a customer. The talk generation unit generates a talk of the high acquisition crew on the basis of the customer situation input by the customer situation input unit. The information query generator generates a necessary information query together with the token generated by the token generator.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to simultaneously improve the knowledge and customer service skills of crew members.

[0005] The system according to the embodiment aims to simultaneously improve the knowledge level and customer service skill level of the crew. [Means for solving the problem]

[0006] The system according to the embodiment includes a customer status input unit, a talk generation unit, and an information query generation unit. The customer status input unit inputs the customer's status. The talk generation unit generates talk for a high-yield crew member based on the customer status input by the customer status input unit. The information query generation unit generates a necessary information query along with the talk generated by the talk generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can simultaneously improve the knowledge level and customer service skill level of the crew. [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 crew support system according to an embodiment of the present invention is a system that inputs the customer's situation into a generation AI and outputs a set of talks by highly successful crew members, as well as necessary information inquiries and talk methods. This allows the crew support system to simultaneously improve the crew's knowledge level and customer service skill level.

[0029] The crew assistance system according to the embodiment includes a customer status input unit, a conversation generation unit, and an information query generation unit. The customer status input unit inputs customer status information, such as the customer's age, gender, purchase history, and current needs. The conversation generation unit generates conversations for high-achieving crew members based on the customer status input by the customer status input unit. For example, the generation AI uses a text generation AI (e.g., LLM) to generate specific conversations for the customer, such as, "Customer, this product is very popular and is especially perfect for this season." The information query generation unit generates necessary information queries along with the conversation generated by the conversation generation unit. For example, the information query generation unit provides crew members with a way to access the information they need, such as, "Please refer to the link below to find out more information about the product you are interested in." This allows the crew assistance system to simultaneously improve the crew members' knowledge and customer service skills.

[0030] The conversation generation unit can generate conversations based on information included in the customer's situation, such as age, gender, purchase history, and current needs. For example, the conversation generation unit uses a generation AI to perform emotion analysis based on the customer's input information and calculate an emotion score. For example, it uses text analysis to infer emotions from the customer's language and expressions, and generates conversations based on those emotions. The conversation generation unit also analyzes voice data and facial expression data in addition to the customer's input information to infer emotions. For example, it reads emotions from voice tone and facial expressions and generates conversations based on those emotions. The conversation generation unit also uses a generation AI to analyze emotional trends based on the customer's past purchase history and behavioral data, and generates conversations based on those emotions. For example, it infers customer preferences and emotional patterns from past purchase history and generates conversations based on those emotions. This allows conversations to be generated based on the customer's detailed situation.

[0031] The information query generation unit can generate links to provide detailed information about products in which a customer is interested. For example, the information query generation unit connects with the customer's social media accounts and collects posts and reviews. For example, it analyzes posts on Twitter and Instagram to understand the customer's interests. The information query generation unit also collects social media activity data in addition to the customer's past purchase history, allowing the generation AI to grasp the detailed situation. For example, it analyzes Facebook "likes" and comments to infer the customer's preferences. The information query generation unit also collects ratings and comments on customer review sites, allowing the generation AI to grasp the detailed situation. For example, it analyzes reviews on Amazon and Yelp to understand the customer's purchasing trends and satisfaction level. This allows the system to quickly provide detailed information about products in which the customer is interested.

[0032] The conversation generation unit can generate conversation methods that greet customers with a smile and then explain the features of products. The conversation generation unit, for example, uses in-store sensors to collect customer movement patterns in real time and inputs this into the generation AI. For example, it tracks customer movements using beacons and cameras. The conversation generation unit also collects the history of products viewed by customers in real time and inputs this into the generation AI. For example, it uses smart shelves and electronic tags to record the products customers pick up. The conversation generation unit also uses in-store Wi-Fi to collect location information from customers' smartphones and inputs this into the generation AI. For example, it identifies customers' locations using Wi-Fi access points and analyzes their movement patterns. This allows staff to learn and practice effective customer service methods.

[0033] The information query generation unit can automatically summarize the results of generated information queries, allowing the crew to quickly understand them. For example, the information query generation unit will build a system in which the generation AI automatically summarizes the results of information queries, allowing the crew to quickly understand them. For example, it will display a short summary of long pieces of information. The information query generation unit will also analyze the results of information queries with the generation AI, extract and summarize the important points. For example, it will display a concise summary of the features and advantages of a product. The information query generation unit will also develop a system in which the generation AI automatically summarizes the results of information queries, allowing the crew to quickly understand them. For example, it will integrate and summarize information obtained from multiple sources. This will allow the crew to quickly understand the information and respond.

[0034] The talk generation unit can perform automatic translation and cultural adaptation to adapt to different languages ​​and cultures. For example, the talk generation unit builds a system in which a generation AI automatically translates the talk of high-achieving crew members and adapts it to different languages. For example, it translates from English to Japanese and provides talk that is tailored to the culture. The talk generation unit also translates the talk of high-achieving crew members to different cultures, with the generation AI taking cultural nuances into account. For example, it provides talk that reflects cultural expressions and customs. The talk generation unit also develops a system in which a generation AI automatically translates the talk of high-achieving crew members and adapts it to different languages ​​and cultures. For example, it provides talk that takes into account not only language differences but also cultural backgrounds. This makes it possible to generate talk that is adapted to different languages ​​and cultures.

[0035] The talk generation unit can use speech synthesis technology to suggest the tone and pace at which the crew will actually speak. For example, the talk generation unit builds a system in which a generation AI uses speech synthesis technology to suggest the tone and pace at which the crew will actually speak. For example, it provides the optimal tone and pace according to the content of the talk. In addition, when generating talk, the talk generation unit uses speech synthesis technology to simulate the tone and pace at which the crew will speak. For example, it suggests the tone and pace to maximize the effectiveness of the talk. In addition, the talk generation unit develops a system in which a generation AI uses speech synthesis technology to suggest the tone and pace at which the crew will actually speak. For example, it performs optimal speech synthesis according to the content of the talk. This makes it possible to optimize the tone and pace at which the crew actually speaks.

[0036] The information query generation unit can display the generated information query results in the crew's field of vision using AR technology. The information query generation unit, for example, builds a system that displays the information query results in the crew's field of vision using AR technology. For example, smart glasses are used to visually display the information. The information query generation unit also uses AR technology to overlay the information query results in the crew's field of vision. For example, detailed product information and usage instructions are visually displayed. The information query generation unit also develops a system that displays the information query results in the crew's field of vision using AR technology, enabling a quick response. For example, an AR device is used to display information in real time. This allows the crew to quickly visually confirm the information.

[0037] The information query generation unit can send the generated information query results to the crew's smartphone or tablet via push notification. The information query generation unit, for example, builds a system that sends the information query results to the crew's smartphone or tablet via push notification. For example, it allows important information to be checked immediately. The information query generation unit also uses a push notification function to send the information query results to the crew's device. For example, it notifies the crew of product inventory status and price information in real time. The information query generation unit also develops a system that sends the information query results to the crew's smartphone or tablet via push notification, allowing them to check the information immediately. For example, it allows the crew who receive the notification to respond quickly. This allows the crew to check the information immediately.

[0038] The talk generation unit can analyze the crew's past performance data and propose individually optimized talk methods. For example, the talk generation unit builds a system in which a generation AI analyzes the crew's past performance data and proposes individually optimized talk methods. For example, it provides an optimal talk method based on past success stories. The talk generation unit also analyzes the crew's performance data and proposes individually optimized talk methods. For example, it provides a talk method that takes into account the crew's strengths and weaknesses. The talk generation unit also develops a system in which a generation AI analyzes the crew's past performance data and proposes individually optimized talk methods. For example, it indicates areas for improvement in talk based on performance data. This makes it possible to propose an optimal talk method based on the crew's individual performance.

[0039] The talk generation unit can perform simulations and present the optimal talk method for different scenarios. For example, the talk generation unit constructs a system in which a generation AI performs simulations and presents the optimal talk method for different scenarios. For example, it compares multiple scenarios and presents the most effective talk method. The talk generation unit also simulates different scenarios in a set of talk methods using a generation AI and presents the optimal talk method. For example, it evaluates the effectiveness of each scenario and selects the optimal talk method. The talk generation unit also develops a system in which a generation AI performs simulations and presents the optimal talk method for different scenarios. For example, it selects a talk method based on the success rate of each scenario. This makes it possible to present the optimal talk method for different scenarios.

[0040] The talk generation unit can visually show a set of talk methods using videos or animations, allowing the crew to intuitively understand. The talk generation unit, for example, builds a system that visually shows a set of talk methods using videos or animations. For example, the flow and key points of the talk are explained using videos. The talk generation unit also shows the set of talk methods using animations, allowing the crew to intuitively understand. For example, the steps of the talk are visually displayed using animations. The talk generation unit also develops a system that visually shows a set of talk methods using videos or animations, allowing the crew to intuitively understand. For example, specific examples of talks are shown using videos. This allows the crew to intuitively understand the talk methods.

[0041] The talk generation unit can display the set of talk methods on the crew's smartwatch or smartglasses so that they can be referenced in real time. The talk generation unit, for example, builds a system that displays the set of talk methods on the crew's smartwatch or smartglasses. For example, it displays the key points of the talk in real time. The talk generation unit also displays the set of talk methods on the smartwatch or smartglasses so that the crew can refer to the talk methods in real time. For example, it visually shows the flow of the talk. The talk generation unit also develops a system that displays the set of talk methods on the crew's smartwatch or smartglasses so that they can be referenced in real time. For example, it displays the specific steps of the talk. This allows the crew to refer to the talk methods in real time.

[0042] The talk generation unit can create individual learning plans to improve the crew's knowledge and customer service skill levels and provide them to the crew. For example, the talk generation unit constructs a system in which a generation AI evaluates the crew's knowledge and customer service skill levels and creates individual learning plans. For example, it provides learning plans that take into account the crew's strengths and weaknesses. The talk generation unit also analyzes the crew's performance data and the generation AI creates individual learning plans. For example, it suggests optimal learning content based on past performance. The talk generation unit also develops a system in which a generation AI evaluates the crew's knowledge and customer service skill levels and creates individual learning plans. For example, it provides training based on the learning plan. This makes it possible to individually improve the crew's knowledge and customer service skill levels.

[0043] The talk generation unit can periodically evaluate the crew's knowledge level and customer service skill level and provide feedback on the results. For example, the talk generation unit builds a system in which a generation AI periodically evaluates the crew's knowledge level and customer service skill level and provides feedback on the results. For example, it conducts regular tests and evaluations and reports progress. The talk generation unit also analyzes the crew's performance data, and the generation AI periodically evaluates them and provides feedback on their progress. For example, it visualizes the crew's growth and sets the next learning goal. The talk generation unit also develops a system in which a generation AI periodically evaluates the crew's knowledge level and customer service skill level and provides feedback on the results. For example, it provides a training plan based on the evaluation results. This makes it possible to periodically evaluate and provide feedback on the progress of the crew's knowledge level and customer service skill level.

[0044] The talk generation unit can provide learning content that incorporates best practices from different industries. For example, the talk generation unit builds a system in which a generation AI provides learning content that incorporates best practices from different industries. For example, it provides training based on success stories from other industries. The talk generation unit also improves the knowledge level and customer service skill level of crew members by incorporating best practices from different industries into the learning content. For example, it provides opportunities to learn customer service skills from other industries. The talk generation unit also develops a system in which a generation AI provides learning content that incorporates best practices from different industries. For example, it provides opportunities to learn through cross-industry exchanges. This makes it possible to incorporate best practices from different industries into the learning content.

[0045] The talk generation unit can provide gamified learning programs and create an environment where crew members can learn in a fun way. For example, the talk generation unit builds a system in which the generation AI provides gamified learning programs. For example, it provides opportunities to learn through quizzes or simulation games. The talk generation unit also creates an environment in which crew members can learn in a fun way through gamified learning programs. For example, it increases motivation by earning points and badges. The talk generation unit also develops a system in which the generation AI provides gamified learning programs. For example, it provides learning programs that incorporate competitive elements. This can provide an environment in which crew members can learn in a fun way.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The crew support system can further include a purchasing intent estimation unit that estimates a customer's purchasing intent. The purchasing intent estimation unit analyzes the customer's past purchase history and current browsing history to generate a score for purchasing intent. For example, it estimates purchasing intent based on the frequency with which similar products have been purchased in the past and the current browsing time. The purchasing intent estimation unit can also analyze the customer's social media activity to estimate purchasing intent. For example, it can evaluate purchasing intent based on the number of "likes" and comments on a particular product. This allows the crew to provide customer service that is in line with the customer's purchasing intent.

[0048] The crew support system can further include a health condition estimation unit that estimates the customer's health condition. The health condition estimation unit estimates the customer's health condition based on the customer's input information and past data. For example, it can analyze the frequency of health food purchases from past purchase history to estimate the customer's level of health consciousness. The health condition estimation unit can also analyze the content of the customer's social media posts to estimate the customer's health condition. For example, it can evaluate the customer's health condition based on posts about exercise and diet. This allows the crew to suggest products that suit the customer's health condition.

[0049] The crew support system can further include a hobby estimation unit that estimates the customer's hobbies and interests. The hobby estimation unit estimates hobbies and interests based on the customer's input information and past data. For example, it estimates hobbies by analyzing the frequency of purchasing products in a specific genre from past purchase history. The hobby estimation unit can also analyze the customer's social media activity to estimate hobbies and interests. For example, it can evaluate hobbies based on posts about specific events or activities. This allows the crew to suggest products that match the customer's hobbies and interests.

[0050] The crew support system can further include a purchasing pattern analysis unit that analyzes customer purchasing patterns. The purchasing pattern analysis unit analyzes a customer's past purchasing history to identify purchasing patterns. For example, it analyzes tendencies to make purchases on specific days of the week or at specific times of the day to estimate purchasing patterns. The purchasing pattern analysis unit can also analyze a customer's social media activity to estimate purchasing patterns. For example, it evaluates purchasing patterns based on posts about specific events or sales. This allows the crew to provide customer service that matches the customer's purchasing patterns.

[0051] The crew support system can further include a lifestyle estimation unit that estimates a customer's lifestyle. The lifestyle estimation unit estimates a customer's lifestyle based on the customer's input information and past data. For example, it estimates a customer's lifestyle by analyzing the frequency of purchases of specific brands or products from past purchase history. The lifestyle estimation unit can also estimate a customer's lifestyle by analyzing the customer's social media activity. For example, it can evaluate a customer's lifestyle based on posts about travel or hobbies. This allows the crew to make product suggestions that suit the customer's lifestyle.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The customer situation input section inputs the customer's situation, such as the customer's age, gender, purchase history, and current needs. Step 2: The conversation generation unit generates conversations for high-achieving crew members based on the customer situation input by the customer situation input unit. For example, the generation AI uses a text generation AI (e.g., LLM) to generate specific conversations for customers, such as, "Customer, this product is very popular and is especially perfect for this season." Step 3: The information query generator generates the necessary information query along with the conversation generated by the conversation generator. For example, the information query generator provides the crew with a way to access the information they need, such as "Please refer to the link below to find out more information about the product you are interested in."

[0054] (Example 2) The crew support system according to an embodiment of the present invention is a system that inputs the customer's situation into a generation AI and outputs a set of talks by highly successful crew members, as well as necessary information inquiries and talk methods. This allows the crew support system to simultaneously improve the crew's knowledge level and customer service skill level.

[0055] The crew assistance system according to the embodiment includes a customer status input unit, a conversation generation unit, and an information query generation unit. The customer status input unit inputs customer status information, such as the customer's age, gender, purchase history, and current needs. The conversation generation unit generates conversations for high-achieving crew members based on the customer status input by the customer status input unit. For example, the generation AI uses a text generation AI (e.g., LLM) to generate specific conversations for the customer, such as, "Customer, this product is very popular and is especially perfect for this season." The information query generation unit generates necessary information queries along with the conversation generated by the conversation generation unit. For example, the information query generation unit provides crew members with a way to access the information they need, such as, "Please refer to the link below to find out more information about the product you are interested in." This allows the crew assistance system to simultaneously improve the crew members' knowledge and customer service skills.

[0056] The conversation generation unit can generate conversations based on information included in the customer's situation, such as age, gender, purchase history, and current needs. For example, the conversation generation unit uses a generation AI to perform emotion analysis based on the customer's input information and calculate an emotion score. For example, it uses text analysis to infer emotions from the customer's language and expressions, and generates conversations based on those emotions. The conversation generation unit also analyzes voice data and facial expression data in addition to the customer's input information to infer emotions. For example, it reads emotions from voice tone and facial expressions and generates conversations based on those emotions. The conversation generation unit also uses a generation AI to analyze emotional trends based on the customer's past purchase history and behavioral data, and generates conversations based on those emotions. For example, it infers customer preferences and emotional patterns from past purchase history and generates conversations based on those emotions. This allows conversations to be generated based on the customer's detailed situation.

[0057] The information query generation unit can generate links to provide detailed information about products in which a customer is interested. For example, the information query generation unit connects with the customer's social media accounts and collects posts and reviews. For example, it analyzes posts on Twitter and Instagram to understand the customer's interests. The information query generation unit also collects social media activity data in addition to the customer's past purchase history, allowing the generation AI to grasp the detailed situation. For example, it analyzes Facebook "likes" and comments to infer the customer's preferences. The information query generation unit also collects ratings and comments on customer review sites, allowing the generation AI to grasp the detailed situation. For example, it analyzes reviews on Amazon and Yelp to understand the customer's purchasing trends and satisfaction level. This allows the system to quickly provide detailed information about products in which the customer is interested.

[0058] The conversation generation unit can generate conversation methods that greet customers with a smile and then explain the features of products. The conversation generation unit, for example, uses in-store sensors to collect customer movement patterns in real time and inputs this into the generation AI. For example, it tracks customer movements using beacons and cameras. The conversation generation unit also collects the history of products viewed by customers in real time and inputs this into the generation AI. For example, it uses smart shelves and electronic tags to record the products customers pick up. The conversation generation unit also uses in-store Wi-Fi to collect location information from customers' smartphones and inputs this into the generation AI. For example, it identifies customers' locations using Wi-Fi access points and analyzes their movement patterns. This allows staff to learn and practice effective customer service methods.

[0059] The conversation generation unit can infer a customer's emotions and generate conversations that correspond to those emotions. For example, the conversation generation unit uses a generation AI to perform emotion analysis based on the customer's input information and calculate an emotion score. For example, it uses text analysis to infer emotions from the customer's language and expressions and generates conversations that correspond to those emotions. The conversation generation unit also analyzes voice data and facial expression data in addition to the customer's input information to infer emotions. For example, it reads emotions from voice tone and facial expressions and generates conversations that correspond to those emotions. The conversation generation unit also uses a generation AI to analyze emotional trends based on the customer's past purchase history and behavioral data and generate conversations that correspond to those emotions. For example, it infers a customer's preferences and emotional patterns from their past purchase history and generates conversations based on that. This makes it possible to generate conversations that correspond to the customer's emotions.

[0060] The information query generation unit can automatically summarize the results of generated information queries, allowing the crew to quickly understand them. For example, the information query generation unit will build a system in which the generation AI automatically summarizes the results of information queries, allowing the crew to quickly understand them. For example, it will display a short summary of long pieces of information. The information query generation unit will also analyze the results of information queries with the generation AI, extract and summarize the important points. For example, it will display a concise summary of the features and advantages of a product. The information query generation unit will also develop a system in which the generation AI automatically summarizes the results of information queries, allowing the crew to quickly understand them. For example, it will integrate and summarize information obtained from multiple sources. This will allow the crew to quickly understand the information and respond.

[0061] The information query generation unit can measure the customer's level of interest using the emotion estimation function and provide information of greatest interest on a priority basis. The information query generation unit, for example, uses the emotion estimation function to measure the customer's level of interest and builds a system that provides information of greatest interest on a priority basis. For example, it determines the priority of information based on the emotion score. The information query generation unit also introduces a device equipped with a function that analyzes the customer's facial expressions and voice and measures the level of interest. For example, it analyzes the customer's level of interest in real time using a camera or microphone. The information query generation unit also measures the customer's level of interest based on the emotion estimation data and develops a system that provides information of greatest interest on a priority basis. For example, it adjusts the display order of information according to the emotion score. This makes it possible to provide information according to the customer's level of interest.

[0062] The talk generation unit can monitor customers' emotional reactions to the generated talk in real time and adjust the talk as needed. The talk generation unit, for example, uses an emotion estimation function to build a system that monitors customers' emotional reactions to the generated talk in real time. For example, the talk content is adjusted based on the emotion score. The talk generation unit also develops a system that analyzes customers' emotional reactions in real time and adjusts the talk based on the results. For example, the talk is changed if negative emotions are detected. The talk generation unit also builds a system that monitors customers' emotional reactions to the generated talk based on the emotion estimation data and adjusts the talk as needed. For example, it provides variations of the talk according to the emotion score. This makes it possible to adjust the talk according to the customers' emotional reactions.

[0063] The talk generation unit can perform automatic translation and cultural adaptation to adapt to different languages ​​and cultures. For example, the talk generation unit builds a system in which a generation AI automatically translates the talk of high-achieving crew members and adapts it to different languages. For example, it translates from English to Japanese and provides talk that is tailored to the culture. The talk generation unit also translates the talk of high-achieving crew members to different cultures, with the generation AI taking cultural nuances into account. For example, it provides talk that reflects cultural expressions and customs. The talk generation unit also develops a system in which a generation AI automatically translates the talk of high-achieving crew members and adapts it to different languages ​​and cultures. For example, it provides talk that takes into account not only language differences but also cultural backgrounds. This makes it possible to generate talk that is adapted to different languages ​​and cultures.

[0064] The talk generation unit can use speech synthesis technology to suggest the tone and pace at which the crew will actually speak. For example, the talk generation unit builds a system in which a generation AI uses speech synthesis technology to suggest the tone and pace at which the crew will actually speak. For example, it provides the optimal tone and pace according to the content of the talk. In addition, when generating talk, the talk generation unit uses speech synthesis technology to simulate the tone and pace at which the crew will speak. For example, it suggests the tone and pace to maximize the effectiveness of the talk. In addition, the talk generation unit develops a system in which a generation AI uses speech synthesis technology to suggest the tone and pace at which the crew will actually speak. For example, it performs optimal speech synthesis according to the content of the talk. This makes it possible to optimize the tone and pace at which the crew actually speaks.

[0065] The information query generation unit can display the generated information query results in the crew's field of vision using AR technology. The information query generation unit, for example, builds a system that displays the information query results in the crew's field of vision using AR technology. For example, smart glasses are used to visually display the information. The information query generation unit also uses AR technology to overlay the information query results in the crew's field of vision. For example, detailed product information and usage instructions are visually displayed. The information query generation unit also develops a system that displays the information query results in the crew's field of vision using AR technology, enabling a quick response. For example, an AR device is used to display information in real time. This allows the crew to quickly visually confirm the information.

[0066] The information query generation unit can send the generated information query results to the crew's smartphone or tablet via push notification. The information query generation unit, for example, builds a system that sends the information query results to the crew's smartphone or tablet via push notification. For example, it allows important information to be checked immediately. The information query generation unit also uses a push notification function to send the information query results to the crew's device. For example, it notifies the crew of product inventory status and price information in real time. The information query generation unit also develops a system that sends the information query results to the crew's smartphone or tablet via push notification, allowing them to check the information immediately. For example, it allows the crew who receive the notification to respond quickly. This allows the crew to check the information immediately.

[0067] The talk generation unit can analyze the crew's past performance data and propose individually optimized talk methods. For example, the talk generation unit builds a system in which a generation AI analyzes the crew's past performance data and proposes individually optimized talk methods. For example, it provides an optimal talk method based on past success stories. The talk generation unit also analyzes the crew's performance data and proposes individually optimized talk methods. For example, it provides a talk method that takes into account the crew's strengths and weaknesses. The talk generation unit also develops a system in which a generation AI analyzes the crew's past performance data and proposes individually optimized talk methods. For example, it indicates areas for improvement in talk based on performance data. This makes it possible to propose an optimal talk method based on the crew's individual performance.

[0068] The talk generation unit can perform simulations and present the optimal talk method for different scenarios. For example, the talk generation unit constructs a system in which a generation AI performs simulations and presents the optimal talk method for different scenarios. For example, it compares multiple scenarios and presents the most effective talk method. The talk generation unit also simulates different scenarios in a set of talk methods using a generation AI and presents the optimal talk method. For example, it evaluates the effectiveness of each scenario and selects the optimal talk method. The talk generation unit also develops a system in which a generation AI performs simulations and presents the optimal talk method for different scenarios. For example, it selects a talk method based on the success rate of each scenario. This makes it possible to present the optimal talk method for different scenarios.

[0069] The talk generation unit can use the emotion estimation function to suggest a conversation method that matches the customer's emotions. The talk generation unit, for example, uses the emotion estimation function to build a system that suggests a conversation method that matches the customer's emotions. For example, the content of the conversation is adjusted based on the emotion score. The talk generation unit also analyzes the customer's emotions in real time and suggests the optimal conversation method based on the results. For example, encouraging conversation is provided for positive emotions. The talk generation unit also develops a system that suggests a conversation method that matches the customer's emotions based on the emotion estimation data. For example, variations in conversation are provided based on the emotion score. This makes it possible to suggest a conversation method that matches the customer's emotions.

[0070] The talk generation unit can visually show a set of talk methods using videos or animations, allowing the crew to intuitively understand. The talk generation unit, for example, builds a system that visually shows a set of talk methods using videos or animations. For example, the flow and key points of the talk are explained using videos. The talk generation unit also shows the set of talk methods using animations, allowing the crew to intuitively understand. For example, the steps of the talk are visually displayed using animations. The talk generation unit also develops a system that visually shows a set of talk methods using videos or animations, allowing the crew to intuitively understand. For example, specific examples of talks are shown using videos. This allows the crew to intuitively understand the talk methods.

[0071] The talk generation unit can display the set of talk methods on the crew's smartwatch or smartglasses so that they can be referenced in real time. The talk generation unit, for example, builds a system that displays the set of talk methods on the crew's smartwatch or smartglasses. For example, it displays the key points of the talk in real time. The talk generation unit also displays the set of talk methods on the smartwatch or smartglasses so that the crew can refer to the talk methods in real time. For example, it visually shows the flow of the talk. The talk generation unit also develops a system that displays the set of talk methods on the crew's smartwatch or smartglasses so that they can be referenced in real time. For example, it displays the specific steps of the talk. This allows the crew to refer to the talk methods in real time.

[0072] The talk generation unit can use the emotion estimation function to analyze the crew's emotional reactions to the set of talk methods and continuously improve the optimal talk method. The talk generation unit, for example, uses the emotion estimation function to build a system that analyzes the crew's emotional reactions to the set of talk methods. For example, the talk method is improved based on the emotion score. The talk generation unit also analyzes the crew's emotional reactions in real time and develops a system that continuously improves the talk method based on the results. For example, the talk generation unit prioritizes the adoption of talk methods that induce strong positive emotions. The talk generation unit also analyzes the crew's emotional reactions to the set of talk methods based on the emotion estimation data and builds a system that continuously improves the optimal talk method. For example, it provides variations of talk methods according to the emotion score. This allows the talk method to be continuously improved based on the crew's emotional reactions.

[0073] The talk generation unit can create individual learning plans to improve the crew's knowledge and customer service skill levels and provide them to the crew. For example, the talk generation unit constructs a system in which a generation AI evaluates the crew's knowledge and customer service skill levels and creates individual learning plans. For example, it provides learning plans that take into account the crew's strengths and weaknesses. The talk generation unit also analyzes the crew's performance data and the generation AI creates individual learning plans. For example, it suggests optimal learning content based on past performance. The talk generation unit also develops a system in which a generation AI evaluates the crew's knowledge and customer service skill levels and creates individual learning plans. For example, it provides training based on the learning plan. This makes it possible to individually improve the crew's knowledge and customer service skill levels.

[0074] The talk generation unit can periodically evaluate the crew's knowledge level and customer service skill level and provide feedback on the results. For example, the talk generation unit builds a system in which a generation AI periodically evaluates the crew's knowledge level and customer service skill level and provides feedback on the results. For example, it conducts regular tests and evaluations and reports progress. The talk generation unit also analyzes the crew's performance data, and the generation AI periodically evaluates them and provides feedback on their progress. For example, it visualizes the crew's growth and sets the next learning goal. The talk generation unit also develops a system in which a generation AI periodically evaluates the crew's knowledge level and customer service skill level and provides feedback on the results. For example, it provides a training plan based on the evaluation results. This makes it possible to periodically evaluate and provide feedback on the progress of the crew's knowledge level and customer service skill level.

[0075] The talk generation unit can measure the motivation of the crew using the emotion estimation function and suggest the optimal learning method. The talk generation unit, for example, uses the emotion estimation function to build a system that measures the motivation of the crew and suggests the optimal learning method. For example, it adjusts the learning plan based on the emotion score. The talk generation unit also analyzes the emotional reactions of the crew in real time and suggests the optimal learning method based on the results. For example, it prioritizes providing learning methods that induce strong positive emotions. The talk generation unit also develops a system that measures the motivation of the crew based on the emotion estimation data and suggests the optimal learning method. For example, it provides learning content according to the emotion score. This makes it possible to suggest the optimal learning method according to the crew's motivation.

[0076] The talk generation unit can provide learning content that incorporates best practices from different industries. For example, the talk generation unit builds a system in which a generation AI provides learning content that incorporates best practices from different industries. For example, it provides training based on success stories from other industries. The talk generation unit also improves the knowledge level and customer service skill level of crew members by incorporating best practices from different industries into the learning content. For example, it provides opportunities to learn customer service skills from other industries. The talk generation unit also develops a system in which a generation AI provides learning content that incorporates best practices from different industries. For example, it provides opportunities to learn through cross-industry exchanges. This makes it possible to incorporate best practices from different industries into the learning content.

[0077] The talk generation unit can provide gamified learning programs and create an environment where crew members can learn in a fun way. For example, the talk generation unit builds a system in which the generation AI provides gamified learning programs. For example, it provides opportunities to learn through quizzes or simulation games. The talk generation unit also creates an environment in which crew members can learn in a fun way through gamified learning programs. For example, it increases motivation by earning points and badges. The talk generation unit also develops a system in which the generation AI provides gamified learning programs. For example, it provides learning programs that incorporate competitive elements. This can provide an environment in which crew members can learn in a fun way.

[0078] The talk generation unit uses the emotion estimation function to analyze the crew's emotional responses to learning and can adjust the optimal learning pace and content. The talk generation unit, for example, uses the emotion estimation function to build a system that analyzes the crew's emotional responses to learning. For example, the learning pace is adjusted based on the emotion score. The talk generation unit also analyzes the crew's emotional responses in real time and adjusts the optimal learning pace and content based on the results. For example, it prioritizes providing learning content that evokes strong positive emotions. The talk generation unit also analyzes the crew's emotional responses to learning based on the emotion estimation data and develops a system that adjusts the optimal learning pace and content. For example, it provides a learning plan based on the emotion score. This makes it possible to optimize the learning pace and content based on the crew's emotional responses.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The crew support system can further include a purchasing intent estimation unit that estimates a customer's purchasing intent. The purchasing intent estimation unit analyzes the customer's past purchase history and current browsing history to generate a score for purchasing intent. For example, it estimates purchasing intent based on the frequency with which similar products have been purchased in the past and the current browsing time. The purchasing intent estimation unit can also analyze the customer's social media activity to estimate purchasing intent. For example, it can evaluate purchasing intent based on the number of "likes" and comments on a particular product. This allows the crew to provide customer service that is in line with the customer's purchasing intent.

[0081] The crew support system can further include a health condition estimation unit that estimates the customer's health condition. The health condition estimation unit estimates the customer's health condition based on the customer's input information and past data. For example, it can analyze the frequency of health food purchases from past purchase history to estimate the customer's level of health consciousness. The health condition estimation unit can also analyze the content of the customer's social media posts to estimate the customer's health condition. For example, it can evaluate the customer's health condition based on posts about exercise and diet. This allows the crew to suggest products that suit the customer's health condition.

[0082] The crew support system can further include a hobby estimation unit that estimates the customer's hobbies and interests. The hobby estimation unit estimates hobbies and interests based on the customer's input information and past data. For example, it estimates hobbies by analyzing the frequency of purchasing products in a specific genre from past purchase history. The hobby estimation unit can also analyze the customer's social media activity to estimate hobbies and interests. For example, it can evaluate hobbies based on posts about specific events or activities. This allows the crew to suggest products that match the customer's hobbies and interests.

[0083] The crew support system can further include a purchasing pattern analysis unit that analyzes customer purchasing patterns. The purchasing pattern analysis unit analyzes a customer's past purchasing history to identify purchasing patterns. For example, it analyzes tendencies to make purchases on specific days of the week or at specific times of the day to estimate purchasing patterns. The purchasing pattern analysis unit can also analyze a customer's social media activity to estimate purchasing patterns. For example, it evaluates purchasing patterns based on posts about specific events or sales. This allows the crew to provide customer service that matches the customer's purchasing patterns.

[0084] The crew support system can further include a lifestyle estimation unit that estimates a customer's lifestyle. The lifestyle estimation unit estimates a customer's lifestyle based on the customer's input information and past data. For example, it estimates a customer's lifestyle by analyzing the frequency of purchases of specific brands or products from past purchase history. The lifestyle estimation unit can also estimate a customer's lifestyle by analyzing the customer's social media activity. For example, it can evaluate a customer's lifestyle based on posts about travel or hobbies. This allows the crew to make product suggestions that suit the customer's lifestyle.

[0085] The crew support system can further estimate the emotions of customers and adjust the service method based on those emotions. For example, the emotion estimation function can be used to analyze the customer's input information and voice data to estimate their emotions. For example, emotions can be read from the tone of voice and the way the customer speaks, and a service method appropriate to the emotion can be provided. The emotion estimation function can also be used to analyze the customer's facial expression data to estimate their emotions. For example, emotions can be read from facial expressions, and a service method appropriate to the emotion can be provided. This allows the crew to provide service appropriate to the customer's emotions.

[0086] The crew support system can further estimate the emotions of customers and make product suggestions based on those emotions. For example, the emotion estimation function can be used to analyze the customer's input information and voice data to estimate their emotions. For example, emotions can be read from the tone of voice and the way the customer speaks, and product suggestions can be provided that correspond to the emotions. The emotion estimation function can also be used to analyze the customer's facial expression data to estimate their emotions. For example, emotions can be read from facial expressions, and product suggestions can be provided that correspond to the emotions. This allows the crew to make product suggestions that correspond to the customer's emotions.

[0087] The crew support system can further estimate the emotions of the customer and adjust the method of providing service based on those emotions. For example, the emotion estimation function can be used to analyze the customer's input information and voice data to estimate the emotion. For example, emotions can be read from the tone of voice and the use of words, and a method of providing service according to the emotion can be provided. The emotion estimation function can also be used to analyze the customer's facial expression data to estimate the emotion. For example, emotions can be read from facial expressions, and a method of providing service according to the emotion can be provided. This allows the crew to provide service according to the customer's emotions.

[0088] The crew support system can further estimate the customer's emotions and adjust the follow-up method based on those emotions. For example, the emotion estimation function can be used to analyze the customer's input information and voice data to estimate their emotions. For example, emotions can be read from the tone of voice and the way the customer speaks, and a follow-up method appropriate for the emotion can be provided. The emotion estimation function can also be used to analyze the customer's facial expression data to estimate their emotions. For example, emotions can be read from facial expressions, and a follow-up method appropriate for the emotion can be provided. This allows the crew to follow up based on the customer's emotions.

[0089] The crew assistance system can further estimate the emotions of customers and make recommendations based on those emotions. For example, the emotion estimation function can be used to analyze the customer's input information and voice data to estimate their emotions. For example, emotions can be read from the tone of voice and the use of words, and recommendations can be provided according to those emotions. The emotion estimation function can also be used to analyze the customer's facial expression data to estimate their emotions. For example, emotions can be read from facial expressions, and recommendations can be provided according to those emotions. This allows the crew to make recommendations according to the customer's emotions.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The customer situation input section inputs the customer's situation, such as the customer's age, gender, purchase history, and current needs. Step 2: The conversation generation unit generates conversations for high-achieving crew members based on the customer situation input by the customer situation input unit. For example, the generation AI uses a text generation AI (e.g., LLM) to generate specific conversations for customers, such as, "Customer, this product is very popular and is especially perfect for this season." Step 3: The information query generator generates the necessary information query along with the conversation generated by the conversation generator. For example, the information query generator provides the crew with a way to access the information they need, such as "Please refer to the link below to find out more information about the product you are interested in."

[0092] 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.

[0093] 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.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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 AI 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.

[0109] 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.

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

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the headset type terminal 314, the 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.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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 AI 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.

[0124] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] 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.

[0138] 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.

[0139] 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 AI 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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]

[0159] 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 customer situation input section for inputting the customer's situation; A talk generation unit that generates talks of high-earning crew members based on the customer status input by the customer status input unit; an information query generation unit that generates a necessary information query together with the token generated by the token generation unit; A system characterized by:

2. The token generation unit Generate a conversation based on the customer's age, gender, purchase history, and current needs.

2. The system of claim 1.

3. The information query generation unit Generate a link to provide more information about the product that the customer is interested in 2. The system of claim 1.

4. The token generation unit Generate a conversation style that greets the customer with a smile and then explains the features of the product 2. The system of claim 1.

5. The token generation unit Estimate the customer's emotions and generate a conversation based on those emotions 2. The system of claim 1.

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