system

Abstract

The system according to this embodiment aims to increase the number of customers entering the store during earlier hours by enabling natural conversations via voice. [Solution] The system according to the embodiment comprises a conversation unit, an information storage unit, an avatar unit, and an exchange unit. The conversation unit enables natural conversation using voice. The information storage unit stores customer information. The avatar unit uses avatars of actual employees. The exchange unit handles SNS exchanges.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the number of customers entering the store in the early hours is small in nightclubs and lounges, and the cost burden of having employees is large.

[0005] The system according to the embodiment aims to realize a natural conversation in voice and increase the number of customers entering the store in the early hours.

Means for Solving the Problems

[0006] The system according to the embodiment includes a conversation unit, an information holding unit, an avatar unit, and an exchange unit. The conversation unit realizes a natural conversation in voice. The information holding unit holds customer information. The avatar unit uses an avatar of an actual employee. The exchange unit performs SNS exchange.

Effects of the Invention

[0007] The system according to this embodiment enables natural voice conversation and can increase the number of customers entering the store during earlier hours. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The system in a nightclub or lounge according to an embodiment of the present invention is a system that increases the number of customers entering during the early hours and reduces the cost burden on employees. This system uses an AI agent to enable natural voice conversation. The system allows customers to have natural conversations with employee avatars displayed on a screen and order drinks and other items. Furthermore, it provides a mechanism to retain customer information and encourage future visits. It uses avatars of actual employees, providing the value of being able to meet real employees through avatars. The system also allows for the exchange of SNS information upon arrival, enabling natural interaction with avatars and even reservations for future visits. For example, the AI ​​agent enables natural voice conversation. Customers can converse with employee avatars displayed on a screen and order drinks. In this case, the AI ​​agent recognizes the customer's voice and provides appropriate responses. This makes it possible to provide an environment where customers can enjoy themselves even during the early hours. Next, it provides a mechanism to retain customer information and encourage future visits. For example, the AI ​​agent records information such as the customer's name and preferred drinks, which can be used on their next visit. This makes it possible to provide personalized services to customers. Furthermore, the system utilizes avatars of actual employees, offering the added value of allowing customers to "meet" real employees through the avatars. For example, an avatar on the display mimics the appearance of a real employee, allowing customers to meet that employee on their next visit. This provides customers with a special experience. The system also allows for the exchange of social media information upon arrival, enabling natural interaction with the avatar and even booking future visits. For example, the avatar on the display might suggest exchanging social media information, allowing customers to book their next visit. This can improve customer retention rates. In this way, utilizing AI agents can increase the number of customers entering nightclubs and lounges during earlier hours, reducing the cost burden on employees.

[0029] The system according to this embodiment comprises a conversation unit, an information storage unit, an avatar unit, and an exchange unit. The conversation unit enables natural voice conversation. The conversation unit recognizes the customer's voice using, for example, speech recognition technology and provides an appropriate response. The conversation unit can adjust the timing of responses to maintain a natural flow of conversation. The conversation unit can enable customers to have natural conversations with employee avatars displayed on a screen. The information storage unit stores customer information. The information storage unit can record information such as the customer's name and preferred drinks, which can be used for future visits. The information storage unit can store customer information in a database and update it as needed. The information storage unit can record customer purchase history, which can be used to provide personalized services. The avatar unit utilizes avatars of actual employees. The avatar unit can display avatars that mimic the appearance of actual employees. The avatar unit can reproduce the characteristics of actual employees using, for example, 3D models or animations. The avatar unit can mimic the voice and speaking style of actual employees. The exchange unit handles the exchange of social networking services (SNS). For example, the exchange unit can have an avatar on the display suggest the exchange of SNS, allowing the customer to make a reservation for their next visit. The exchange unit can also suggest specific SNS accounts, making it easy for the customer to exchange them. For example, the exchange unit can guide the customer through the SNS exchange process, ensuring a smooth exchange. As a result, the system according to this embodiment can achieve natural voice conversations, retain customer information, and use avatars of actual employees to exchange SNS information.

[0030] The conversation unit enables natural voice conversations. For example, it uses speech recognition technology to recognize the customer's voice and provide an appropriate response. Specifically, the speech recognition technology utilizes a deep learning-based voice model to convert the customer's utterances into text with high accuracy. This text data is analyzed using natural language processing (NLP) technology to understand the customer's intent and the content of their questions. Next, the conversation unit generates an appropriate response using a pre-prepared response database and generative AI. The generative AI can, for example, generate natural and fluent responses while considering the context of the conversation. To adjust the timing of responses, the conversation unit monitors the flow of the conversation and the customer's reactions in real time to maintain appropriate pauses. Furthermore, the conversation unit synchronizes the facial expressions and movements of the employee avatar displayed on the screen so that the customer can have a natural conversation with the avatar. This allows the customer to have an experience as if they were talking to a real employee. The conversation unit can also support multiple languages ​​and can accommodate customers who speak different languages. This allows the conversational unit to utilize speech recognition and natural language processing technologies to achieve natural conversations with customers and provide a superior user experience.

[0031] The information retention unit stores customer information. For example, it records information such as the customer's name and preferred beverages, which can then be used for future visits. Specifically, the information retention unit efficiently manages customer information using a database system. When a customer visits for the first time, information collected through the conversation unit is sent to the information retention unit and stored in the database. This includes the customer's name, contact information, preferred beverages and food, and allergy information. On subsequent visits, the information retention unit can quickly retrieve this information and provide it to the conversation unit and avatar unit, enabling personalized service. Furthermore, the information retention unit records the customer's purchase history and can provide recommendations based on this. For example, it can suggest new products and services based on previously purchased items and services. The information retention unit also prioritizes data security, implementing encryption technology and access control to thoroughly protect personal information. This allows the information retention unit to safely and efficiently manage customer information and provide a foundation for delivering personalized services.

[0032] The avatar function utilizes avatars of actual employees. For example, it can display avatars that mimic the appearance of real employees. Specifically, the avatar function uses 3D modeling technology to faithfully reproduce the appearance and movements of actual employees. This includes facial expressions, body movements, and clothing. The avatars can react in real time to customer interactions and change their expressions. Furthermore, the avatar function uses speech synthesis technology to mimic the voices and speaking styles of actual employees. This technology learns the characteristics of the employee's voice and can generate natural speech. This allows customers to experience interacting with actual employees. The avatar function also uses animation technology to smooth the avatar's movements and achieve a more realistic expression. This makes the avatar's interactions with customers more natural and engaging. Additionally, the avatar function can display multiple avatars simultaneously and allows switching between avatars of different employees. This allows the avatar function to faithfully reproduce the characteristics of actual employees and provide customers with a realistic conversational experience.

[0033] The exchange unit facilitates the exchange of social media accounts. For example, an avatar on the display might suggest exchanging social media accounts, allowing customers to make appointments for their next visit. Specifically, the exchange unit manages social media account information, making it easy for customers to exchange accounts. The avatar on the display naturally suggests exchanging social media accounts during the conversation, and if the customer is interested, it guides them through the specific steps. For example, it might display a 2D code (e.g., QR code®) so that customers can exchange social media accounts simply by scanning it with their smartphone. The exchange unit also provides voice and visual guidance to guide customers through the social media exchange process and ensure a smooth exchange. Furthermore, the exchange unit can send reminders via social media when customers make their next appointment. This ensures that customers don't forget their next visit and can make appointments smoothly. The exchange unit can also suggest specific social media accounts and provide content and information that customers might be interested in. This allows the exchange unit to strengthen communication with customers and promote an increase in repeat customers.

[0034] The system includes a customer visit promotion unit that encourages repeat visits. This unit can, for example, encourage customers to return by offering promotions or discount coupons. It can also attract customers' interest by announcing events. Furthermore, it can improve customer retention rates by offering special services or benefits. This allows the system to encourage repeat visits. Some or all of the above processes in the customer visit promotion unit may be performed using AI, or not. For example, the customer visit promotion unit can input a customer's past visit history into the AI, which can then suggest the most suitable promotions and benefits.

[0035] The system includes a reservation section for booking the next visit. The reservation section can, for example, provide an online reservation system, allowing customers to easily book their next visit. The reservation section can, for example, accept telephone reservations, allowing customers to book directly. The reservation section can, for example, provide a reservation confirmation method, allowing customers to check their reservation details. This enables the system to book the next visit. Some or all of the above processes in the reservation section may be performed using AI, for example, or not using AI. For example, the reservation section can input the customer's past reservation history into the AI, which can then suggest the optimal reservation date and time.

[0036] The conversation unit can engage in natural conversation with an employee avatar displayed on the screen and take drink orders. The conversation unit can recognize the customer's voice using, for example, speech recognition technology and provide an appropriate response. The conversation unit can adjust the timing of responses to maintain a natural flow of conversation. The conversation unit can enable customers to have natural conversations with employee avatars displayed on the screen. This allows the conversation unit to engage in natural conversations with avatars on the screen and take drink orders. Some or all of the above processing in the conversation unit may be performed using, for example, generative AI, or without generative AI. For example, the conversation unit can input the customer's voice into a generative AI, which can then generate an appropriate response.

[0037] The information storage unit can record information such as the customer's name and preferred beverage, which can then be used for future visits. The information storage unit can, for example, store customer information in a database and update it as needed. The information storage unit can, for example, record the customer's purchase history and use it to provide personalized services. This allows the information storage unit to record information such as the customer's name and preferred beverage, which can then be used for future visits. Some or all of the above processing in the information storage unit may be performed using, for example, a generating AI, or without a generating AI. For example, the information storage unit can input customer information into a generating AI, which can then suggest the most suitable service.

[0038] The avatar unit can display avatars that mimic the appearance of actual employees. The avatar unit can reproduce the characteristics of actual employees, for example, using 3D models or animations. The avatar unit can mimic the voice and speaking style of actual employees, for example. As a result, the avatar unit can display avatars that mimic the appearance of actual employees. Some or all of the above-described processes in the avatar unit may be performed using, for example, a generative AI, or without a generative AI. For example, the avatar unit can input the characteristics of actual employees into a generative AI, and the generative AI can generate an avatar.

[0039] The exchange unit can suggest exchanging social media accounts and make a reservation for the next visit. For example, the exchange unit can have an avatar on the display suggest exchanging social media accounts, and the customer can make a reservation for the next visit. For example, the exchange unit can suggest a specific social media account, making it easy for the customer to exchange accounts. For example, the exchange unit can guide the customer through the social media exchange process, making it easy for the customer to exchange accounts. As a result, the exchange unit can suggest exchanging social media accounts and make a reservation for the next visit. Some or all of the above processes in the exchange unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the exchange unit can input the customer's social media account information into a generating AI, and the generating AI can suggest the optimal exchange method.

[0040] The conversation unit can refer to the customer's past conversation history during a conversation and provide relevant topics. For example, if the conversation unit discusses the customer's favorite music that they have talked about in the past, it can bring up the latest music trends. If the conversation unit discusses a travel destination that the customer has previously mentioned, it can introduce new tourist attractions in that area. If the conversation unit discusses a hobby that the customer has previously mentioned, it can suggest relevant events or activities. In this way, the conversation unit can refer to the customer's past conversation history and provide relevant topics. Some or all of the above processing in the conversation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the conversation unit can input the customer's past conversation history data into a generative AI, which can then generate relevant topics.

[0041] The conversational unit can suggest specific events or promotions based on the customer's interests during the conversation. For example, if the customer is interested in music, the conversational unit can suggest live events held in the store. For example, if the customer is interested in wine, the conversational unit can suggest a special wine tasting event. For example, if the customer is interested in dancing, the conversational unit can suggest a dance party. In this way, the conversational unit can suggest specific events or promotions based on the customer's interests. Some or all of the above processing in the conversational unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the conversational unit can input customer interest data into a generative AI, which can then suggest the most suitable events or promotions.

[0042] The conversational unit can take the customer's geographical location into consideration during the conversation and provide topics relevant to the region. For example, if the customer is talking about local events, the conversational unit can provide information about nearby events. If the customer is talking about a travel destination, the conversational unit can introduce tourist attractions in that area. If the customer is talking about local restaurants, the conversational unit can suggest popular restaurants. In this way, the conversational unit can provide topics relevant to the region, taking the customer's geographical location into consideration. Some or all of the above processing in the conversational unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the conversational unit can input the customer's geographical location data into a generative AI, which can then generate topics relevant to the region.

[0043] The conversation unit can analyze the customer's social media activity during the conversation and provide relevant topics. For example, the conversation unit can provide topics related to a photo the customer recently posted. For example, the conversation unit can provide topics related to an article the customer shared on social media. For example, the conversation unit can provide topics related to accounts the customer follows on social media. In this way, the conversation unit can analyze the customer's social media activity and provide relevant topics. Some or all of the above processing in the conversation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the conversation unit can input the customer's social media activity data into a generative AI, and the generative AI can generate relevant topics.

[0044] The information storage unit can refer to the customer's past visit history when storing information and prioritize recording relevant information. For example, the information storage unit can prioritize recording information about drinks the customer has ordered in the past. For example, the information storage unit can prioritize recording information about the date and time of the customer's past visits. For example, the information storage unit can prioritize recording information about services the customer has used in the past. This allows the information storage unit to refer to the customer's past visit history and prioritize recording relevant information. Some or all of the above processing in the information storage unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the information storage unit can input the customer's past visit history data into a generating AI, which can then prioritize recording relevant information.

[0045] The information storage unit can record customer preferences and allergy information in detail when storing information. For example, the information storage unit can record in detail the types and brands of drinks that the customer likes. For example, the information storage unit can record in detail any foods or drinks that the customer is allergic to. For example, the information storage unit can record in detail the types of services and entertainment that the customer prefers. This allows the information storage unit to record customer preferences and allergy information in detail. Some or all of the above processing in the information storage unit may be performed using, for example, a generating AI, or without a generating AI. For example, the information storage unit can input customer preferences and allergy information into a generating AI, which can then suggest the most suitable service.

[0046] The information storage unit can record relevant information while considering the customer's geographical location information. For example, the information storage unit can record information about places the customer has visited. For example, the information storage unit can record event information related to places the customer has visited. For example, the information storage unit can record service information related to places the customer has visited. In this way, the information storage unit can record relevant information while considering the customer's geographical location information. Some or all of the above processing in the information storage unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the information storage unit can input the customer's geographical location information data into a generating AI, and the generating AI can record relevant information.

[0047] The information storage unit can analyze the customer's social media activity and record relevant information when storing information. For example, the information storage unit can record information that the customer has shared on social media. For example, the information storage unit can record information related to accounts that the customer follows on social media. For example, the information storage unit can record information related to photos and videos that the customer has posted on social media. In this way, the information storage unit can analyze the customer's social media activity and record relevant information. Some or all of the above processing in the information storage unit may be performed using, for example, a generative AI, or without a generative AI. For example, the information storage unit can input the customer's social media activity data into a generative AI, and the generative AI can record relevant information.

[0048] The avatar unit can accurately reflect the characteristics of actual employees when displaying avatars. For example, the avatar unit can accurately reflect the hairstyle and clothing of actual employees. For example, the avatar unit can accurately reflect the voice and speaking style of actual employees. For example, the avatar unit can accurately reflect the movements and gestures of actual employees. In this way, the avatar unit can accurately reflect the characteristics of actual employees. Some or all of the above processing in the avatar unit may be performed using, for example, a generation AI, or without a generation AI. For example, the avatar unit can input actual employee characteristic data into a generation AI, and the generation AI can generate an avatar.

[0049] The avatar unit can customize the appearance of the avatar according to the user's preferences when the avatar is displayed. For example, the avatar unit can allow the user to select their preferred hairstyle and clothing. For example, the avatar unit can allow the user to select their preferred voice and speaking style. For example, the avatar unit can allow the user to select their preferred actions and gestures. In this way, the avatar unit can customize the appearance of the avatar according to the user's preferences. Some or all of the above processing in the avatar unit may be performed using, for example, a generation AI, or without a generation AI. For example, the avatar unit can input the user's preference data into a generation AI, which can then customize the appearance of the avatar.

[0050] The avatar unit can dress the avatar in region-related clothing, taking into account the customer's geographical location information when displaying the avatar. For example, the avatar unit can dress the avatar in traditional clothing of the region the customer is visiting. For example, the avatar unit can dress the avatar in clothing related to an event in the region the customer is visiting. For example, the avatar unit can dress the avatar in clothing appropriate for the season in the region the customer is visiting. In this way, the avatar unit can dress the avatar in region-related clothing, taking into account the customer's geographical location information. Some or all of the above processing in the avatar unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the avatar unit can input the customer's geographical location information data into a generating AI, and the generating AI can generate region-related clothing.

[0051] The avatar function can analyze the customer's social media activity when displaying the avatar and reflect relevant appearances. For example, the avatar function can reflect the fashion styles the customer has shared on social media in the avatar. For example, the avatar function can reflect the fashion brands the customer follows on social media in the avatar. For example, the avatar function can customize the avatar's appearance based on photos the customer has posted on social media. This allows the avatar function to analyze the customer's social media activity and reflect relevant appearances. Some or all of the above processing in the avatar function may be performed using, for example, a generative AI, or without a generative AI. For example, the avatar function can input the customer's social media activity data into a generative AI, which can then generate relevant appearances.

[0052] The exchange unit can refer to the customer's past interaction history when exchanging SNS information and make relevant suggestions. For example, the exchange unit can suggest SNS accounts of employees the customer has interacted with in the past. For example, the exchange unit can suggest SNS accounts related to events the customer has participated in in the past. For example, the exchange unit can suggest SNS accounts related to topics the customer has shown interest in in the past. In this way, the exchange unit can refer to the customer's past interaction history and make relevant suggestions. Some or all of the above processing in the exchange unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the exchange unit can input the customer's past interaction history data into a generative AI, and the generative AI can generate relevant suggestions.

[0053] The exchange unit can suggest specific social media accounts based on the customer's interests during social media exchange. For example, if the customer is interested in music, the exchange unit can suggest music-related social media accounts. For example, if the customer is interested in travel, the exchange unit can suggest travel-related social media accounts. For example, if the customer is interested in cooking, the exchange unit can suggest cooking-related social media accounts. In this way, the exchange unit can suggest specific social media accounts based on the customer's interests. Some or all of the above processing in the exchange unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the exchange unit can input customer interest data into a generative AI, and the generative AI can suggest the most suitable social media accounts.

[0054] The exchange unit can suggest accounts relevant to the customer's region, taking into account their geographical location information, when exchanging social media accounts. For example, the exchange unit can suggest popular accounts in the region the customer is visiting. For example, the exchange unit can suggest event accounts in the region the customer is visiting. For example, the exchange unit can suggest tourist spot accounts in the region the customer is visiting. In this way, the exchange unit can suggest accounts relevant to the region, taking into account the customer's geographical location information. Some or all of the above processing in the exchange unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the exchange unit can input the customer's geographical location data into a generative AI, and the generative AI can suggest accounts relevant to the region.

[0055] The exchange unit can analyze the customer's social media activity during SNS exchange and suggest relevant accounts. For example, the exchange unit can suggest accounts related to accounts the customer follows on social media. For example, the exchange unit can suggest accounts related to information the customer has shared on social media. For example, the exchange unit can suggest accounts related to content the customer has posted on social media. In this way, the exchange unit can analyze the customer's social media activity and suggest relevant accounts. Some or all of the above processing in the exchange unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the exchange unit can input the customer's social media activity data into a generative AI, and the generative AI can suggest relevant accounts.

[0056] The Customer Visit Promotion Department can refer to a customer's past visit history when promoting visits and suggest relevant promotions. For example, the Customer Visit Promotion Department can suggest promotions related to services the customer has used in the past. For example, the Customer Visit Promotion Department can suggest promotions related to the date and time the customer visited in the past. For example, the Customer Visit Promotion Department can suggest promotions related to drinks the customer ordered in the past. In this way, the Customer Visit Promotion Department can refer to a customer's past visit history and suggest relevant promotions. Some or all of the above processing in the Customer Visit Promotion Department may be performed using, for example, a generating AI, or not using a generating AI. For example, the Customer Visit Promotion Department can input the customer's past visit history data into a generating AI, and the generating AI can suggest relevant promotions.

[0057] The Customer Acquisition Department can propose region-related promotions while considering the customer's geographical location information during the customer acquisition process. For example, the Customer Acquisition Department can propose event promotions in the area the customer is visiting. For example, the Customer Acquisition Department can propose special discounts in the area the customer is visiting. For example, the Customer Acquisition Department can propose new menu items in the area the customer is visiting. In this way, the Customer Acquisition Department can propose region-related promotions while considering the customer's geographical location information. Some or all of the above processing in the Customer Acquisition Department may be performed using, for example, a generative AI, or not using a generative AI. For example, the Customer Acquisition Department can input the customer's geographical location data into a generative AI, and the generative AI can propose region-related promotions.

[0058] The reservation department can refer to a customer's past reservation history when a reservation is made and make relevant suggestions. For example, the reservation department can make suggestions related to the date and time the customer has made reservations in the past. For example, the reservation department can make suggestions related to the services the customer has used in the past. For example, the reservation department can make suggestions related to the drinks the customer has ordered in the past. In this way, the reservation department can refer to a customer's past reservation history and make relevant suggestions. Some or all of the above processing in the reservation department may be performed using, for example, a generative AI, or not using a generative AI. For example, the reservation department can input the customer's past reservation history data into a generative AI, and the generative AI can generate relevant suggestions.

[0059] The reservation department can make region-related suggestions by taking into account the customer's geographical location information when a reservation is made. For example, the reservation department can suggest event reservations in the area the customer is visiting. For example, the reservation department can suggest special discount reservations in the area the customer is visiting. For example, the reservation department can suggest new menu reservations in the area the customer is visiting. In this way, the reservation department can make region-related suggestions by taking into account the customer's geographical location information. Some or all of the above processing in the reservation department may be performed using, for example, a generative AI, or not using a generative AI. For example, the reservation department can input the customer's geographical location information data into a generative AI, and the generative AI can generate region-related suggestions.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The system may also include a promotions department that references customers' past visit history and provides relevant promotions and benefits. For example, the promotions department could offer discounts related to services previously used by the customer. It could also offer benefits related to the date and time of past visits. Furthermore, it could offer coupons related to beverages previously ordered by the customer. This allows the promotions department to reference customers' past visit history and provide relevant promotions and benefits.

[0062] The system can also include a regional suggestion section that proposes events and services relevant to the customer's area, taking into account their geographical location. For example, the regional suggestion section can provide event information in the area the customer is visiting. It can also introduce tourist attractions in the area the customer is visiting. Furthermore, it can suggest restaurants and cafes in the area the customer is visiting. This allows the regional suggestion section to propose events and services relevant to the area, taking into account the customer's geographical location.

[0063] The system can also include a social suggestion unit that analyzes the customer's social media activity and proposes relevant events and services. For example, the social suggestion unit can propose events related to information the customer has shared on social media. It can also propose services related to accounts the customer follows on social media. Furthermore, it can propose activities related to content the customer has posted on social media. This allows the social suggestion unit to analyze the customer's social media activity and propose relevant events and services.

[0064] The system can also include a preference record section that records customer preferences and allergy information in detail for use on subsequent visits. For example, the preference record section can record the types and brands of beverages a customer likes. It can also record any foods or beverages a customer is allergic to. Furthermore, it can record the types of services and entertainment a customer prefers. This allows the preference record section to record customer preferences and allergy information in detail, which can then be used on subsequent visits.

[0065] The system may also include an SNS suggestion unit that refers to the customer's past interaction history and suggests relevant SNS accounts. For example, the SNS suggestion unit could suggest SNS accounts of employees the customer has interacted with in the past. It could also suggest SNS accounts related to events the customer has attended in the past. Furthermore, it could suggest SNS accounts related to topics the customer has shown interest in in the past. This allows the SNS suggestion unit to refer to the customer's past interaction history and suggest relevant SNS accounts.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The conversation unit enables natural voice conversation. The conversation unit uses speech recognition technology to recognize the customer's voice and provide an appropriate response. Furthermore, it can adjust the timing of responses to maintain a natural flow of conversation. In addition, customers can have natural conversations with employee avatars displayed on the screen. Step 2: The information storage unit stores customer information. The information storage unit records information such as the customer's name and preferred beverage, which can be used for future visits. Furthermore, customer information can be stored in a database and updated as needed. In addition, customer purchase history can be recorded and used to provide personalized services. Step 3: The avatar section utilizes avatars of actual employees. The avatar section can display avatars that mimic the appearance of actual employees. Furthermore, it can reproduce the characteristics of actual employees using 3D models and animations. It can also mimic the voice and speaking style of actual employees. Step 4: The exchange unit facilitates the exchange of social media accounts. The exchange unit's on-screen avatar suggests exchanging social media accounts, allowing customers to make appointments for their next visit. Furthermore, it can suggest specific social media accounts to make the exchange process easier for customers. It can also guide customers through the social media exchange process to ensure a smooth exchange.

[0068] (Example of form 2) The system in a nightclub or lounge according to an embodiment of the present invention is a system that increases the number of customers entering during the early hours and reduces the cost burden on employees. This system uses an AI agent to enable natural voice conversation. The system allows customers to have natural conversations with employee avatars displayed on a screen and order drinks and other items. Furthermore, it provides a mechanism to retain customer information and encourage future visits. It uses avatars of actual employees, providing the value of being able to meet real employees through avatars. The system also allows for the exchange of SNS information upon arrival, enabling natural interaction with avatars and even reservations for future visits. For example, the AI ​​agent enables natural voice conversation. Customers can converse with employee avatars displayed on a screen and order drinks. In this case, the AI ​​agent recognizes the customer's voice and provides appropriate responses. This makes it possible to provide an environment where customers can enjoy themselves even during the early hours. Next, it provides a mechanism to retain customer information and encourage future visits. For example, the AI ​​agent records information such as the customer's name and preferred drinks, which can be used on their next visit. This makes it possible to provide personalized services to customers. Furthermore, the system utilizes avatars of actual employees, offering the added value of allowing customers to "meet" real employees through the avatars. For example, an avatar on the display mimics the appearance of a real employee, allowing customers to meet that employee on their next visit. This provides customers with a special experience. The system also allows for the exchange of social media information upon arrival, enabling natural interaction with the avatar and even booking future visits. For example, the avatar on the display might suggest exchanging social media information, allowing customers to book their next visit. This can improve customer retention rates. In this way, utilizing AI agents can increase the number of customers entering nightclubs and lounges during earlier hours, reducing the cost burden on employees.

[0069] The system according to this embodiment comprises a conversation unit, an information storage unit, an avatar unit, and an exchange unit. The conversation unit enables natural voice conversation. The conversation unit recognizes the customer's voice using, for example, speech recognition technology and provides an appropriate response. The conversation unit can adjust the timing of responses to maintain a natural flow of conversation. The conversation unit can enable customers to have natural conversations with employee avatars displayed on a screen. The information storage unit stores customer information. The information storage unit can record information such as the customer's name and preferred drinks, which can be used for future visits. The information storage unit can store customer information in a database and update it as needed. The information storage unit can record customer purchase history, which can be used to provide personalized services. The avatar unit utilizes avatars of actual employees. The avatar unit can display avatars that mimic the appearance of actual employees. The avatar unit can reproduce the characteristics of actual employees using, for example, 3D models or animations. The avatar unit can mimic the voice and speaking style of actual employees. The exchange unit handles the exchange of social networking services (SNS). For example, the exchange unit can have an avatar on the display suggest the exchange of SNS, allowing the customer to make a reservation for their next visit. The exchange unit can also suggest specific SNS accounts, making it easy for the customer to exchange them. For example, the exchange unit can guide the customer through the SNS exchange process, ensuring a smooth exchange. As a result, the system according to this embodiment can achieve natural voice conversations, retain customer information, and use avatars of actual employees to exchange SNS information.

[0070] The conversation unit enables natural voice conversations. For example, it uses speech recognition technology to recognize the customer's voice and provide an appropriate response. Specifically, the speech recognition technology utilizes a deep learning-based voice model to convert the customer's utterances into text with high accuracy. This text data is analyzed using natural language processing (NLP) technology to understand the customer's intent and the content of their questions. Next, the conversation unit generates an appropriate response using a pre-prepared response database and generative AI. The generative AI can, for example, generate natural and fluent responses while considering the context of the conversation. To adjust the timing of responses, the conversation unit monitors the flow of the conversation and the customer's reactions in real time to maintain appropriate pauses. Furthermore, the conversation unit synchronizes the facial expressions and movements of the employee avatar displayed on the screen so that the customer can have a natural conversation with the avatar. This allows the customer to have an experience as if they were talking to a real employee. The conversation unit can also support multiple languages ​​and can accommodate customers who speak different languages. This allows the conversational unit to utilize speech recognition and natural language processing technologies to achieve natural conversations with customers and provide a superior user experience.

[0071] The information retention unit stores customer information. For example, it records information such as the customer's name and preferred beverages, which can then be used for future visits. Specifically, the information retention unit efficiently manages customer information using a database system. When a customer visits for the first time, information collected through the conversation unit is sent to the information retention unit and stored in the database. This includes the customer's name, contact information, preferred beverages and food, and allergy information. On subsequent visits, the information retention unit can quickly retrieve this information and provide it to the conversation unit and avatar unit, enabling personalized service. Furthermore, the information retention unit records the customer's purchase history and can provide recommendations based on this. For example, it can suggest new products and services based on previously purchased items and services. The information retention unit also prioritizes data security, implementing encryption technology and access control to thoroughly protect personal information. This allows the information retention unit to safely and efficiently manage customer information and provide a foundation for delivering personalized services.

[0072] The avatar function utilizes avatars of actual employees. For example, it can display avatars that mimic the appearance of real employees. Specifically, the avatar function uses 3D modeling technology to faithfully reproduce the appearance and movements of actual employees. This includes facial expressions, body movements, and clothing. The avatars can react in real time to customer interactions and change their expressions. Furthermore, the avatar function uses speech synthesis technology to mimic the voices and speaking styles of actual employees. This technology learns the characteristics of the employee's voice and can generate natural speech. This allows customers to experience interacting with actual employees. The avatar function also uses animation technology to smooth the avatar's movements and achieve a more realistic expression. This makes the avatar's interactions with customers more natural and engaging. Additionally, the avatar function can display multiple avatars simultaneously and allows switching between avatars of different employees. This allows the avatar function to faithfully reproduce the characteristics of actual employees and provide customers with a realistic conversational experience.

[0073] The exchange unit facilitates the exchange of social media accounts. For example, an avatar on the display might suggest exchanging social media accounts, allowing customers to make appointments for their next visit. Specifically, the exchange unit manages social media account information, making it easy for customers to exchange accounts. The avatar on the display naturally suggests exchanging social media accounts during the conversation, and if the customer is interested, it guides them through the specific steps. For example, it might display a QR code, allowing customers to exchange social media accounts simply by scanning it with their smartphone. The exchange unit also provides voice and visual guidance to help customers smoothly exchange accounts and guides them through the social media exchange process. Furthermore, the exchange unit can send reminders via social media when customers make their next appointment. This ensures that customers don't forget their next visit and can make appointments smoothly. The exchange unit can also suggest specific social media accounts and provide content and information that customers might find interesting. This allows the exchange unit to strengthen communication with customers and promote an increase in repeat customers.

[0074] The system includes a customer visit promotion unit that encourages repeat visits. This unit can, for example, encourage customers to return by offering promotions or discount coupons. It can also attract customers' interest by announcing events. Furthermore, it can improve customer retention rates by offering special services or benefits. This allows the system to encourage repeat visits. Some or all of the above processes in the customer visit promotion unit may be performed using AI, or not. For example, the customer visit promotion unit can input a customer's past visit history into the AI, which can then suggest the most suitable promotions and benefits.

[0075] The system includes a reservation section for booking the next visit. The reservation section can, for example, provide an online reservation system, allowing customers to easily book their next visit. The reservation section can, for example, accept telephone reservations, allowing customers to book directly. The reservation section can, for example, provide a reservation confirmation method, allowing customers to check their reservation details. This enables the system to book the next visit. Some or all of the above processes in the reservation section may be performed using AI, for example, or not using AI. For example, the reservation section can input the customer's past reservation history into the AI, which can then suggest the optimal reservation date and time.

[0076] The conversation unit can engage in natural conversation with an employee avatar displayed on the screen and take drink orders. The conversation unit can recognize the customer's voice using, for example, speech recognition technology and provide an appropriate response. The conversation unit can adjust the timing of responses to maintain a natural flow of conversation. The conversation unit can enable customers to have natural conversations with employee avatars displayed on the screen. This allows the conversation unit to engage in natural conversations with avatars on the screen and take drink orders. Some or all of the above processing in the conversation unit may be performed using, for example, generative AI, or without generative AI. For example, the conversation unit can input the customer's voice into a generative AI, which can then generate an appropriate response.

[0077] The information storage unit can record information such as the customer's name and preferred beverage, which can then be used for future visits. The information storage unit can, for example, store customer information in a database and update it as needed. The information storage unit can, for example, record the customer's purchase history and use it to provide personalized services. This allows the information storage unit to record information such as the customer's name and preferred beverage, which can then be used for future visits. Some or all of the above processing in the information storage unit may be performed using, for example, a generating AI, or without a generating AI. For example, the information storage unit can input customer information into a generating AI, which can then suggest the most suitable service.

[0078] The avatar unit can display avatars that mimic the appearance of actual employees. The avatar unit can reproduce the characteristics of actual employees, for example, using 3D models or animations. The avatar unit can mimic the voice and speaking style of actual employees, for example. As a result, the avatar unit can display avatars that mimic the appearance of actual employees. Some or all of the above-described processes in the avatar unit may be performed using, for example, a generative AI, or without a generative AI. For example, the avatar unit can input the characteristics of actual employees into a generative AI, and the generative AI can generate an avatar.

[0079] The exchange unit can suggest exchanging social media accounts and make a reservation for the next visit. For example, the exchange unit can have an avatar on the display suggest exchanging social media accounts, and the customer can make a reservation for the next visit. For example, the exchange unit can suggest a specific social media account, making it easy for the customer to exchange accounts. For example, the exchange unit can guide the customer through the social media exchange process, making it easy for the customer to exchange accounts. As a result, the exchange unit can suggest exchanging social media accounts and make a reservation for the next visit. Some or all of the above processes in the exchange unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the exchange unit can input the customer's social media account information into a generating AI, and the generating AI can suggest the optimal exchange method.

[0080] The conversation unit can estimate the customer's emotions and adjust the tone and content of the conversation based on the estimated emotions. For example, if the customer is enjoying themselves, the conversation unit will proceed in a bright and cheerful tone. For example, if the customer is tired, the conversation unit can provide a relaxing conversation in a calm tone. For example, if the customer is excited, the conversation unit can liven up the conversation in an energetic tone. In this way, the conversation unit can adjust the tone and content of the conversation based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the conversation unit may be performed using a generative AI, or not using a generative AI. For example, the conversation unit can input customer voice and facial expression data into a generative AI, which can estimate emotions and generate an appropriate tone and content of conversation.

[0081] The conversation unit can refer to the customer's past conversation history during a conversation and provide relevant topics. For example, if the conversation unit discusses the customer's favorite music that they have talked about in the past, it can bring up the latest music trends. If the conversation unit discusses a travel destination that the customer has previously mentioned, it can introduce new tourist attractions in that area. If the conversation unit discusses a hobby that the customer has previously mentioned, it can suggest relevant events or activities. In this way, the conversation unit can refer to the customer's past conversation history and provide relevant topics. Some or all of the above processing in the conversation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the conversation unit can input the customer's past conversation history data into a generative AI, which can then generate relevant topics.

[0082] The conversational unit can suggest specific events or promotions based on the customer's interests during the conversation. For example, if the customer is interested in music, the conversational unit can suggest live events held in the store. For example, if the customer is interested in wine, the conversational unit can suggest a special wine tasting event. For example, if the customer is interested in dancing, the conversational unit can suggest a dance party. In this way, the conversational unit can suggest specific events or promotions based on the customer's interests. Some or all of the above processing in the conversational unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the conversational unit can input customer interest data into a generative AI, which can then suggest the most suitable events or promotions.

[0083] The conversation unit can estimate the customer's emotions and adjust the pace of the conversation based on those emotions. For example, if the customer is relaxed, the conversation unit will proceed at a slow pace. If the customer is in a hurry, the conversation unit can proceed quickly. If the customer is excited, the conversation unit can provide a fast-paced conversation. In this way, the conversation unit can adjust the pace of the conversation based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the conversation unit may be performed using a generative AI, or not using a generative AI. For example, the conversation unit can input customer voice and facial expression data into a generative AI, which can estimate emotions and generate an appropriate conversation pace.

[0084] The conversational unit can take the customer's geographical location into consideration during the conversation and provide topics relevant to the region. For example, if the customer is talking about local events, the conversational unit can provide information about nearby events. If the customer is talking about a travel destination, the conversational unit can introduce tourist attractions in that area. If the customer is talking about local restaurants, the conversational unit can suggest popular restaurants. In this way, the conversational unit can provide topics relevant to the region, taking the customer's geographical location into consideration. Some or all of the above processing in the conversational unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the conversational unit can input the customer's geographical location data into a generative AI, which can then generate topics relevant to the region.

[0085] The conversation unit can analyze the customer's social media activity during the conversation and provide relevant topics. For example, the conversation unit can provide topics related to a photo the customer recently posted. For example, the conversation unit can provide topics related to an article the customer shared on social media. For example, the conversation unit can provide topics related to accounts the customer follows on social media. In this way, the conversation unit can analyze the customer's social media activity and provide relevant topics. Some or all of the above processing in the conversation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the conversation unit can input the customer's social media activity data into a generative AI, and the generative AI can generate relevant topics.

[0086] The information storage unit can estimate the customer's emotions and adjust the method of recording information based on the estimated emotions. For example, if the customer is relaxed, the information storage unit can record detailed information. For example, if the customer is in a hurry, the information storage unit can record only the minimum necessary information. For example, if the customer is excited, the information storage unit can highlight and record important information. In this way, the information storage unit can adjust the method of recording information based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information storage unit may be performed using a generative AI, or not using a generative AI. For example, the information storage unit can input customer voice and facial expression data into a generative AI, which can estimate emotions and generate an appropriate method of recording information.

[0087] The information storage unit can refer to the customer's past visit history when storing information and prioritize recording relevant information. For example, the information storage unit can prioritize recording information about drinks the customer has ordered in the past. For example, the information storage unit can prioritize recording information about the date and time of the customer's past visits. For example, the information storage unit can prioritize recording information about services the customer has used in the past. This allows the information storage unit to refer to the customer's past visit history and prioritize recording relevant information. Some or all of the above processing in the information storage unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the information storage unit can input the customer's past visit history data into a generating AI, which can then prioritize recording relevant information.

[0088] The information storage unit can record customer preferences and allergy information in detail when storing information. For example, the information storage unit can record in detail the types and brands of drinks that the customer likes. For example, the information storage unit can record in detail any foods or drinks that the customer is allergic to. For example, the information storage unit can record in detail the types of services and entertainment that the customer prefers. This allows the information storage unit to record customer preferences and allergy information in detail. Some or all of the above processing in the information storage unit may be performed using, for example, a generating AI, or without a generating AI. For example, the information storage unit can input customer preferences and allergy information into a generating AI, which can then suggest the most suitable service.

[0089] The information storage unit can estimate the customer's emotions and adjust the way information is displayed based on the estimated emotions. For example, if the customer is relaxed, the information storage unit can display detailed information. For example, if the customer is in a hurry, the information storage unit can display concise information. For example, if the customer is excited, the information storage unit can provide a visually stimulating display method. In this way, the information storage unit can adjust the way information is displayed based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the information storage unit may be performed using a generative AI, or not using a generative AI. For example, the information storage unit can input customer voice and facial expression data into a generative AI, which can estimate emotions and generate an appropriate way to display information.

[0090] The information storage unit can record relevant information while considering the customer's geographical location information. For example, the information storage unit can record information about places the customer has visited. For example, the information storage unit can record event information related to places the customer has visited. For example, the information storage unit can record service information related to places the customer has visited. In this way, the information storage unit can record relevant information while considering the customer's geographical location information. Some or all of the above processing in the information storage unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the information storage unit can input the customer's geographical location information data into a generating AI, and the generating AI can record relevant information.

[0091] The information storage unit can analyze the customer's social media activity and record relevant information when storing information. For example, the information storage unit can record information that the customer has shared on social media. For example, the information storage unit can record information related to accounts that the customer follows on social media. For example, the information storage unit can record information related to photos and videos that the customer has posted on social media. In this way, the information storage unit can analyze the customer's social media activity and record relevant information. Some or all of the above processing in the information storage unit may be performed using, for example, a generative AI, or without a generative AI. For example, the information storage unit can input the customer's social media activity data into a generative AI, and the generative AI can record relevant information.

[0092] The avatar unit can estimate the customer's emotions and adjust the avatar's facial expressions and actions based on the estimated emotions. For example, if the customer is having fun, the avatar unit can display a smiling and cheerful expression. For example, if the customer is tired, the avatar unit can display a calm expression. For example, if the customer is excited, the avatar unit can display energetic actions. In this way, the avatar unit can adjust the avatar's facial expressions and actions based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the avatar unit may be performed using a generative AI, or not using a generative AI. For example, the avatar unit can input customer voice and facial expression data into a generative AI, which can estimate emotions and generate appropriate facial expressions and actions.

[0093] The avatar unit can accurately reflect the characteristics of actual employees when displaying avatars. For example, the avatar unit can accurately reflect the hairstyle and clothing of actual employees. For example, the avatar unit can accurately reflect the voice and speaking style of actual employees. For example, the avatar unit can accurately reflect the movements and gestures of actual employees. In this way, the avatar unit can accurately reflect the characteristics of actual employees. Some or all of the above processing in the avatar unit may be performed using, for example, a generation AI, or without a generation AI. For example, the avatar unit can input actual employee characteristic data into a generation AI, and the generation AI can generate an avatar.

[0094] The avatar unit can customize the appearance of the avatar according to the user's preferences when the avatar is displayed. For example, the avatar unit can allow the user to select their preferred hairstyle and clothing. For example, the avatar unit can allow the user to select their preferred voice and speaking style. For example, the avatar unit can allow the user to select their preferred actions and gestures. In this way, the avatar unit can customize the appearance of the avatar according to the user's preferences. Some or all of the above processing in the avatar unit may be performed using, for example, a generation AI, or without a generation AI. For example, the avatar unit can input the user's preference data into a generation AI, which can then customize the appearance of the avatar.

[0095] The avatar unit can estimate the customer's emotions and adjust the tone of the avatar's voice based on the estimated emotions. For example, if the customer is relaxed, the avatar unit will speak in a calm tone of voice. For example, if the customer is in a hurry, the avatar unit can speak in a quick and concise tone of voice. For example, if the customer is excited, the avatar unit can speak in an energetic tone of voice. In this way, the avatar unit can adjust the tone of the avatar's voice based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the avatar unit may be performed using a generative AI, or not using a generative AI. For example, the avatar unit can input customer voice and facial expression data into a generative AI, which can estimate emotions and generate an appropriate tone of voice.

[0096] The avatar unit can dress the avatar in region-related clothing, taking into account the customer's geographical location information when displaying the avatar. For example, the avatar unit can dress the avatar in traditional clothing of the region the customer is visiting. For example, the avatar unit can dress the avatar in clothing related to an event in the region the customer is visiting. For example, the avatar unit can dress the avatar in clothing appropriate for the season in the region the customer is visiting. In this way, the avatar unit can dress the avatar in region-related clothing, taking into account the customer's geographical location information. Some or all of the above processing in the avatar unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the avatar unit can input the customer's geographical location information data into a generating AI, and the generating AI can generate region-related clothing.

[0097] The avatar function can analyze the customer's social media activity when displaying the avatar and reflect relevant appearances. For example, the avatar function can reflect the fashion styles the customer has shared on social media in the avatar. For example, the avatar function can reflect the fashion brands the customer follows on social media in the avatar. For example, the avatar function can customize the avatar's appearance based on photos the customer has posted on social media. This allows the avatar function to analyze the customer's social media activity and reflect relevant appearances. Some or all of the above processing in the avatar function may be performed using, for example, a generative AI, or without a generative AI. For example, the avatar function can input the customer's social media activity data into a generative AI, which can then generate relevant appearances.

[0098] The exchange unit can estimate the customer's emotions and adjust its SNS exchange suggestion method based on the estimated emotions. For example, if the customer is relaxed, the exchange unit can suggest an SNS exchange in a calm tone. If the customer is excited, the exchange unit can suggest an SNS exchange in an energetic tone. If the customer is in a hurry, the exchange unit can suggest an SNS exchange quickly and concisely. In this way, the exchange unit can adjust its SNS exchange suggestion method based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the exchange unit may be performed using a generative AI, or not using a generative AI. For example, the exchange unit can input customer voice and facial expression data into a generative AI, which can estimate emotions and generate an appropriate SNS exchange suggestion method.

[0099] The exchange unit can refer to the customer's past interaction history when exchanging SNS information and make relevant suggestions. For example, the exchange unit can suggest SNS accounts of employees the customer has interacted with in the past. For example, the exchange unit can suggest SNS accounts related to events the customer has participated in in the past. For example, the exchange unit can suggest SNS accounts related to topics the customer has shown interest in in the past. In this way, the exchange unit can refer to the customer's past interaction history and make relevant suggestions. Some or all of the above processing in the exchange unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the exchange unit can input the customer's past interaction history data into a generative AI, and the generative AI can generate relevant suggestions.

[0100] The exchange unit can suggest specific social media accounts based on the customer's interests during social media exchange. For example, if the customer is interested in music, the exchange unit can suggest music-related social media accounts. For example, if the customer is interested in travel, the exchange unit can suggest travel-related social media accounts. For example, if the customer is interested in cooking, the exchange unit can suggest cooking-related social media accounts. In this way, the exchange unit can suggest specific social media accounts based on the customer's interests. Some or all of the above processing in the exchange unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the exchange unit can input customer interest data into a generative AI, and the generative AI can suggest the most suitable social media accounts.

[0101] The exchange unit can estimate the customer's emotions and adjust the timing of SNS exchanges based on the estimated emotions. For example, if the customer is relaxed, the exchange unit may suggest an SNS exchange towards the end of the conversation. If the customer is excited, the exchange unit may suggest an SNS exchange in line with the excitement of the conversation. If the customer is in a hurry, the exchange unit may suggest an SNS exchange at the beginning of the conversation. In this way, the exchange unit can adjust the timing of SNS exchanges based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the exchange unit may be performed using a generative AI, or not using a generative AI. For example, the exchange unit can input customer voice and facial expression data into a generative AI, which can estimate emotions and generate an appropriate timing for an SNS exchange.

[0102] The exchange unit can suggest accounts relevant to the customer's region, taking into account their geographical location information, when exchanging social media accounts. For example, the exchange unit can suggest popular accounts in the region the customer is visiting. For example, the exchange unit can suggest event accounts in the region the customer is visiting. For example, the exchange unit can suggest tourist spot accounts in the region the customer is visiting. In this way, the exchange unit can suggest accounts relevant to the region, taking into account the customer's geographical location information. Some or all of the above processing in the exchange unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the exchange unit can input the customer's geographical location data into a generative AI, and the generative AI can suggest accounts relevant to the region.

[0103] The exchange unit can analyze the customer's social media activity during SNS exchange and suggest relevant accounts. For example, the exchange unit can suggest accounts related to accounts the customer follows on social media. For example, the exchange unit can suggest accounts related to information the customer has shared on social media. For example, the exchange unit can suggest accounts related to content the customer has posted on social media. In this way, the exchange unit can analyze the customer's social media activity and suggest relevant accounts. Some or all of the above processing in the exchange unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the exchange unit can input the customer's social media activity data into a generative AI, and the generative AI can suggest relevant accounts.

[0104] The customer visit promotion unit can estimate the customer's emotions and adjust its methods of promoting visits based on those emotions. For example, if the customer is relaxed, the unit can promote visits in a calm tone. If the customer is excited, the unit can promote visits in an energetic tone. If the customer is in a hurry, the unit can promote visits quickly and concisely. This allows the customer visit promotion unit to adjust its methods of promoting visits based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customer visit promotion unit may be performed using or without a generative AI. For example, the customer visit promotion unit can input customer voice and facial expression data into a generative AI, which can estimate emotions and generate appropriate methods of promoting visits.

[0105] The Customer Visit Promotion Department can refer to a customer's past visit history when promoting visits and suggest relevant promotions. For example, the Customer Visit Promotion Department can suggest promotions related to services the customer has used in the past. For example, the Customer Visit Promotion Department can suggest promotions related to the date and time the customer visited in the past. For example, the Customer Visit Promotion Department can suggest promotions related to drinks the customer ordered in the past. In this way, the Customer Visit Promotion Department can refer to a customer's past visit history and suggest relevant promotions. Some or all of the above processing in the Customer Visit Promotion Department may be performed using, for example, a generating AI, or not using a generating AI. For example, the Customer Visit Promotion Department can input the customer's past visit history data into a generating AI, and the generating AI can suggest relevant promotions.

[0106] The customer visit promotion unit can estimate the customer's emotions and adjust the timing of customer visit promotions based on those estimated emotions. For example, if the customer is relaxed, the unit might promote a visit towards the end of the conversation. If the customer is excited, the unit might promote a visit in line with the excitement of the conversation. If the customer is in a hurry, the unit might promote a visit at the beginning of the conversation. In this way, the customer visit promotion unit can adjust the timing of customer visit promotions based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customer visit promotion unit may be performed using a generative AI, or not using a generative AI. For example, the customer visit promotion unit can input customer voice and facial expression data into a generative AI, which can estimate emotions and generate appropriate timing for customer visit promotions.

[0107] The Customer Acquisition Department can propose region-related promotions while considering the customer's geographical location information during the customer acquisition process. For example, the Customer Acquisition Department can propose event promotions in the area the customer is visiting. For example, the Customer Acquisition Department can propose special discounts in the area the customer is visiting. For example, the Customer Acquisition Department can propose new menu items in the area the customer is visiting. In this way, the Customer Acquisition Department can propose region-related promotions while considering the customer's geographical location information. Some or all of the above processing in the Customer Acquisition Department may be performed using, for example, a generative AI, or not using a generative AI. For example, the Customer Acquisition Department can input the customer's geographical location data into a generative AI, and the generative AI can propose region-related promotions.

[0108] The reservation department can estimate the customer's emotions and adjust its reservation suggestion method based on those emotions. For example, if the customer is relaxed, the reservation department can suggest a reservation in a calm tone. If the customer is excited, the reservation department can suggest a reservation in an energetic tone. If the customer is in a hurry, the reservation department can suggest a reservation quickly and concisely. This allows the reservation department to adjust its reservation suggestion method based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reservation department may be performed using generative AI, or not. For example, the reservation department can input customer voice and facial expression data into a generative AI, which can estimate emotions and generate an appropriate reservation suggestion method.

[0109] The reservation department can refer to a customer's past reservation history when a reservation is made and make relevant suggestions. For example, the reservation department can make suggestions related to the date and time the customer has made reservations in the past. For example, the reservation department can make suggestions related to the services the customer has used in the past. For example, the reservation department can make suggestions related to the drinks the customer has ordered in the past. In this way, the reservation department can refer to a customer's past reservation history and make relevant suggestions. Some or all of the above processing in the reservation department may be performed using, for example, a generative AI, or not using a generative AI. For example, the reservation department can input the customer's past reservation history data into a generative AI, and the generative AI can generate relevant suggestions.

[0110] The reservation department can estimate the customer's emotions and adjust the timing of reservations based on those emotions. For example, if the customer is relaxed, the reservation department can suggest a reservation towards the end of the conversation. If the customer is excited, the reservation department can suggest a reservation as the conversation is building up. If the customer is in a hurry, the reservation department can suggest a reservation at the beginning of the conversation. In this way, the reservation department can adjust the timing of reservations based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reservation department may be performed using generative AI, or not using generative AI. For example, the reservation department can input customer voice and facial expression data into a generative AI, which can estimate emotions and generate appropriate reservation timings.

[0111] The reservation department can make region-related suggestions by taking into account the customer's geographical location information when a reservation is made. For example, the reservation department can suggest event reservations in the area the customer is visiting. For example, the reservation department can suggest special discount reservations in the area the customer is visiting. For example, the reservation department can suggest new menu reservations in the area the customer is visiting. In this way, the reservation department can make region-related suggestions by taking into account the customer's geographical location information. Some or all of the above processing in the reservation department may be performed using, for example, a generative AI, or not using a generative AI. For example, the reservation department can input the customer's geographical location information data into a generative AI, and the generative AI can generate region-related suggestions.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The system may also include an environment adjustment unit that estimates the customer's emotions and adjusts the music and lighting based on those emotions. For example, if the customer is relaxed, the environment adjustment unit can provide calming music and soft lighting. If the customer is excited, the environment adjustment unit can provide energetic music and bright lighting. If the customer is tired, the environment adjustment unit can provide relaxing music and calming lighting. In this way, the environment adjustment unit can adjust the music and lighting based on the customer's emotions.

[0114] The system can also include a suggestion unit that estimates the customer's emotions and proposes drinks and food based on those emotions. For example, if the customer is relaxed, the suggestion unit might suggest relaxing herbal tea or a light snack. If the customer is excited, the suggestion unit might suggest an energetic cocktail or snack. If the customer is tired, the suggestion unit might suggest a refreshing juice or nutritious food. In this way, the suggestion unit can propose drinks and food based on the customer's emotions.

[0115] The system may further include a conversation adjustment unit that estimates the customer's emotions and adjusts the content of the conversation based on those emotions. For example, if the customer is relaxed, the conversation adjustment unit can offer calm topics. For example, if the customer is excited, the conversation adjustment unit can offer energetic topics. For example, if the customer is tired, the conversation adjustment unit can offer relaxing topics. In this way, the conversation adjustment unit can adjust the content of the conversation based on the customer's emotions.

[0116] The system may further include a service adjustment unit that estimates the customer's emotions and adjusts the type of service provided based on those emotions. For example, if the customer is relaxed, the service adjustment unit may provide a relaxing massage or aromatherapy. If the customer is excited, the service adjustment unit may provide an energetic dance lesson or activity. If the customer is tired, the service adjustment unit may provide a refreshing spa or relaxation service. In this way, the service adjustment unit can adjust the type of service provided based on the customer's emotions.

[0117] The system may further include a customer service adjustment unit that estimates the customer's emotions and adjusts its service attitude based on those emotions. For example, if the customer is relaxed, the customer service adjustment unit may serve the customer in a calm manner. If the customer is excited, the customer service adjustment unit may serve the customer in an energetic manner. If the customer is tired, the customer service adjustment unit may serve the customer in a calm manner. In this way, the customer service adjustment unit can adjust its service attitude based on the customer's emotions.

[0118] The system may also include a promotions department that references customers' past visit history and provides relevant promotions and benefits. For example, the promotions department could offer discounts related to services previously used by the customer. It could also offer benefits related to the date and time of past visits. Furthermore, it could offer coupons related to beverages previously ordered by the customer. This allows the promotions department to reference customers' past visit history and provide relevant promotions and benefits.

[0119] The system can also include a regional suggestion section that proposes events and services relevant to the customer's area, taking into account their geographical location. For example, the regional suggestion section can provide event information in the area the customer is visiting. It can also introduce tourist attractions in the area the customer is visiting. Furthermore, it can suggest restaurants and cafes in the area the customer is visiting. This allows the regional suggestion section to propose events and services relevant to the area, taking into account the customer's geographical location.

[0120] The system can also include a social suggestion unit that analyzes the customer's social media activity and proposes relevant events and services. For example, the social suggestion unit can propose events related to information the customer has shared on social media. It can also propose services related to accounts the customer follows on social media. Furthermore, it can propose activities related to content the customer has posted on social media. This allows the social suggestion unit to analyze the customer's social media activity and propose relevant events and services.

[0121] The system can also include a preference record section that records customer preferences and allergy information in detail for use on subsequent visits. For example, the preference record section can record the types and brands of beverages a customer likes. It can also record any foods or beverages a customer is allergic to. Furthermore, it can record the types of services and entertainment a customer prefers. This allows the preference record section to record customer preferences and allergy information in detail, which can then be used on subsequent visits.

[0122] The system may also include an SNS suggestion unit that refers to the customer's past interaction history and suggests relevant SNS accounts. For example, the SNS suggestion unit could suggest SNS accounts of employees the customer has interacted with in the past. It could also suggest SNS accounts related to events the customer has attended in the past. Furthermore, it could suggest SNS accounts related to topics the customer has shown interest in in the past. This allows the SNS suggestion unit to refer to the customer's past interaction history and suggest relevant SNS accounts.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The conversation unit enables natural voice conversation. The conversation unit uses speech recognition technology to recognize the customer's voice and provide an appropriate response. Furthermore, it can adjust the timing of responses to maintain a natural flow of conversation. In addition, customers can have natural conversations with employee avatars displayed on the screen. Step 2: The information storage unit stores customer information. The information storage unit records information such as the customer's name and preferred beverage, which can be used for future visits. Furthermore, customer information can be stored in a database and updated as needed. In addition, customer purchase history can be recorded and used to provide personalized services. Step 3: The avatar section utilizes avatars of actual employees. The avatar section can display avatars that mimic the appearance of actual employees. Furthermore, it can reproduce the characteristics of actual employees using 3D models and animations. It can also mimic the voice and speaking style of actual employees. Step 4: The exchange unit facilitates the exchange of social media accounts. The exchange unit's on-screen avatar suggests exchanging social media accounts, allowing customers to make appointments for their next visit. Furthermore, it can suggest specific social media accounts to make the exchange process easier for customers. It can also guide customers through the social media exchange process to ensure a smooth exchange.

[0125] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0126] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0127] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0128] Each of the multiple elements described above, including the conversation unit, information storage unit, avatar unit, exchange unit, customer visit promotion unit, and reservation unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the conversation unit is implemented by the processor 46 of the smart device 14, which recognizes the customer's voice using speech recognition technology and provides an appropriate response. The information storage unit stores customer information in the database 24 of the data processing unit 12 and uses it for the next visit. The avatar unit displays an avatar of an actual employee on the display 40A of the smart device 14. The exchange unit, via the control unit 46A of the smart device 14, suggests exchanging SNS information and makes a reservation for the next visit. The customer visit promotion unit, via the specific processing unit 290 of the data processing unit 12, provides promotions and discount coupons. The reservation unit, via the control unit 46A of the smart device 14, provides an online reservation system, making it easy to make a reservation for the next visit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the conversation unit, information storage unit, avatar unit, exchange unit, customer visit promotion unit, and reservation unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the conversation unit is implemented by the processor 46 of the smart glasses 214, which recognizes the customer's voice using speech recognition technology and provides an appropriate response. The information storage unit stores customer information in the database 24 of the data processing unit 12 and uses it for the next visit. The avatar unit displays an avatar of an actual employee on the display of the smart glasses 214. The exchange unit, via the control unit 46A of the smart glasses 214, suggests exchanging SNS information and makes a reservation for the next visit. The customer visit promotion unit, via the specific processing unit 290 of the data processing unit 12, provides promotions and discount coupons. The reservation unit, via the control unit 46A of the smart glasses 214, provides an online reservation system, making it easy to make a reservation for the next visit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the conversation unit, information storage unit, avatar unit, exchange unit, customer visit promotion unit, and reservation unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the conversation unit is implemented by the processor 46 of the headset terminal 314, which recognizes the customer's voice using speech recognition technology and provides an appropriate response. The information storage unit stores customer information in the database 24 of the data processing unit 12 and uses it for the next visit. The avatar unit displays an avatar of an actual employee on the display 343 of the headset terminal 314. The exchange unit, via the control unit 46A of the headset terminal 314, proposes SNS exchange and makes a reservation for the next visit. The customer visit promotion unit, via the specific processing unit 290 of the data processing unit 12, provides promotions and discount coupons. The reservation unit, via the control unit 46A of the headset terminal 314, provides an online reservation system, making it easy to make a reservation for the next visit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] As shown in Figure 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.

[0163] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0165] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0167] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0168] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0169] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0170] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0171] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0172] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0173] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0174] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0175] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0176] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] Each of the multiple elements described above, including the conversation unit, information storage unit, avatar unit, exchange unit, customer visit promotion unit, and reservation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the conversation unit is implemented by the processor 46 of the robot 414, which recognizes the customer's voice using speech recognition technology and provides an appropriate response. The information storage unit stores customer information in the database 24 of the data processing unit 12 and uses it for the next visit. The avatar unit displays an avatar of an actual employee on the display of the robot 414. The exchange unit, via the control unit 46A of the robot 414, suggests exchanging SNS information and makes a reservation for the next visit. The customer visit promotion unit, via the specific processing unit 290 of the data processing unit 12, provides promotions and discount coupons. The reservation unit, via the control unit 46A of the robot 414, provides an online reservation system, making it easy to make a reservation for the next visit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0178] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0180] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0181] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0182] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0186] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0187] 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.

[0188] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0189] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0190] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0191] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0193] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0194] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0195] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0196] (Note 1) A conversation unit that enables natural voice conversation, An information storage unit that holds customer information, The avatar department uses avatars of actual employees, It comprises an exchange unit for exchanging SNS information. A system characterized by the following features. (Note 2) We have a customer retention department to encourage repeat visits. The system described in Appendix 1, characterized by the features described herein. (Note 3) A reservation department is available to take reservations for your next visit. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned conversation section is, Customers can engage in natural conversations with employee avatars displayed on a screen and take drink orders. The system described in Appendix 1, characterized by the features described herein. (Note 5) The information holding unit is We record customer information such as their name and preferred drinks, and use this information for their next visit. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned avatar section is, Display avatars that mimic the appearance of actual employees. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned replacement part is Suggest exchanging social media contact information and scheduling a follow-up appointment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned conversation section is, We estimate the customer's emotions and adjust the tone and content of the conversation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned conversation section is, During the conversation, we refer to the customer's past conversation history and provide relevant topics. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned conversation section is, Based on the customer's interests and preferences during the conversation, we suggest specific events and promotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned conversation section is, The system estimates the customer's emotions and adjusts the pace of the conversation based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned conversation section is, During the conversation, we take the customer's geographical location into consideration and provide topics relevant to their area. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned conversation section is, We analyze the customer's social media activity during the conversation and provide relevant topics. The system described in Appendix 1, characterized by the features described herein. (Note 14) The information holding unit is We estimate the customer's emotions and adjust how we record information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The information holding unit is When retaining information, the system references the customer's past visit history and prioritizes recording relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The information holding unit is When storing information, the system records customer preferences and allergy information in detail. The system described in Appendix 1, characterized by the features described herein. (Note 17) The information holding unit is We estimate the customer's emotions and adjust how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The information holding unit is When storing information, the system takes the customer's geographical location into consideration and records relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The information holding unit is When retaining information, we analyze the customer's social media activity and record relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned avatar section is, The system estimates the customer's emotions and adjusts the avatar's facial expressions and movements based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned avatar section is, The avatar display accurately reflects the characteristics of actual employees. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned avatar section is, When displaying an avatar, the avatar's appearance can be customized according to the customer's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned avatar section is, The system estimates the customer's emotions and adjusts the avatar's voice tone based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned avatar section is, When displaying avatars, the system takes the customer's geographical location into consideration and dresses them in clothing relevant to their region. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned avatar section is, When displaying an avatar, the system analyzes the customer's social media activity and reflects relevant appearances. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned replacement part is We estimate the customer's emotions and adjust the way we suggest exchanging social media information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned replacement part is When exchanging social media information, we refer to the customer's past interaction history and make relevant suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned replacement part is When exchanging social media accounts, we suggest specific social media accounts based on the customer's interests. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned replacement part is We estimate the customer's emotions and adjust the timing of SNS exchanges based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned replacement part is When exchanging social media accounts, we will consider the customer's geographical location and suggest accounts relevant to their area. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned replacement part is When exchanging social media accounts, we analyze the customer's social media activity and suggest relevant accounts. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned customer visit promotion department, We estimate customer emotions and adjust our methods for encouraging store visits based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned customer visit promotion department, When encouraging customers to visit the store, we refer to their past visit history and suggest relevant promotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned customer visit promotion department, We estimate customer emotions and adjust the timing of store visit promotions based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned customer visit promotion department, When encouraging store visits, we consider the customer's geographical location and propose promotions relevant to their region. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned reservation section is, We estimate the customer's emotions and adjust the booking suggestion method based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned reservation section is, When a customer makes a reservation, we refer to their past reservation history and make relevant suggestions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned reservation section is, We estimate the customer's emotions and adjust the timing of reservations based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned reservation section is, When making a reservation, we take the customer's geographical location into consideration and provide region-related suggestions. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A conversation unit that enables natural voice conversation, An information storage unit that holds customer information, The avatar department uses avatars of actual employees, It comprises an exchange unit for exchanging SNS information. A system characterized by the following features.

2. We have a customer retention department to encourage repeat visits. The system according to feature 1.

3. A reservation department is available to take reservations for your next visit. The system according to feature 1.

4. The aforementioned conversation section is, Customers can engage in natural conversations with employee avatars displayed on a screen and take drink orders. The system according to feature 1.

5. The information holding unit is We record customer information such as their name and preferred drinks, and use this information for their next visit. The system according to feature 1.

6. The aforementioned avatar section is, Display avatars that mimic the appearance of actual employees. The system according to feature 1.

7. The aforementioned replacement part is Suggest exchanging social media contact information and scheduling a follow-up appointment. The system according to feature 1.

8. The aforementioned conversation section is, We estimate the customer's emotions and adjust the tone and content of the conversation based on those estimated emotions. The system according to feature 1.

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