system

Abstract

The system according to this embodiment aims to efficiently gather customer requests and personal information and propose appropriate responses. [Solution] The system according to the embodiment comprises a reception unit, a hearing unit, a preference hearing unit, and a proposal unit. The reception unit temporarily receives the customer's request. The hearing unit hears the customer's current personal information based on the information received by the reception unit. The preference hearing unit hears the customer's preferences based on the information heard by the hearing unit. The proposal unit analyzes the information heard by the preference hearing unit and proposes an appropriate response.

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 character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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 it is difficult to efficiently hear the requests and personal information of customers and propose appropriate responses.

[0005] The system according to the embodiment aims to efficiently hear the requests and personal information of customers and propose appropriate responses.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a hearing unit, a preference hearing unit, and a proposal unit. The reception unit initially receives the customer's request. The hearing unit hears the customer's current personal information based on the information received by the reception unit. The preference hearing unit hears the customer's preferences based on the information heard by the hearing unit. The proposal unit analyzes the information heard by the preference hearing unit and proposes an appropriate response. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently gather customer requests and personal information and propose appropriate responses. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 shopping center reception robot system according to an embodiment of the present invention is a system that shortens customer service time and alleviates labor shortages by having a robot take over reception duties at a shopping center. In this shopping center reception robot system, first, the robot temporarily receives the customer's request. Next, the robot uses an AI agent to interview the customer's current personal information. Furthermore, the robot interviews the customer's wishes. This makes human interaction smoother and reduces customer service time. Shopping centers are understaffed to meet customer demand, so by having a robot perform temporary reception duties in place of a receptionist, the time spent on customer service can be shortened. For example, the shopping center reception robot system utilizes an AI agent and targets all customers visiting the shopping center. For example, the shopping center reception robot system includes a reception unit that temporarily receives customer requests. The reception unit can, for example, temporarily receive customer requests. Next, the shopping center reception robot system includes an interview unit that interviews the customer's current personal information based on the information received by the reception unit. The interview unit can, for example, interview the customer's current personal information. Furthermore, the shopping center reception robot system includes a preference-gathering unit that gathers information from customers based on the information gathered by the hearing unit. The preference-gathering unit can, for example, gather information from customers. Finally, the shopping center reception robot system includes a proposal unit that analyzes the information gathered by the preference-gathering unit and proposes appropriate responses. The proposal unit can, for example, analyze the gathered information and propose appropriate responses. As a result, the shopping center reception robot system can efficiently receive customer requests, conduct hearings, gather preferences, and make proposals. This allows the shopping center reception robot system to reduce customer service time and alleviate labor shortages.

[0029] The shopping center reception robot system according to this embodiment comprises a reception unit, a hearing unit, a preference hearing unit, and a proposal unit. The reception unit temporarily receives customer requests. The reception unit can, for example, temporarily receive customer requests. The reception unit can, for example, receive customer requests using AI. The hearing unit hears the customer's current personal information based on the information received by the reception unit. The hearing unit can, for example, hear the customer's current personal information. The hearing unit can, for example, hear the customer's personal information using AI. The preference hearing unit hears the customer's preferences based on the information heard by the hearing unit. The preference hearing unit can, for example, hear the customer's preferences. The preference hearing unit can, for example, hear the customer's preferences using AI. The proposal unit analyzes the information heard by the preference hearing unit and proposes an appropriate response. The proposal unit can, for example, analyze the information heard and propose an appropriate response. The proposal department can, for example, use AI to suggest appropriate responses. This allows the shopping center reception robot system to efficiently receive customer requests, conduct interviews, understand their needs, and make suggestions.

[0030] The reception desk will initially receive customer requests. Specifically, the reception desk will be located at the entrance and key areas of the shopping center, and will automatically activate when a customer approaches the robot. The robot will use voice recognition technology to listen to customer requests and record them as text data. For example, if a customer says, "Please tell me about nearby restaurants," the robot will accurately recognize the request and temporarily store it in the database. Furthermore, the reception desk is multilingual and designed to accommodate foreign tourists. By using AI, the reception desk can quickly and accurately receive customer requests and can also refer to past data to utilize the history of how similar requests were handled. This allows the reception desk to efficiently process customer requests and smoothly provide information to the next step, the hearing department.

[0031] The Hearing Department gathers current personal information from customers based on the information received by the Reception Department. Specifically, the Hearing Department collects basic personal information such as the customer's name, contact information, and purpose of visit. The robot uses a voice dialogue system to engage in natural conversation with the customer and extract necessary information. For example, it might ask questions such as, "Could you tell me your name?" or "What kind of service are you looking for?" By using AI, the Hearing Department can analyze the customer's responses in real time and quickly collect the necessary information. Furthermore, from a privacy perspective, the Hearing Department manages the collected personal information appropriately and implements security measures to prevent leakage to third parties. This allows the Hearing Department to accurately and securely collect customers' personal information and smoothly provide that information to the next step, the Desired Hearing Department.

[0032] The Preferences Hearing Department gathers information about customers' preferences based on the information gathered by the General Hearing Department. Specifically, the Preferences Hearing Department collects detailed information about the specific services and products that customers are looking for. For example, it might ask questions such as, "What kind of restaurant are you looking for?" or "Are you looking for a specific brand of product?" By using AI, the Preferences Hearing Department can quickly analyze customer responses and store relevant information in a database. It can also refer to customers' past visit and purchase history to ask more personalized questions. This allows the Preferences Hearing Department to accurately understand customers' specific needs and preferences, and smoothly provide that information to the Proposal Department, which is the next step. Furthermore, the Preferences Hearing Department can collect customer feedback and provide data to continuously improve the quality of service.

[0033] The Proposal Department analyzes information gathered by the Customer Needs Assessment Department and proposes appropriate responses. Specifically, the Proposal Department uses AI to suggest the most suitable services and products based on customer preferences. For example, if a customer responds that they are "looking for an Italian restaurant," the Proposal Department will display a list of Italian restaurants within the shopping center, providing menus, ratings, and availability information for each restaurant. The Proposal Department can also refer to the customer's past visit and purchase history to provide more personalized suggestions. For example, it can suggest new restaurants based on ratings of restaurants visited in the past and preferred menu items. Furthermore, the Proposal Department can suggest the latest services and products based on real-time updated information. This allows the Proposal Department to quickly provide optimal suggestions to customers and improve their experience at the shopping center. The Proposal Department can also collect customer feedback and provide data to continuously improve the accuracy and effectiveness of its suggestions. This allows the Proposal Department to provide optimal suggestions tailored to customer needs and improve the overall service quality of the shopping center.

[0034] The shopping center reception robot system includes a recording unit that records information collected by the robot. The recording unit can, for example, record information collected by the robot. The recording unit can also record information using, for example, AI. This makes information management easier by recording the information collected by the robot. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input information collected by the robot into a generating AI and have the generating AI perform the recording of the information.

[0035] The reception area can temporarily receive customer requests using a robot. The reception area can, for example, temporarily receive customer requests using a robot. The reception area can also, for example, receive customer requests using AI. This allows for efficient handling of customer requests by using a robot. Some or all of the above-described processes in the reception area may be performed using AI, for example, or without AI. For example, the reception area can use a robot to input customer requests into a generating AI and have the generating AI handle the request reception.

[0036] The interviewing unit can use a robot to interview customers about their current personal information. The interviewing unit can, for example, use a robot to interview customers about their current personal information. The interviewing unit can also, for example, use AI to interview customers about their personal information. This allows for efficient interviewing of customers' personal information by using a robot. Some or all of the above-described processes in the interviewing unit may be performed using AI, for example, or without AI. For example, the interviewing unit can use a robot to input the customer's personal information into a generating AI and have the generating AI perform the personal information interview.

[0037] The preference gathering unit can gather customer preferences using a robot. The preference gathering unit can, for example, gather customer preferences using a robot. The preference gathering unit can also, for example, gather customer preferences using AI. This allows for efficient gathering of customer preferences by using a robot. Some or all of the above-described processes in the preference gathering unit may be performed using AI, for example, or without AI. For example, the preference gathering unit can use a robot to input customer preferences into a generating AI and have the generating AI perform the preference gathering.

[0038] The proposal unit can analyze information gathered through interviews using a robot and propose appropriate responses. For example, the proposal unit can analyze information gathered through interviews using a robot and propose appropriate responses. The proposal unit can also propose appropriate responses using AI, for example. This allows for efficient analysis of interview information and the proposal of appropriate responses through the use of a robot. Some or all of the above-described processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input information gathered through interviews using a robot into a generating AI and have the generating AI execute a proposal for an appropriate response.

[0039] The reception area can use a robot to refer to a customer's past visit history at check-in to select the most appropriate response. For example, if a customer has visited frequently in the past, the reception robot can refer to that history and provide the most appropriate response based on previous interactions. For example, if a customer is visiting for the first time, the reception robot can provide basic guidance and necessary information. For example, if a customer has a history of using a particular service, the reception area can prioritize providing information related to that service. This allows the reception area to select the most appropriate response by referring to the customer's past visit history. Some or all of the above processes in the reception area may be performed using AI, for example, or not. For example, the reception area can use a robot to input a customer's past visit history into a generating AI and have the generating AI select the most appropriate response.

[0040] The reception area can use a robot to consider the customer's current location within the shopping center when checking in. For example, if the customer is in a specific area within the shopping center, the reception area can provide information related to that area. For example, if the customer is moving around within the shopping center, the reception area can also provide optimal route guidance based on their current location. For example, if the customer is approaching a specific store, the reception area can provide promotional information about that store. This allows for more appropriate service by considering the customer's current location. Some or all of the above processing at the reception area may be performed using AI, for example, or not using AI. For example, the reception area can use a robot to input the customer's current location within the shopping center into a generating AI, and have the generating AI select the appropriate response method.

[0041] The reception area can use robots to customize how they interact with customers based on their age and gender. For example, if a customer is elderly, the reception robot can interact at a slow pace and carefully explain the necessary information. If a customer is young, the reception robot can interact in a casual tone and provide concise explanations. If a customer is female, the reception robot can provide information on services and promotions tailored to women. This allows for more appropriate service by customizing the interaction based on the customer's age and gender. Some or all of the above processes in the reception area may be performed using AI, for example, or not. For example, the reception area can use a robot to input the customer's age and gender into a generating AI and have the generating AI perform the customization of the interaction.

[0042] The reception desk can use a robot to analyze customers' social media activity at the time of check-in and provide relevant information. For example, if a customer shows interest in a particular event on social media, the reception desk can provide information about that event. For example, if a customer follows a particular brand on social media, the reception desk can also provide promotional information about that brand. For example, if a customer posts a review of a particular product on social media, the reception desk can also provide information about that product. In this way, relevant information can be provided by analyzing customers' social media activity. Some or all of the above processing at the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can use a robot to input the customer's social media activity into a generating AI and have the generating AI perform the task of providing relevant information.

[0043] The interviewing unit can use a robot to optimize the questions asked during the interview by referring to the customer's past purchase history. For example, the interviewing unit can ask questions about related products based on products the customer has purchased in the past. The interviewing unit can also ask questions about related services based on services the customer has used in the past. For example, the interviewing unit can ask questions about related events based on events the customer has participated in in the past. This allows the questions to be optimized by referring to the customer's past purchase history. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or without AI. For example, the interviewing unit can use a robot to input the customer's past purchase history into a generating AI and have the generating AI perform the optimization of the questions.

[0044] The interviewing unit can use a robot to adjust the questions asked during the interview, taking into account the customer's current health condition. For example, if the customer is tired, the interviewing robot can ask concise questions to quickly gather information. If the customer is healthy, the interviewing robot can ask more detailed questions to gather more information. If the customer is unwell, the interviewing robot can ask only the minimum necessary questions to quickly gather information. This allows for more appropriate information gathering by adjusting the questions according to the customer's health condition. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or without AI. For example, the interviewing unit can use a robot to input the customer's health condition into a generating AI and have the generating AI adjust the questions.

[0045] The interviewing unit can use a robot to customize the questions asked during the interview based on the customer's occupation and hobbies. For example, if the customer is engaged in a specific occupation, the interviewing unit will ask questions related to that occupation. For example, if the customer has a specific hobby, the interviewing unit can also ask questions related to that hobby. For example, if the customer is interested in a specific industry, the interviewing unit can also ask questions related to that industry. By customizing the questions according to the customer's occupation and hobbies, it becomes possible to collect more appropriate information. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or not using AI. For example, the interviewing unit can use a robot to input the customer's occupation and hobbies into a generating AI and have the generating AI perform the customization of the questions.

[0046] The interviewing unit can use a robot to adjust the questions asked during the interview, taking into account the customer's family structure. For example, if the customer is with their family, the interviewing unit will ask questions relevant to all family members. If the customer is visiting alone, the interviewing unit can also ask individual questions. If the customer has a specific family structure, the interviewing unit can also ask questions related to that structure. By adjusting the questions according to the customer's family structure, more appropriate information can be collected. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or without AI. For example, the interviewing unit can use a robot to input the customer's family structure into a generating AI and have the generating AI adjust the questions.

[0047] The preference hearing unit can use a robot to optimize the questions asked during the preference hearing by referring to the customer's past preferences. For example, the preference hearing unit can ask relevant preference questions based on what the customer has requested in the past. The preference hearing unit can also ask relevant preference questions based on services the customer has used in the past. The preference hearing unit can also ask relevant preference questions based on events the customer has participated in in the past. This allows the questions to be optimized by referring to the customer's past preferences. Some or all of the above processing in the preference hearing unit may be performed using AI, for example, or without AI. For example, the preference hearing unit can use a robot to input the customer's past preferences into a generating AI and have the generating AI perform the optimization of the questions.

[0048] The preference gathering unit can use a robot to adjust the questions asked during the preference gathering process, taking into account the customer's current living situation. For example, if the customer is busy, the preference gathering robot can ask concise questions to quickly gather their preferences. If the customer is relaxed, the preference gathering robot can ask more detailed questions to gather more preferences. If the customer is in a specific living situation, the preference gathering unit can also ask questions related to that situation. By adjusting the questions according to the customer's living situation, it is possible to gather more appropriate preferences. Some or all of the above processes in the preference gathering unit may be performed using AI, for example, or without AI. For example, the preference gathering unit can use a robot to input the customer's living situation into a generating AI and have the generating AI adjust the questions.

[0049] The preference gathering department can use a robot to customize the questions asked during the preference gathering process based on the customer's age and gender. For example, if the customer is elderly, the preference gathering robot can respond at a slow pace and carefully explain the necessary information. If the customer is young, the preference gathering robot can respond in a casual tone and provide concise explanations. If the customer is female, the preference gathering robot can provide information on services and promotions tailored to women. This allows for the collection of more appropriate preferences by customizing the questions according to the customer's age and gender. Some or all of the above processing in the preference gathering department may be performed using AI, for example, or not. For example, the preference gathering department can use a robot to input the customer's age and gender into a generating AI and have the generating AI customize the questions.

[0050] The Preferences Hearing Department can use a robot to analyze the customer's social media activity during the preferences hearing and provide relevant preferences. For example, if the customer shows interest in a particular event on social media, the Preferences Hearing Department can provide information about that event. For example, if the customer follows a particular brand on social media, the Preferences Hearing Department can also provide promotional information about that brand. For example, if the customer posts reviews of a particular product on social media, the Preferences Hearing Department can also provide information about that product. In this way, by analyzing the customer's social media activity, relevant preferences can be provided. Some or all of the above processing in the Preferences Hearing Department may be performed using AI, for example, or not using AI. For example, the Preferences Hearing Department can use a robot to input the customer's social media activity into a generating AI and have the generating AI provide relevant information.

[0051] The proposal department can use a robot to refer to the customer's past interaction history when making a proposal and select the most suitable proposal method. For example, the proposal department can propose relevant services based on services the customer has used in the past. For example, the proposal department can also propose relevant products based on products the customer has purchased in the past. For example, the proposal department can propose relevant events based on events the customer has participated in in the past. This allows the department to select the most suitable proposal method by referring to the customer's past interaction history. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can use a robot to input the customer's past interaction history into a generating AI and have the generating AI select the proposal method.

[0052] The suggestion unit can use a robot to make suggestions while considering the customer's current location within the shopping center. For example, if the customer is in a specific area within the shopping center, the suggestion unit can suggest services and products related to that area. For example, if the customer is moving around within the shopping center, the suggestion unit can also provide optimal route guidance based on their current location. For example, if the customer is approaching a specific store, the suggestion unit can provide promotional information for that store. This allows for more appropriate suggestions by considering the customer's current location. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can use a robot to input the customer's current location within the shopping center into a generating AI, and have the generating AI select the content of the suggestions.

[0053] The proposal department can use robots to customize proposals based on the customer's age and gender. For example, if the customer is elderly, the proposal robot can respond at a slow pace and carefully explain the necessary information. If the customer is young, the proposal robot can respond in a casual tone and provide concise explanations. If the customer is female, the proposal robot can provide information on services and promotions tailored to women. By customizing proposals according to the customer's age and gender, more appropriate proposals can be made. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can use a robot to input the customer's age and gender into a generating AI and have the generating AI perform the customization of the proposal content.

[0054] The suggestion department can use a robot to analyze the customer's social media activity when making suggestions and provide relevant suggestions. For example, if the customer shows interest in a particular event on social media, the suggestion department can provide information about that event. For example, if the customer follows a particular brand on social media, the suggestion department can also provide promotional information about that brand. For example, if the customer posts a review of a particular product on social media, the suggestion department can also provide information about that product. In this way, by analyzing the customer's social media activity, it is possible to provide relevant suggestions. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not using AI. For example, the suggestion department can use a robot to input the customer's social media activity into a generating AI and have the generating AI perform the task of providing relevant information.

[0055] The recording unit can use a robot to optimize the recorded content by referring to the customer's past visit history during recording. For example, the recording unit can record relevant information based on the customer's past visit history. The recording unit can also record relevant information based on services the customer has used in the past. The recording unit can also record relevant information based on products the customer has purchased in the past. This allows the recording content to be optimized by referring to the customer's past visit history. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can use a robot to input the customer's past visit history into a generating AI and have the generating AI perform the optimization of the recorded content.

[0056] The recording unit can use a robot to customize the recording content based on the customer's age and gender during recording. For example, if the customer is elderly, the recording robot can respond at a slow pace and carefully record the necessary information. If the customer is young, the recording robot can respond in a casual tone and make a concise record. If the customer is female, the recording robot can record information about services and promotions aimed at women. This allows for the recording of more appropriate information by customizing the recording content according to the customer's age and gender. Some or all of the above processing in the recording unit may be performed using AI, for example, or not using AI. For example, the recording unit can use a robot to input the customer's age and gender into a generating AI and have the generating AI perform the customization of the recording content.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The shopping center reception robot system uses speech recognition technology to accurately understand customer requests when the robot performs reception duties. For example, the reception desk can transcribe customer speech in real time and accurately record requests. Furthermore, by using speech recognition technology, it can respond individually even when multiple customers speak at the same time. This improves the efficiency of reception work and reduces waiting times. In addition, by using speech recognition technology, it is possible to support multiple languages, making it possible to serve tourists from overseas.

[0059] The shopping center reception robot system can store information collected by the robots in the cloud and share it with other robots and systems. For example, the recording unit uses cloud storage to save information, allowing other robots to access the same information. This enables multiple robots to work together efficiently. In addition, the information stored in the cloud can be viewed in real time by shopping center managers, which can be used to improve operations. Furthermore, the information in the cloud is protected by security measures, ensuring the protection of personal information.

[0060] The shopping center reception robot system can use facial recognition technology to identify customers when the robot performs reception duties. For example, the reception area is equipped with a facial recognition camera that can recognize customers' faces and automatically retrieve their personal information. This allows for quick verification of past visit history and requests when customers return. Furthermore, facial recognition technology can be used to identify VIP customers and provide them with special treatment. In addition, facial recognition technology can be used as a security measure, helping to detect suspicious individuals and prevent crime.

[0061] The shopping center reception robot system uses natural language processing technology to understand customer requests when the robot performs interviewing tasks. For example, the interviewing unit can analyze the customer's speech and accurately grasp their requests. This enables appropriate responses to customer requests. Furthermore, by using natural language processing technology, the system can handle ambiguous expressions and phrasing from customers. In addition, natural language processing technology can support multiple languages, allowing the system to assist customers who speak foreign languages.

[0062] The shopping center reception robot system utilizes machine learning technology to learn customer preferences and provide optimal suggestions when the robot performs its recommendation tasks. For example, the recommendation department can learn customer preferences based on their past purchase and visit history and suggest relevant products and services. This allows for suggestions that meet customer needs and improves customer satisfaction. Furthermore, the use of machine learning technology improves the accuracy of suggestions, enabling more effective marketing. Moreover, the accuracy of machine learning technology improves over time, allowing for continuously optimal suggestions.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk initially receives the customer's request. The reception desk can, for example, use AI to receive customer requests. Step 2: The interviewing department interviews customers to gather their current personal information based on the information received by the reception department. The interviewing department can also use AI, for example, to gather customers' personal information. Step 3: The Request Hearing Department will hear the customer's wishes based on the information gathered by the Hearing Department. The Request Hearing Department can, for example, use AI to hear the customer's wishes. Step 4: The proposal department analyzes the information gathered by the needs assessment department and proposes appropriate solutions. The proposal department can, for example, use AI to propose appropriate solutions.

[0065] (Example of form 2) The shopping center reception robot system according to an embodiment of the present invention is a system that shortens customer service time and alleviates labor shortages by having a robot take over reception duties at a shopping center. In this shopping center reception robot system, first, the robot temporarily receives the customer's request. Next, the robot uses an AI agent to interview the customer's current personal information. Furthermore, the robot interviews the customer's wishes. This makes human interaction smoother and reduces customer service time. Shopping centers are understaffed to meet customer demand, so by having a robot perform temporary reception duties in place of a receptionist, the time spent on customer service can be shortened. For example, the shopping center reception robot system utilizes an AI agent and targets all customers visiting the shopping center. For example, the shopping center reception robot system includes a reception unit that temporarily receives customer requests. The reception unit can, for example, temporarily receive customer requests. Next, the shopping center reception robot system includes an interview unit that interviews the customer's current personal information based on the information received by the reception unit. The interview unit can, for example, interview the customer's current personal information. Furthermore, the shopping center reception robot system includes a preference-gathering unit that gathers information from customers based on the information gathered by the hearing unit. The preference-gathering unit can, for example, gather information from customers. Finally, the shopping center reception robot system includes a proposal unit that analyzes the information gathered by the preference-gathering unit and proposes appropriate responses. The proposal unit can, for example, analyze the gathered information and propose appropriate responses. As a result, the shopping center reception robot system can efficiently receive customer requests, conduct hearings, gather preferences, and make proposals. This allows the shopping center reception robot system to reduce customer service time and alleviate labor shortages.

[0066] The shopping center reception robot system according to this embodiment comprises a reception unit, a hearing unit, a preference hearing unit, and a proposal unit. The reception unit temporarily receives customer requests. The reception unit can, for example, temporarily receive customer requests. The reception unit can, for example, receive customer requests using AI. The hearing unit hears the customer's current personal information based on the information received by the reception unit. The hearing unit can, for example, hear the customer's current personal information. The hearing unit can, for example, hear the customer's personal information using AI. The preference hearing unit hears the customer's preferences based on the information heard by the hearing unit. The preference hearing unit can, for example, hear the customer's preferences. The preference hearing unit can, for example, hear the customer's preferences using AI. The proposal unit analyzes the information heard by the preference hearing unit and proposes an appropriate response. The proposal unit can, for example, analyze the information heard and propose an appropriate response. The proposal department can, for example, use AI to suggest appropriate responses. This allows the shopping center reception robot system to efficiently receive customer requests, conduct interviews, understand their needs, and make suggestions.

[0067] The reception desk will initially receive customer requests. Specifically, the reception desk will be located at the entrance and key areas of the shopping center, and will automatically activate when a customer approaches the robot. The robot will use voice recognition technology to listen to customer requests and record them as text data. For example, if a customer says, "Please tell me about nearby restaurants," the robot will accurately recognize the request and temporarily store it in the database. Furthermore, the reception desk is multilingual and designed to accommodate foreign tourists. By using AI, the reception desk can quickly and accurately receive customer requests and can also refer to past data to utilize the history of how similar requests were handled. This allows the reception desk to efficiently process customer requests and smoothly provide information to the next step, the hearing department.

[0068] The Hearing Department gathers current personal information from customers based on the information received by the Reception Department. Specifically, the Hearing Department collects basic personal information such as the customer's name, contact information, and purpose of visit. The robot uses a voice dialogue system to engage in natural conversation with the customer and extract necessary information. For example, it might ask questions such as, "Could you tell me your name?" or "What kind of service are you looking for?" By using AI, the Hearing Department can analyze the customer's responses in real time and quickly collect the necessary information. Furthermore, from a privacy perspective, the Hearing Department manages the collected personal information appropriately and implements security measures to prevent leakage to third parties. This allows the Hearing Department to accurately and securely collect customers' personal information and smoothly provide that information to the next step, the Desired Hearing Department.

[0069] The Preferences Hearing Department gathers information about customers' preferences based on the information gathered by the General Hearing Department. Specifically, the Preferences Hearing Department collects detailed information about the specific services and products that customers are looking for. For example, it might ask questions such as, "What kind of restaurant are you looking for?" or "Are you looking for a specific brand of product?" By using AI, the Preferences Hearing Department can quickly analyze customer responses and store relevant information in a database. It can also refer to customers' past visit and purchase history to ask more personalized questions. This allows the Preferences Hearing Department to accurately understand customers' specific needs and preferences, and smoothly provide that information to the Proposal Department, which is the next step. Furthermore, the Preferences Hearing Department can collect customer feedback and provide data to continuously improve the quality of service.

[0070] The Proposal Department analyzes information gathered by the Customer Needs Assessment Department and proposes appropriate responses. Specifically, the Proposal Department uses AI to suggest the most suitable services and products based on customer preferences. For example, if a customer responds that they are "looking for an Italian restaurant," the Proposal Department will display a list of Italian restaurants within the shopping center, providing menus, ratings, and availability information for each restaurant. The Proposal Department can also refer to the customer's past visit and purchase history to provide more personalized suggestions. For example, it can suggest new restaurants based on ratings of restaurants visited in the past and preferred menu items. Furthermore, the Proposal Department can suggest the latest services and products based on real-time updated information. This allows the Proposal Department to quickly provide optimal suggestions to customers and improve their experience at the shopping center. The Proposal Department can also collect customer feedback and provide data to continuously improve the accuracy and effectiveness of its suggestions. This allows the Proposal Department to provide optimal suggestions tailored to customer needs and improve the overall service quality of the shopping center.

[0071] The shopping center reception robot system includes a recording unit that records information collected by the robot. The recording unit can, for example, record information collected by the robot. The recording unit can also record information using, for example, AI. This makes information management easier by recording the information collected by the robot. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input information collected by the robot into a generating AI and have the generating AI perform the recording of the information.

[0072] The reception area can temporarily receive customer requests using a robot. The reception area can, for example, temporarily receive customer requests using a robot. The reception area can also, for example, receive customer requests using AI. This allows for efficient handling of customer requests by using a robot. Some or all of the above-described processes in the reception area may be performed using AI, for example, or without AI. For example, the reception area can use a robot to input customer requests into a generating AI and have the generating AI handle the request reception.

[0073] The interviewing unit can use a robot to interview customers about their current personal information. The interviewing unit can, for example, use a robot to interview customers about their current personal information. The interviewing unit can also, for example, use AI to interview customers about their personal information. This allows for efficient interviewing of customers' personal information by using a robot. Some or all of the above-described processes in the interviewing unit may be performed using AI, for example, or without AI. For example, the interviewing unit can use a robot to input the customer's personal information into a generating AI and have the generating AI perform the personal information interview.

[0074] The preference gathering unit can gather customer preferences using a robot. The preference gathering unit can, for example, gather customer preferences using a robot. The preference gathering unit can also, for example, gather customer preferences using AI. This allows for efficient gathering of customer preferences by using a robot. Some or all of the above-described processes in the preference gathering unit may be performed using AI, for example, or without AI. For example, the preference gathering unit can use a robot to input customer preferences into a generating AI and have the generating AI perform the preference gathering.

[0075] The proposal unit can analyze information gathered through interviews using a robot and propose appropriate responses. For example, the proposal unit can analyze information gathered through interviews using a robot and propose appropriate responses. The proposal unit can also propose appropriate responses using AI, for example. This allows for efficient analysis of interview information and the proposal of appropriate responses through the use of a robot. Some or all of the above-described processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input information gathered through interviews using a robot into a generating AI and have the generating AI execute a proposal for an appropriate response.

[0076] The reception area can use a robot to estimate a customer's emotions and adjust its response based on the estimated emotions. For example, if a customer is stressed, the reception robot can respond in a calm voice and quickly take their request. If a customer is relaxed, the reception robot can respond in a friendly tone and provide detailed explanations. If a customer is in a hurry, the reception robot can provide a concise response and quickly move on to the next step. This allows for more appropriate service by adjusting the reception response according to 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 reception area may be performed using AI or not. For example, the reception area can use a robot to input the customer's emotions into a generative AI and have the generative AI perform emotion estimation.

[0077] The reception area can use a robot to refer to a customer's past visit history at check-in to select the most appropriate response. For example, if a customer has visited frequently in the past, the reception robot can refer to that history and provide the most appropriate response based on previous interactions. For example, if a customer is visiting for the first time, the reception robot can provide basic guidance and necessary information. For example, if a customer has a history of using a particular service, the reception area can prioritize providing information related to that service. This allows the reception area to select the most appropriate response by referring to the customer's past visit history. Some or all of the above processes in the reception area may be performed using AI, for example, or not. For example, the reception area can use a robot to input a customer's past visit history into a generating AI and have the generating AI select the most appropriate response.

[0078] The reception area can use a robot to consider the customer's current location within the shopping center when checking in. For example, if the customer is in a specific area within the shopping center, the reception area can provide information related to that area. For example, if the customer is moving around within the shopping center, the reception area can also provide optimal route guidance based on their current location. For example, if the customer is approaching a specific store, the reception area can provide promotional information about that store. This allows for more appropriate service by considering the customer's current location. Some or all of the above processing at the reception area may be performed using AI, for example, or not using AI. For example, the reception area can use a robot to input the customer's current location within the shopping center into a generating AI, and have the generating AI select the appropriate response method.

[0079] The reception area can use a robot to estimate a customer's emotions and determine the priority of service based on the estimated emotions. For example, if a customer is stressed, the reception robot can prioritize their request and quickly process it. If a customer is relaxed, the reception area can attend to them after other customers have been served. If a customer is in a hurry, the reception robot can prioritize their request and quickly move them to the next step. This allows for more appropriate service by prioritizing service according to 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 reception area may be performed using AI or not. For example, the reception area can use a robot to input the customer's emotions into a generative AI and have the generative AI perform emotion estimation.

[0080] The reception area can use robots to customize how they interact with customers based on their age and gender. For example, if a customer is elderly, the reception robot can interact at a slow pace and carefully explain the necessary information. If a customer is young, the reception robot can interact in a casual tone and provide concise explanations. If a customer is female, the reception robot can provide information on services and promotions tailored to women. This allows for more appropriate service by customizing the interaction based on the customer's age and gender. Some or all of the above processes in the reception area may be performed using AI, for example, or not. For example, the reception area can use a robot to input the customer's age and gender into a generating AI and have the generating AI perform the customization of the interaction.

[0081] The reception desk can use a robot to analyze customers' social media activity at the time of check-in and provide relevant information. For example, if a customer shows interest in a particular event on social media, the reception desk can provide information about that event. For example, if a customer follows a particular brand on social media, the reception desk can also provide promotional information about that brand. For example, if a customer posts a review of a particular product on social media, the reception desk can also provide information about that product. In this way, relevant information can be provided by analyzing customers' social media activity. Some or all of the above processing at the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can use a robot to input the customer's social media activity into a generating AI and have the generating AI perform the task of providing relevant information.

[0082] The interviewing unit can use a robot to estimate the customer's emotions and adjust the interview questions based on the estimated emotions. For example, if the customer is stressed, the interviewing robot can ask concise questions to quickly gather information. If the customer is relaxed, the interviewing robot can ask detailed questions to gather more information. If the customer is in a hurry, the interviewing robot can ask concise questions to quickly gather information. By adjusting the interview questions according to the customer's emotions, more appropriate information can be collected. 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 interviewing unit may be performed using AI, or not using AI. For example, the interviewing unit can use a robot to input the customer's emotions into a generative AI and have the generative AI perform emotion estimation.

[0083] The interviewing unit can use a robot to optimize the questions asked during the interview by referring to the customer's past purchase history. For example, the interviewing unit can ask questions about related products based on products the customer has purchased in the past. The interviewing unit can also ask questions about related services based on services the customer has used in the past. For example, the interviewing unit can ask questions about related events based on events the customer has participated in in the past. This allows the questions to be optimized by referring to the customer's past purchase history. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or without AI. For example, the interviewing unit can use a robot to input the customer's past purchase history into a generating AI and have the generating AI perform the optimization of the questions.

[0084] The interviewing unit can use a robot to adjust the questions asked during the interview, taking into account the customer's current health condition. For example, if the customer is tired, the interviewing robot can ask concise questions to quickly gather information. If the customer is healthy, the interviewing robot can ask more detailed questions to gather more information. If the customer is unwell, the interviewing robot can ask only the minimum necessary questions to quickly gather information. This allows for more appropriate information gathering by adjusting the questions according to the customer's health condition. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or without AI. For example, the interviewing unit can use a robot to input the customer's health condition into a generating AI and have the generating AI adjust the questions.

[0085] The interviewing unit can use a robot to estimate the customer's emotions and adjust the order of the interview based on the estimated emotions. For example, if the customer is stressed, the interviewing robot will prioritize asking important questions. If the customer is relaxed, the interviewing unit can also adjust the order by having the interviewing robot ask detailed questions. If the customer is in a hurry, the interviewing unit can also prioritize asking concise questions. By adjusting the order of the interview according to the customer's emotions, more appropriate information can be gathered. 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 interviewing unit may be performed using AI, or not using AI. For example, the interviewing unit can use a robot to input the customer's emotions into a generative AI and have the generative AI perform emotion estimation.

[0086] The interviewing unit can use a robot to customize the questions asked during the interview based on the customer's occupation and hobbies. For example, if the customer is engaged in a specific occupation, the interviewing unit will ask questions related to that occupation. For example, if the customer has a specific hobby, the interviewing unit can also ask questions related to that hobby. For example, if the customer is interested in a specific industry, the interviewing unit can also ask questions related to that industry. By customizing the questions according to the customer's occupation and hobbies, it becomes possible to collect more appropriate information. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or not using AI. For example, the interviewing unit can use a robot to input the customer's occupation and hobbies into a generating AI and have the generating AI perform the customization of the questions.

[0087] The interviewing unit can use a robot to adjust the questions asked during the interview, taking into account the customer's family structure. For example, if the customer is with their family, the interviewing unit will ask questions relevant to all family members. If the customer is visiting alone, the interviewing unit can also ask individual questions. If the customer has a specific family structure, the interviewing unit can also ask questions related to that structure. By adjusting the questions according to the customer's family structure, more appropriate information can be collected. Some or all of the above processing in the interviewing unit may be performed using AI, for example, or without AI. For example, the interviewing unit can use a robot to input the customer's family structure into a generating AI and have the generating AI adjust the questions.

[0088] The desire gathering unit can use a robot to estimate the customer's emotions and adjust the method of desire gathering based on the estimated emotions. For example, if the customer is stressed, the desire gathering unit's robot can ask concise questions to quickly gather their desires. For example, if the customer is relaxed, the desire gathering unit's robot can ask detailed questions to gather more desires. For example, if the customer is in a hurry, the desire gathering unit's robot can ask concise questions to quickly gather their desires. This allows for the collection of more appropriate desires by adjusting the method of desire gathering according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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-described processing in the desire gathering unit may be performed using AI, for example, or without AI. For example, the customer needs assessment department can use a robot to input customer emotions into a generative AI, which can then perform emotion estimation.

[0089] The preference hearing unit can use a robot to optimize the questions asked during the preference hearing by referring to the customer's past preferences. For example, the preference hearing unit can ask relevant preference questions based on what the customer has requested in the past. The preference hearing unit can also ask relevant preference questions based on services the customer has used in the past. The preference hearing unit can also ask relevant preference questions based on events the customer has participated in in the past. This allows the questions to be optimized by referring to the customer's past preferences. Some or all of the above processing in the preference hearing unit may be performed using AI, for example, or without AI. For example, the preference hearing unit can use a robot to input the customer's past preferences into a generating AI and have the generating AI perform the optimization of the questions.

[0090] The preference gathering unit can use a robot to adjust the questions asked during the preference gathering process, taking into account the customer's current living situation. For example, if the customer is busy, the preference gathering robot can ask concise questions to quickly gather their preferences. If the customer is relaxed, the preference gathering robot can ask more detailed questions to gather more preferences. If the customer is in a specific living situation, the preference gathering unit can also ask questions related to that situation. By adjusting the questions according to the customer's living situation, it is possible to gather more appropriate preferences. Some or all of the above processes in the preference gathering unit may be performed using AI, for example, or without AI. For example, the preference gathering unit can use a robot to input the customer's living situation into a generating AI and have the generating AI adjust the questions.

[0091] The desire assessment unit can use a robot to estimate the customer's emotions and determine the priority of desire assessments based on the estimated emotions. For example, if the customer is stressed, the desire assessment robot will prioritize their response and quickly collect their wishes. For example, if the customer is relaxed, the desire assessment unit can respond after assisting other customers. For example, if the customer is in a hurry, the desire assessment robot can prioritize their response and quickly collect their wishes. This allows for the collection of more appropriate wishes by prioritizing desire assessments according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the desire assessment unit may be performed using AI or not using AI. For example, the customer needs assessment department can use a robot to input customer emotions into a generative AI, which can then perform emotion estimation.

[0092] The preference gathering department can use a robot to customize the questions asked during the preference gathering process based on the customer's age and gender. For example, if the customer is elderly, the preference gathering robot can respond at a slow pace and carefully explain the necessary information. If the customer is young, the preference gathering robot can respond in a casual tone and provide concise explanations. If the customer is female, the preference gathering robot can provide information on services and promotions tailored to women. This allows for the collection of more appropriate preferences by customizing the questions according to the customer's age and gender. Some or all of the above processing in the preference gathering department may be performed using AI, for example, or not. For example, the preference gathering department can use a robot to input the customer's age and gender into a generating AI and have the generating AI customize the questions.

[0093] The Preferences Hearing Department can use a robot to analyze the customer's social media activity during the preferences hearing and provide relevant preferences. For example, if the customer shows interest in a particular event on social media, the Preferences Hearing Department can provide information about that event. For example, if the customer follows a particular brand on social media, the Preferences Hearing Department can also provide promotional information about that brand. For example, if the customer posts reviews of a particular product on social media, the Preferences Hearing Department can also provide information about that product. In this way, by analyzing the customer's social media activity, relevant preferences can be provided. Some or all of the above processing in the Preferences Hearing Department may be performed using AI, for example, or not using AI. For example, the Preferences Hearing Department can use a robot to input the customer's social media activity into a generating AI and have the generating AI provide relevant information.

[0094] The suggestion department can use a robot to estimate the customer's emotions and adjust the way suggestions are presented based on the estimated emotions. For example, if the customer is stressed, the suggestion robot can make suggestions in a calm voice and respond quickly. If the customer is relaxed, the suggestion robot can make suggestions in a friendly tone and provide detailed explanations. If the customer is in a hurry, the suggestion robot can make concise suggestions and quickly move on to the next step. This allows for more appropriate suggestions by adjusting the way suggestions are presented according to 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 suggestion department may be performed using AI or not. For example, the suggestion department can use a robot to input the customer's emotions into a generative AI and have the generative AI perform emotion estimation.

[0095] The proposal department can use a robot to refer to the customer's past interaction history when making a proposal and select the most suitable proposal method. For example, the proposal department can propose relevant services based on services the customer has used in the past. For example, the proposal department can also propose relevant products based on products the customer has purchased in the past. For example, the proposal department can propose relevant events based on events the customer has participated in in the past. This allows the department to select the most suitable proposal method by referring to the customer's past interaction history. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can use a robot to input the customer's past interaction history into a generating AI and have the generating AI select the proposal method.

[0096] The suggestion unit can use a robot to make suggestions while considering the customer's current location within the shopping center. For example, if the customer is in a specific area within the shopping center, the suggestion unit can suggest services and products related to that area. For example, if the customer is moving around within the shopping center, the suggestion unit can also provide optimal route guidance based on their current location. For example, if the customer is approaching a specific store, the suggestion unit can provide promotional information for that store. This allows for more appropriate suggestions by considering the customer's current location. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can use a robot to input the customer's current location within the shopping center into a generating AI, and have the generating AI select the content of the suggestions.

[0097] The suggestion department can use a robot to estimate the customer's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the customer is stressed, the suggestion robot will prioritize their response and make suggestions quickly. If the customer is relaxed, the suggestion department can make suggestions after assisting other customers. If the customer is in a hurry, the suggestion robot will prioritize their response and make suggestions quickly. This allows for more appropriate suggestions by prioritizing suggestions according to 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 suggestion department may be performed using AI or not. For example, the suggestion department can use a robot to input the customer's emotions into the generative AI and have the generative AI perform emotion estimation.

[0098] The proposal department can use robots to customize proposals based on the customer's age and gender. For example, if the customer is elderly, the proposal robot can respond at a slow pace and carefully explain the necessary information. If the customer is young, the proposal robot can respond in a casual tone and provide concise explanations. If the customer is female, the proposal robot can provide information on services and promotions tailored to women. By customizing proposals according to the customer's age and gender, more appropriate proposals can be made. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can use a robot to input the customer's age and gender into a generating AI and have the generating AI perform the customization of the proposal content.

[0099] The suggestion department can use a robot to analyze the customer's social media activity when making suggestions and provide relevant suggestions. For example, if the customer shows interest in a particular event on social media, the suggestion department can provide information about that event. For example, if the customer follows a particular brand on social media, the suggestion department can also provide promotional information about that brand. For example, if the customer posts a review of a particular product on social media, the suggestion department can also provide information about that product. In this way, by analyzing the customer's social media activity, it is possible to provide relevant suggestions. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not using AI. For example, the suggestion department can use a robot to input the customer's social media activity into a generating AI and have the generating AI perform the task of providing relevant information.

[0100] The recording unit can use a robot to estimate the customer's emotions and adjust the recording method based on the estimated emotions. For example, if the customer is stressed, the recording robot can make a concise record and quickly save the information. If the customer is relaxed, the recording robot can make a detailed record and save more information. If the customer is in a hurry, the recording robot can make a concise record and quickly save the information. This allows for the recording of more appropriate information by adjusting the recording method according to 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 recording unit may be performed using AI or not using AI. For example, the recording unit can use a robot to input the customer's emotions into the generative AI and have the generative AI perform emotion estimation.

[0101] The recording unit can use a robot to optimize the recorded content by referring to the customer's past visit history during recording. For example, the recording unit can record relevant information based on the customer's past visit history. The recording unit can also record relevant information based on services the customer has used in the past. The recording unit can also record relevant information based on products the customer has purchased in the past. This allows the recording content to be optimized by referring to the customer's past visit history. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can use a robot to input the customer's past visit history into a generating AI and have the generating AI perform the optimization of the recorded content.

[0102] The recording unit can use a robot to estimate the customer's emotions and determine the priority of recording based on the estimated emotions. For example, if the customer is stressed, the recording robot will prioritize their response and record quickly. If the customer is relaxed, the recording unit can record after assisting other customers. If the customer is in a hurry, the recording robot will prioritize their response and record quickly. This allows for the recording of more appropriate information by prioritizing recording according to 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 recording unit may be performed using AI or not. For example, the recording unit can use a robot to input the customer's emotions into the generative AI and have the generative AI perform emotion estimation.

[0103] The recording unit can use a robot to customize the recording content based on the customer's age and gender during recording. For example, if the customer is elderly, the recording robot can respond at a slow pace and carefully record the necessary information. If the customer is young, the recording robot can respond in a casual tone and make a concise record. If the customer is female, the recording robot can record information about services and promotions aimed at women. This allows for the recording of more appropriate information by customizing the recording content according to the customer's age and gender. Some or all of the above processing in the recording unit may be performed using AI, for example, or not using AI. For example, the recording unit can use a robot to input the customer's age and gender into a generating AI and have the generating AI perform the customization of the recording content.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The shopping center reception robot system uses speech recognition technology to accurately understand customer requests when the robot performs reception duties. For example, the reception desk can transcribe customer speech in real time and accurately record requests. Furthermore, by using speech recognition technology, it can respond individually even when multiple customers speak at the same time. This improves the efficiency of reception work and reduces waiting times. In addition, by using speech recognition technology, it is possible to support multiple languages, making it possible to serve tourists from overseas.

[0106] The shopping center reception robot system can store information collected by the robots in the cloud and share it with other robots and systems. For example, the recording unit uses cloud storage to save information, allowing other robots to access the same information. This enables multiple robots to work together efficiently. In addition, the information stored in the cloud can be viewed in real time by shopping center managers, which can be used to improve operations. Furthermore, the information in the cloud is protected by security measures, ensuring the protection of personal information.

[0107] The shopping center reception robot system can use facial recognition technology to identify customers when the robot performs reception duties. For example, the reception area is equipped with a facial recognition camera that can recognize customers' faces and automatically retrieve their personal information. This allows for quick verification of past visit history and requests when customers return. Furthermore, facial recognition technology can be used to identify VIP customers and provide them with special treatment. In addition, facial recognition technology can be used as a security measure, helping to detect suspicious individuals and prevent crime.

[0108] The shopping center reception robot system uses natural language processing technology to understand customer requests when the robot performs interviewing tasks. For example, the interviewing unit can analyze the customer's speech and accurately grasp their requests. This enables appropriate responses to customer requests. Furthermore, by using natural language processing technology, the system can handle ambiguous expressions and phrasing from customers. In addition, natural language processing technology can support multiple languages, allowing the system to assist customers who speak foreign languages.

[0109] The shopping center reception robot system uses emotion analysis technology to estimate customers' emotions when the robot performs the task of gathering customer requests, and adjusts its response accordingly. For example, the request gathering unit can analyze the customer's tone of voice and facial expressions to estimate their emotions. This allows the robot to respond quickly if the customer is stressed, and provide detailed explanations if they are relaxed. Furthermore, using emotion analysis technology can improve customer satisfaction. In addition, emotion analysis technology is useful in handling complaints, allowing for early detection of customer dissatisfaction and appropriate responses.

[0110] The shopping center reception robot system utilizes machine learning technology to learn customer preferences and provide optimal suggestions when the robot performs its recommendation tasks. For example, the recommendation department can learn customer preferences based on their past purchase and visit history and suggest relevant products and services. This allows for suggestions that meet customer needs and improves customer satisfaction. Furthermore, the use of machine learning technology improves the accuracy of suggestions, enabling more effective marketing. Moreover, the accuracy of machine learning technology improves over time, allowing for continuously optimal suggestions.

[0111] The shopping center reception robot system uses emotion estimation capabilities to adjust its approach based on the customer's emotions when performing reception duties. For example, if a customer is stressed, the receptionist can respond in a calm voice and quickly take their request. If the customer is relaxed, the robot can respond in a friendly tone and provide detailed explanations. If the customer is in a hurry, the robot can respond concisely and quickly move on to the next step. This allows for more appropriate service by adjusting the approach according to the customer's emotions.

[0112] The shopping center reception robot system can use emotion estimation to adjust questions based on the customer's emotions when the robot performs interview tasks. For example, if the customer is stressed, the interviewer can ask concise questions to quickly gather information. If the customer is relaxed, the robot can ask more detailed questions to gather more information. If the customer is in a hurry, the robot can ask concise questions to quickly gather information. In this way, by adjusting the questions according to the customer's emotions, more appropriate information can be gathered.

[0113] The shopping center reception robot system can use emotion estimation capabilities to adjust the way it presents suggestions based on the customer's emotions when the robot is making suggestions. For example, if the customer is stressed, the suggestion department can make suggestions in a calm voice and respond quickly. If the customer is relaxed, it can make suggestions in a friendly tone and provide detailed explanations. If the customer is in a hurry, it can make concise suggestions and move quickly to the next step. In this way, by adjusting the way suggestions are presented according to the customer's emotions, more appropriate suggestions can be made.

[0114] The shopping center reception robot system can use emotion estimation to adjust its recording method based on the customer's emotions when the robot performs recording tasks. For example, if the customer is stressed, the recording unit can make a concise record and quickly save the information. If the customer is relaxed, it can make a detailed record and save more information. If the customer is in a hurry, it can make a concise record and quickly save the information. In this way, by adjusting the recording method according to the customer's emotions, more appropriate information can be recorded.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The reception desk initially receives the customer's request. The reception desk can, for example, use AI to receive customer requests. Step 2: The interviewing department interviews customers to gather their current personal information based on the information received by the reception department. The interviewing department can also use AI, for example, to gather customers' personal information. Step 3: The Request Hearing Department will hear the customer's wishes based on the information gathered by the Hearing Department. The Request Hearing Department can, for example, use AI to hear the customer's wishes. Step 4: The proposal department analyzes the information gathered by the needs assessment department and proposes appropriate solutions. The proposal department can, for example, use AI to propose appropriate solutions.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the reception unit, hearing unit, preference hearing unit, proposal unit, and recording unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and temporarily receives the customer's request. The hearing unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and hears the customer's current personal information. The preference hearing unit is implemented by, for example, the control unit 46A of the smart device 14 and hears the customer's preference. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the heard information and proposes an appropriate response. The recording unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and records the information collected by the robot. 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.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the reception unit, hearing unit, preference hearing unit, proposal unit, and recording unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and temporarily receives the customer's request. The hearing unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and hears the customer's current personal information. The preference hearing unit is implemented by, for example, the control unit 46A of the smart glasses 214 and hears the customer's preference. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the heard information and proposes an appropriate response. The recording unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and records the information collected by the robot. 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.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the reception unit, hearing unit, preference hearing unit, proposal unit, and recording unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and temporarily receives the customer's request. The hearing unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and hears the customer's current personal information. The preference hearing unit is implemented by, for example, the control unit 46A of the headset terminal 314 and hears the customer's preference. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the heard information and proposes an appropriate response. The recording unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and records the information collected by the robot. 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.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Each of the multiple elements described above, including the reception unit, hearing unit, preference hearing unit, proposal unit, and recording unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and temporarily receives the customer's request. The hearing unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and hears the customer's current personal information. The preference hearing unit is implemented by, for example, the control unit 46A of the robot 414 and hears the customer's preference. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the heard information and proposes an appropriate response. The recording unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and records the information collected by the robot. 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] (Note 1) A reception desk to temporarily receive customer requests, Based on the information received by the aforementioned reception department, there is an interview department that interviews customers to gather their current personal information, A desire-gathering unit that gathers information from customers based on the information gathered by the aforementioned hearing unit, The system includes a proposal unit that analyzes the information gathered by the aforementioned preference hearing unit and proposes an appropriate response. A system characterized by the following features. (Note 2) It is equipped with a recording unit that records the information collected by the robot. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is We use robots to temporarily receive customer requests. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned hearing section is, We use robots to gather customers' current personal information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned preference hearing section is, We use robots to listen to customers' needs. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, The system uses robots to analyze information gathered through interviews and proposes appropriate responses. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We use robots to estimate customers' emotions and adjust our reception service based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is A robot is used to refer to the customer's past visit history at the time of check-in to select the most appropriate course of action. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Using robots, the system takes into account the customer's current location within the shopping center when they check in. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is A robot is used to estimate the customer's emotions, and the priority of service is determined based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is Using robots, the system customizes the approach to customers at the reception desk based on their age and gender. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is Using robots, the system analyzes customers' social media activity at check-in and provides relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned hearing section is, We use robots to estimate the customer's emotions and adjust the interview questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned hearing section is, Using a robot, we optimize the questions asked during interviews by referring to the customer's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned hearing section is, Using a robot, we adjust the questions asked during the interview process to take into account the customer's current health condition. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned hearing section is, We use a robot to estimate the customer's emotions and adjust the order of the interview based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned hearing section is, Using a robot, the questions asked during the interview are customized based on the customer's occupation and hobbies. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned hearing section is, Using a robot, we adjust the questions during the interview process to take into account the customer's family structure. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned preference hearing section is, We use robots to estimate customer emotions and adjust the method of gathering customer preferences based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned preference hearing section is, Using a robot, we optimize the questions during the initial consultation by referencing the customer's past requests. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned preference hearing section is, Using a robot, we adjust the questions asked during the initial consultation to take into account the customer's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned preference hearing section is, We use robots to estimate customer emotions and prioritize the desired customer needs based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned preference hearing section is, Using a robot, the questions asked during the initial consultation are customized based on the customer's age and gender. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned preference hearing section is, Using robots, we analyze customers' social media activity during the initial consultation to provide relevant information and suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, We use robots to estimate customer emotions and adjust the way we present our proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, Using a robot, the system selects the optimal proposal method by referring to the customer's past interaction history during the proposal process. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, Using a robot, the system takes into account the customer's current location within the shopping center when making suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, We use robots to estimate customer emotions and prioritize suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, Using robots, proposals are customized based on the customer's age and gender. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, Using robots, we analyze customers' social media activity during the proposal process and provide relevant suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The recording unit is, We use robots to estimate customers' emotions and adjust the recording method based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The recording unit is, Using a robot, the system optimizes the recording content by referencing the customer's past visit history during the recording process. The system described in Appendix 2, characterized by the features described herein. (Note 33) The recording unit is, A robot is used to estimate customer emotions, and the priority of recordings is determined based on the estimated customer emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The recording unit is, Using a robot, the recording content is customized based on the customer's age and gender during the recording process. The system described in Appendix 2, characterized by the features described herein. [Explanation of symbols]

[0189] 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 reception desk to temporarily receive customer requests, Based on the information received by the aforementioned reception department, there is an interview department that interviews customers to gather their current personal information, A desire-gathering unit that gathers information from customers based on the information gathered by the aforementioned hearing unit, The system includes a proposal unit that analyzes the information gathered by the aforementioned preference hearing unit and proposes an appropriate response. A system characterized by the following features.

2. It is equipped with a recording unit that records the information collected by the robot. The system according to feature 1.

3. The aforementioned reception unit is We use robots to temporarily receive customer requests. The system according to feature 1.

4. The aforementioned hearing section is, We use robots to gather customers' current personal information. The system according to feature 1.

5. The aforementioned preference hearing section is, We use robots to listen to customers' needs. The system according to feature 1.

6. The aforementioned proposal section is, The system uses robots to analyze information gathered through interviews and proposes appropriate responses. The system according to feature 1.

7. The aforementioned reception unit is We use robots to estimate customers' emotions and adjust our reception service based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is A robot is used to refer to the customer's past visit history at the time of check-in to select the most appropriate course of action. The system according to feature 1.

9. The aforementioned reception unit is Using robots, the system takes into account the customer's current location within the shopping center when they check in. The system according to feature 1.

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