system

Abstract

The system according to this embodiment aims to provide appropriate answers to customer inquiries. [Solution] The system according to the embodiment comprises a reception unit, an input unit, an acquisition unit, and a provision unit. The reception unit receives questions from customers. The input unit pre-enters answers to the questions received by the reception unit. The acquisition unit acquires the information entered by the input unit. The provision unit provides answers to customers based on the information acquired by the acquisition unit.

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, it is difficult to provide up-to-date and desired information in response to questions from customers, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an appropriate answer to a question from a customer.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an input unit, an acquisition unit, and a provision unit. The reception unit receives a question from a customer. The input unit pre-inputs an answer to the question received by the reception unit. The acquisition unit acquires the information input by the input unit. The provision unit provides an answer to the customer based on the information acquired by the acquisition unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide appropriate answers to customer questions. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The information provision system according to an embodiment of the present invention is a system that provides a platform for commercial facility managers and tenants to proactively provide information requested by customers, and provides customers with a generating AI that uses this platform. This information provision system is a mechanism that improves customer convenience and satisfaction, and contributes to reducing labor costs for facilities and tenants. Specifically, it consists of the following steps. First, commercial facility managers and tenants use a platform in which they pre-enter answers to customer questions. Next, the generating AI retrieves information from this platform and provides appropriate answers to customer questions. This eliminates the need for customers to search for store staff and allows them to quickly obtain the necessary information. For example, if a customer asks, "What are the sale items today?", the generating AI provides a list of current sale items based on the information entered into the platform. Also, if a customer asks, "Which shelf is the boiled bamboo shoots on?", the generating AI guides them to the location of the relevant shelf based on the store layout information. Furthermore, if a customer asks, "How much do I need to spend to get a parking voucher?", the generating AI answers the required purchase amount based on service information. This platform can be used in various locations such as commercial facilities, theme parks, and event venues. For example, a theme park could provide information such as attraction wait times and show schedules. An event venue could provide information on booth locations and ongoing events. This system not only improves customer convenience and satisfaction but also contributes to reducing labor costs for facilities and tenants. For instance, even with limited staff available for customer service, the AI ​​can handle customer interactions, enabling more efficient operations. It also facilitates understanding customer needs, aiding in marketing strategy development. Furthermore, by registering an automated information retrieval URL, the platform can automatically update inventory and event information, ensuring that the latest information is always available. For example, by linking with an inventory database, the system can track product stock levels in real time and provide this information to customers.In this way, by providing a platform that allows commercial facility managers and tenants to proactively provide information requested by customers, and by providing customers with a generative AI that uses this platform, it is possible to improve customer convenience and satisfaction, and contribute to reducing labor costs for facilities and tenants. As a result, the information provision system can provide quick and appropriate answers to customer questions.

[0029] The information provision system according to this embodiment comprises a reception unit, an input unit, an acquisition unit, and a provision unit. The reception unit receives questions from customers. For example, customers can input questions using a smartphone or tablet. The reception unit can also receive questions from customers using voice input. For example, if a customer asks "What are today's sale items?" by voice, the reception unit will receive the question. Furthermore, customers can also input questions at the information counter within the store. For example, a customer can input a question using a tablet at the information counter. The input unit pre-enters answers to questions received by the reception unit. For example, the input unit can allow a commercial facility manager or tenant to input answers to customer questions into the platform. The input unit can also automatically generate answers to customer questions using a generation AI. For example, the generation AI generates an answer to the question "What are today's sale items?" based on the information entered into the platform. Furthermore, the input unit can periodically update the answers to customer questions. For example, the input unit inputs daily sale information into the platform, and the generation AI provides answers based on that information. The acquisition unit acquires information entered by the input unit. For example, the acquisition unit can acquire inventory information from a platform. The acquisition unit can also acquire store layout information. For example, the acquisition unit can acquire a store layout diagram from a platform and provide it to the customer. Furthermore, the acquisition unit can acquire service information and event information. For example, the acquisition unit can acquire parking service information and event schedules from a platform. The provision unit provides answers to customers based on the information acquired by the acquisition unit. For example, the provision unit can provide customers with information on the availability of products based on inventory information. Furthermore, the provision unit can guide customers to the location of products based on store layout information. For example, in response to the question, "Which shelf is the boiled bamboo shoots on?", the provision unit will guide the customer to the location of the relevant shelf based on store layout information. Furthermore, the provision unit can also provide answers to customers based on service information and event information.For example, in response to the question, "How much do I need to spend to receive a parking service voucher?", the information provision unit will answer the required purchase amount based on the service information. This allows the information provision system according to this embodiment to provide quick and appropriate answers to customer questions.

[0030] The reception desk accepts questions from customers. For example, customers can input questions using smartphones or tablets. Specifically, the reception desk provides an interface for customers to input questions through a dedicated application or website. This allows customers to easily input and submit questions. The reception desk can also accept customer questions using voice input. For example, if a customer asks "What are today's sale items?", voice recognition technology is used to convert the question into text and input it into the system. The voice recognition technology employs noise cancellation and voice filtering to ensure accurate text conversion. Furthermore, the reception desk can also accept questions from customers at the in-store information counter. For example, if a customer uses a tablet at the information counter to input a question, the tablet has a dedicated question input application installed, making it easy for the customer to input their question. This allows the reception desk to accept questions regardless of the device or method the customer uses, improving customer convenience.

[0031] The input unit pre-enters answers to questions received by the reception unit. For example, managers and tenants of commercial facilities can input answers to customer questions into the platform. Specifically, managers and tenants can input and save answers to questions in text format through a dedicated management screen. The input unit can also automatically generate answers to customer questions using generative AI. The generative AI analyzes the intent of the question using natural language processing technology and generates the optimal answer based on the information entered into the platform. For example, in response to the question "What are today's sale items?", the generative AI searches the sale information entered into the platform and generates an appropriate answer. Furthermore, the input unit can periodically update answers to customer questions. For example, the input unit inputs daily sale information into the platform, and the generative AI provides answers based on that information. This allows the input unit to always provide answers based on the latest information, enabling accurate and timely information provision to customers.

[0032] The acquisition unit retrieves information entered by the input unit. For example, the acquisition unit can retrieve inventory information from a platform. Specifically, the acquisition unit can link with the commercial facility's inventory management system to retrieve inventory information in real time. This allows it to provide answers to customer inquiries about specific products based on the latest inventory status. The acquisition unit can also retrieve store layout information. For example, the acquisition unit can retrieve a store layout diagram from the platform and provide it to customers. The layout diagram shows details such as product placement and aisle locations, helping customers quickly find the products they are looking for. Furthermore, the acquisition unit can also retrieve service information and event information. For example, the acquisition unit can retrieve parking service information and event schedules from the platform. This allows it to provide accurate information to customers when they inquire about parking conditions or event times. The acquisition unit centrally manages this information and can link with other systems and departments as needed. This allows the acquisition unit to retrieve information efficiently and effectively, improving the overall system performance.

[0033] The information provision department provides answers to customers based on information acquired by the information acquisition department. For example, the information provision department can provide customers with information on the availability of products based on inventory information. Specifically, the information provision department analyzes the inventory information acquired from the information acquisition department and displays the availability of the product the customer inquired about in real time. The information provision department can also guide customers to the location of products based on store layout information. For example, in response to the question, "Which shelf is the boiled bamboo shoots on?", the information provision department will guide the customer to the location of the relevant shelf based on the store layout information. The information provision department can not only visually display the layout diagram but also provide information to customers through voice guidance and text messages. Furthermore, the information provision department can provide answers to customers based on service information and event information. For example, in response to the question, "How much do I need to spend to get a parking voucher?", the information provision department will answer the required purchase amount based on service information. By providing this information quickly and accurately, the information provision department can improve customer satisfaction. Furthermore, the information provision department can collect customer feedback and continuously improve the accuracy and effectiveness of the information it provides. This allows the service department to provide customers with quick and appropriate answers, thereby improving customer convenience.

[0034] The acquisition unit can acquire inventory information. For example, the acquisition unit can acquire product inventory information from a platform. For example, the acquisition unit can acquire the quantity and location of a product. The acquisition unit can also update inventory information in real time. For example, the acquisition unit can link with an inventory database to understand the inventory status of products in real time. Furthermore, the acquisition unit can provide answers to customers based on the inventory information. For example, the acquisition unit can provide an answer to the question, "Is this product in stock?" based on the inventory information. In this way, by acquiring inventory information, the system can provide customers with the latest inventory status. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or not using AI. For example, the acquisition unit can use an AI model to analyze the inventory database in order to acquire inventory information and obtain the latest inventory status.

[0035] The acquisition unit can acquire store layout information. For example, the acquisition unit can acquire store layout information from a platform. For example, the acquisition unit can acquire the placement of products and the location of aisles. The acquisition unit can also provide answers to customers based on the store layout information. For example, in response to the question, "Which shelf is the boiled bamboo shoots on?", the acquisition unit will guide the customer to the location of the relevant shelf based on the store layout information. Furthermore, the acquisition unit can update the store layout information in real time. For example, the acquisition unit can periodically update the store layout diagram to provide the latest information. In this way, by acquiring store layout information, it is possible to provide customers with guidance within the store. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or not using AI. For example, in order to acquire store layout information, the acquisition unit can use an AI model to analyze the store layout diagram and acquire the placement of products and the location of aisles.

[0036] The acquisition unit can acquire service information. For example, the acquisition unit can acquire service information from a platform. For example, the acquisition unit can acquire the types of services offered and details of those services. The acquisition unit can also provide answers to customers based on the service information. For example, in response to the question, "How much do I need to buy to get a parking voucher?", the acquisition unit will answer the required purchase amount based on the service information. Furthermore, the acquisition unit can update service information in real time. For example, the acquisition unit can periodically update the content and conditions of services to provide the latest information. In this way, by acquiring service information, the acquisition unit can provide customers with the latest service information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or not using AI. For example, the acquisition unit can use an AI model to analyze a service database in order to acquire service information and obtain the latest service information.

[0037] The acquisition unit can acquire event information. For example, the acquisition unit can acquire event information from a platform. For example, the acquisition unit can acquire the date, time, location, and content of an event. The acquisition unit can also provide answers to customers based on the event information. For example, the acquisition unit can provide an answer to the question, "What events are happening today?" based on the event information. Furthermore, the acquisition unit can update event information in real time. For example, the acquisition unit can periodically update the event schedule and content to provide the latest information. In this way, by acquiring event information, the acquisition unit can provide customers with the latest event information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or not using AI. For example, the acquisition unit can use an AI model to analyze an event database in order to acquire event information and obtain the latest event information.

[0038] The service provider can provide answers to customers based on acquired inventory information. For example, the service provider can provide customers with information on the availability of a product based on the inventory information. For example, in response to the question, "Is this product in stock?", the service provider can provide an answer based on the inventory information. The service provider can also provide information on the expected arrival date of a product based on the inventory information. For example, in response to the question, "When will this product be in stock?", the service provider can provide an answer on the expected arrival date based on the inventory information. Furthermore, the service provider can also provide information on the reservation status of a product based on the inventory information. For example, in response to the question, "Can I reserve this product?", the service provider can provide an answer on the reservation status based on the inventory information. In this way, by providing answers to customers based on inventory information, the service provider can convey accurate inventory status to customers. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can acquire inventory information, analyze the inventory status using an AI model, and provide it to the customer.

[0039] The service provider can provide answers to customers based on the acquired store layout information. For example, the service provider can guide customers to the location of products based on the store layout information. For example, in response to the question, "Which shelf is the boiled bamboo shoots on?", the service provider can guide customers to the location of the relevant shelf based on the store layout information. The service provider can also provide a store map based on the store layout information. For example, the service provider can provide a map to customers based on the store layout diagram. Furthermore, the service provider can also guide customers to the placement of products based on the store layout information. For example, in response to the question, "Where is this product?", the service provider can guide customers to the placement of the product based on the store layout information. In this way, by providing answers to customers based on the store layout information, accurate directions within the store can be conveyed to customers. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can acquire store layout information, analyze it using an AI model, and provide it to the customer.

[0040] The service provider can provide answers to customers based on the acquired service information. For example, the service provider can provide customers with the latest service information based on the service information. For example, in response to the question, "How much do I need to spend to get a parking voucher?", the service provider can answer with the required purchase amount based on the service information. The service provider can also provide details about the services offered based on the service information. For example, in response to the question, "How can I use this service?", the service provider can provide instructions on how to use it based on the service information. Furthermore, the service provider can also provide information on the conditions for providing the service based on the service information. For example, in response to the question, "What are the conditions for using this service?", the service provider can provide information on the conditions for providing the service based on the service information. In this way, by providing answers to customers based on the service information, the service provider can accurately convey the latest service information to customers. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can acquire service information, analyze it using an AI model, and provide it to the customer.

[0041] The service provider can provide answers to customers based on the acquired event information. For example, the service provider can provide customers with the latest event information based on the event information. For example, in response to the question, "What is happening today?", the service provider can provide an answer based on the event information. The service provider can also provide details about events based on the event information. For example, in response to the question, "Where is this event being held?", the service provider can provide the venue based on the event information. Furthermore, the service provider can also provide the event schedule based on the event information. For example, in response to the question, "What time does this event start?", the service provider can provide the schedule based on the event information. In this way, by providing answers to customers based on event information, the service provider can accurately convey the latest event information to customers. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can acquire event information, analyze it using an AI model, and provide it to customers.

[0042] The reception desk can analyze the customer's past question history when receiving a question and select the most suitable reception method. For example, the reception desk can automatically display as suggestions the types of questions the customer has frequently asked in the past. The reception desk can also prioritize suggesting question methods (voice, text, etc.) that the customer has used in the past. Furthermore, the reception desk can predict and suggest the types of questions the customer will use at specific times based on their past question history. In this way, by analyzing the customer's past question history, the reception desk can provide the most suitable question reception method. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can acquire the customer's past question history, analyze it using an AI model, and select the most suitable reception method.

[0043] The reception desk can filter questions based on the customer's current situation and areas of interest when they are received. For example, the reception desk can prioritize displaying relevant questions based on the customer's current location. It can also filter and display relevant questions based on the customer's areas of interest. Furthermore, the reception desk can suggest appropriate questions based on the customer's current situation (e.g., time of day or weather). This allows for priority reception of highly relevant questions by filtering them based on the customer's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can acquire the customer's current situation and areas of interest, filter them using an AI model, and then display the questions.

[0044] The reception desk can prioritize receiving questions that are highly relevant, taking into account the customer's geographical location. For example, the reception desk can prioritize receiving questions based on the customer's current location. It can also prioritize receiving questions about nearby stores and facilities based on the customer's geographical location. Furthermore, the reception desk can prioritize receiving questions about region-specific information, taking into account the customer's geographical location. In this way, by considering the customer's geographical location, highly relevant questions can be prioritized. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can acquire the customer's geographical location, use an AI model to select highly relevant questions, and receive them.

[0045] The reception desk can analyze the customer's social media activity when receiving a question and accept relevant questions. For example, the reception desk can suggest questions based on the customer's interests from their social media activity. The reception desk can also analyze the customer's social media posts and prioritize accepting relevant questions. Furthermore, the reception desk can suggest relevant questions by referring to the activity of the customer's followers and friends on social media. In this way, by analyzing the customer's social media activity, relevant questions can be prioritized. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can acquire the customer's social media activity, analyze it using an AI model, select relevant questions, and accept them.

[0046] The input unit can adjust the level of detail in the input based on the importance of the question when entering an answer. For example, the input unit will input a detailed answer for important questions. It can also input a concise answer for general questions. Furthermore, it can input a quick answer for urgent questions. This allows for detailed answers to important questions by adjusting the level of detail based on the importance of the question. Some or all of the above processing in the input unit may be performed using AI, for example, or not. For example, the input unit can evaluate the importance of the question and adjust the level of detail using an AI model.

[0047] The input unit can apply different input algorithms depending on the question category when inputting answers. For example, for questions about products, the input unit can input answers by referring to the product database. It can also input answers for questions about services by referring to service information. Furthermore, for questions about events, the input unit can input answers by referring to event information. This allows for the provision of appropriate answers by applying different input algorithms depending on the question category. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can classify the question category and apply an appropriate input algorithm using an AI model.

[0048] The input unit can prioritize inputs based on when the questions were submitted when entering answers. For example, the input unit may prioritize answering recently submitted questions. It can also prioritize answering questions submitted within a specific time period. Furthermore, it can prioritize answering urgent questions. This allows for the rapid provision of answers by prioritizing inputs based on when the questions were submitted. Some or all of the above processing in the input unit may be performed using AI, for example, or not. For example, the input unit may evaluate when the questions were submitted and use an AI model to determine the priority of the inputs.

[0049] The input unit can adjust the order of inputs based on the relevance of the questions when inputting answers. For example, the input unit may prioritize answering questions related to the customer's current situation. It can also prioritize answering questions related to the customer's areas of interest. Furthermore, it can prioritize answering questions related to the customer's past question history. By adjusting the order of inputs based on the relevance of the questions, it is possible to provide preferential answers to highly relevant questions. Some or all of the above processing in the input unit may be performed using AI, for example, or not. For example, the input unit may evaluate the relevance of the questions and adjust the order of inputs using an AI model.

[0050] The acquisition unit can select the optimal acquisition method by referring to past acquired data when acquiring information. For example, the acquisition unit may prioritize acquiring information that has been frequently acquired in the past. The acquisition unit can also analyze past acquired data and select the optimal acquisition method. Furthermore, the acquisition unit can predict and acquire information to be acquired during a specific time period based on past acquired data. This allows the acquisition unit to provide the optimal information acquisition method by referring to past acquired data. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can acquire past acquired data, analyze it using an AI model, and select the optimal acquisition method.

[0051] The acquisition unit can apply different acquisition methods to each category of information when acquiring it. For example, when acquiring product information, the acquisition unit can refer to a product database. Similarly, when acquiring service information, the acquisition unit can refer to a service database. Furthermore, when acquiring event information, the acquisition unit can refer to an event database. This allows for the provision of accurate information by applying the appropriate acquisition method according to the information category. Some or all of the above-described processes in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can classify the information categories and apply the appropriate acquisition method using an AI model.

[0052] The information acquisition unit can determine the priority of information acquisition based on the timing of information submission. For example, the acquisition unit may prioritize the acquisition of recently submitted information. It can also prioritize the acquisition of information submitted during a specific time period. Furthermore, it can prioritize the acquisition of information of high urgency. This allows for the rapid provision of information by determining the priority of acquisition based on the timing of information submission. Some or all of the above-described processes in the acquisition unit may be performed using AI, for example, or not. For example, the acquisition unit may evaluate the timing of information submission and determine the priority of acquisition using an AI model.

[0053] The data acquisition unit can adjust the order of data acquisition based on the relevance of the information. For example, the data acquisition unit may prioritize acquiring information related to the customer's current situation. It can also prioritize acquiring information related to the customer's areas of interest. Furthermore, it can prioritize acquiring information related to the customer's past question history. By adjusting the order of data acquisition based on the relevance of the information, it is possible to prioritize the acquisition of highly relevant information. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit may evaluate the relevance of the information and adjust the order of data acquisition using an AI model.

[0054] The service provider can select the optimal delivery method by referring to the customer's past question history when providing an answer. For example, the service provider can provide the optimal answer based on the questions the customer has frequently asked in the past. The service provider can also analyze the customer's past question history and select the optimal delivery method. Furthermore, the service provider can predict and provide answers to be delivered at specific times based on the customer's past question history. In this way, the service provider can provide the optimal answer delivery method by referring to the customer's past question history. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can acquire the customer's past question history, analyze it using an AI model, and select the optimal delivery method.

[0055] The service provider can customize the means of providing answers based on the customer's current situation. For example, if the customer is in a store, the service provider can provide a store map. If the customer is asking a question online, the service provider can also provide a detailed answer including links and images. Furthermore, if the customer is in a hurry, the service provider can provide a concise and quick answer. This allows for the provision of appropriate answers by customizing the means of providing answers based on the customer's current situation. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can acquire the customer's current situation and customize the means of providing answers using an AI model.

[0056] The service provider can select the optimal delivery method when providing responses, taking into account the customer's geographical location. For example, the service provider can provide relevant information based on the customer's current location. It can also provide information about nearby stores and facilities based on the customer's geographical location. Furthermore, the service provider can provide region-specific information, taking into account the customer's geographical location. This allows for the provision of highly relevant information by considering the customer's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can acquire the customer's geographical location and select the optimal delivery method using an AI model.

[0057] The service provider can analyze the customer's social media activity and propose a means of providing information when providing responses. For example, the service provider can provide information based on topics of interest from the customer's social media activity. The service provider can also analyze the content of the customer's social media posts and provide relevant information. Furthermore, the service provider can provide relevant information by referring to the activities of the customer's followers and friends on social media. In this way, relevant information can be provided by analyzing the customer's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can acquire the customer's social media activity, analyze it using an AI model, and propose a means of providing information.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The data acquisition unit can select the optimal acquisition method by referring to past data acquisitions when acquiring information. For example, it can prioritize acquiring information that has been acquired frequently in the past. It can also analyze past data acquisitions to select the optimal acquisition method. Furthermore, it can predict and acquire information to be acquired at a specific time period based on past data acquisitions. In this way, by referring to past data acquisitions, the optimal information acquisition method can be provided. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can acquire past data, analyze it using an AI model, and select the optimal acquisition method.

[0060] The service provider can select the optimal delivery method by referring to the customer's past question history when providing answers. For example, it can provide the optimal answer based on the questions the customer has frequently asked in the past. It can also analyze the customer's past question history and select the optimal delivery method. Furthermore, it can predict and provide answers to be delivered at specific times based on the customer's past question history. In this way, the service provider can provide the optimal answer delivery method by referring to the customer's past question history. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can acquire the customer's past question history, analyze it using an AI model, and select the optimal delivery method.

[0061] The reception desk can filter questions based on the customer's current situation and areas of interest when receiving them. For example, it can prioritize displaying relevant questions based on the customer's current location. It can also filter and display relevant questions based on the customer's areas of interest. Furthermore, it can suggest appropriate questions based on the customer's current situation (e.g., time of day or weather). This allows for priority reception of highly relevant questions by filtering them based on the customer's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can acquire the customer's current situation and areas of interest, filter them using an AI model, and then display the questions.

[0062] The input unit can apply different input algorithms depending on the question category when inputting answers. For example, for questions about products, it can input answers by referring to the product database. For questions about services, it can input answers by referring to service information. Furthermore, for questions about events, it can input answers by referring to event information. By applying different input algorithms depending on the question category, appropriate answers can be provided. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can classify the question category and apply an appropriate input algorithm using an AI model.

[0063] The acquisition unit can apply different acquisition methods to each category of information when acquiring it. For example, when acquiring product information, it can refer to the product database. When acquiring service information, it can also refer to the service database. Furthermore, when acquiring event information, it can also refer to the event database. This allows for the provision of accurate information by applying the appropriate acquisition method according to the category of information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can classify the categories of information and apply the appropriate acquisition method using an AI model.

[0064] The input unit can prioritize inputs based on when the questions were submitted. For example, it can prioritize answering recently submitted questions. It can also prioritize answering questions submitted within a specific time period. Furthermore, it can prioritize answering urgent questions. This allows for quicker responses by prioritizing inputs based on when the questions were submitted. Some or all of the above processing in the input unit may be performed using AI, for example, or not. For example, the input unit can evaluate when the questions were submitted and use an AI model to determine the priority of the inputs.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk receives questions from customers. For example, customers can enter their questions using a smartphone or tablet. The reception desk can also accept questions using voice input. For example, if a customer asks by voice, "What are today's sale items?", the reception desk will accept the question. Furthermore, customers can also enter their questions at the information counter within the store. For example, a customer can enter their question using a tablet at the information counter. Step 2: The input unit pre-enters answers to questions received by the reception unit. For example, the input unit allows commercial facility managers or tenants to input answers to customer questions into the platform. The input unit can also automatically generate answers to customer questions using generative AI. For example, the generative AI can generate an answer to the question "What are today's sale items?" based on the information entered into the platform. Furthermore, the input unit can periodically update the answers to customer questions. For example, the input unit can input daily sale information into the platform, and the generative AI can provide answers based on that information. Step 3: The acquisition unit retrieves the information entered by the input unit. For example, the acquisition unit can retrieve inventory information from the platform. The acquisition unit can also retrieve store layout information. For example, the acquisition unit can retrieve a store layout diagram from the platform and provide it to the customer. Furthermore, the acquisition unit can also retrieve service information and event information. For example, the acquisition unit can retrieve parking service information and event schedules from the platform. Step 4: The service department provides answers to customers based on the information acquired by the information acquisition department. For example, the service department can provide customers with information on the availability of products based on inventory information. The service department can also guide customers to the location of products based on store layout information. For example, in response to the question, "Which shelf is the boiled bamboo shoots on?", the service department will guide customers to the location of the relevant shelf based on the store layout information. Furthermore, the service department can also provide answers to customers based on service information and event information. For example, in response to the question, "How much do I need to spend to get a parking voucher?", the service department will answer with the required purchase amount based on service information.

[0067] (Example of form 2) The information provision system according to an embodiment of the present invention is a system that provides a platform for commercial facility managers and tenants to proactively provide information requested by customers, and provides customers with a generating AI that uses this platform. This information provision system is a mechanism that improves customer convenience and satisfaction, and contributes to reducing labor costs for facilities and tenants. Specifically, it consists of the following steps. First, commercial facility managers and tenants use a platform in which they pre-enter answers to customer questions. Next, the generating AI retrieves information from this platform and provides appropriate answers to customer questions. This eliminates the need for customers to search for store staff and allows them to quickly obtain the necessary information. For example, if a customer asks, "What are the sale items today?", the generating AI provides a list of current sale items based on the information entered into the platform. Also, if a customer asks, "Which shelf is the boiled bamboo shoots on?", the generating AI guides them to the location of the relevant shelf based on the store layout information. Furthermore, if a customer asks, "How much do I need to spend to get a parking voucher?", the generating AI answers the required purchase amount based on service information. This platform can be used in various locations such as commercial facilities, theme parks, and event venues. For example, a theme park could provide information such as attraction wait times and show schedules. An event venue could provide information on booth locations and ongoing events. This system not only improves customer convenience and satisfaction but also contributes to reducing labor costs for facilities and tenants. For instance, even with limited staff available for customer service, the AI ​​can handle customer interactions, enabling more efficient operations. It also facilitates understanding customer needs, aiding in marketing strategy development. Furthermore, by registering an automated information retrieval URL, the platform can automatically update inventory and event information, ensuring that the latest information is always available. For example, by linking with an inventory database, the system can track product stock levels in real time and provide this information to customers.In this way, by providing a platform that allows commercial facility managers and tenants to proactively provide information requested by customers, and by providing customers with a generative AI that uses this platform, it is possible to improve customer convenience and satisfaction, and contribute to reducing labor costs for facilities and tenants. As a result, the information provision system can provide quick and appropriate answers to customer questions.

[0068] The information provision system according to this embodiment comprises a reception unit, an input unit, an acquisition unit, and a provision unit. The reception unit receives questions from customers. For example, customers can input questions using a smartphone or tablet. The reception unit can also receive questions from customers using voice input. For example, if a customer asks "What are today's sale items?" by voice, the reception unit will receive the question. Furthermore, customers can also input questions at the information counter within the store. For example, a customer can input a question using a tablet at the information counter. The input unit pre-enters answers to questions received by the reception unit. For example, the input unit can allow a commercial facility manager or tenant to input answers to customer questions into the platform. The input unit can also automatically generate answers to customer questions using a generation AI. For example, the generation AI generates an answer to the question "What are today's sale items?" based on the information entered into the platform. Furthermore, the input unit can periodically update the answers to customer questions. For example, the input unit inputs daily sale information into the platform, and the generation AI provides answers based on that information. The acquisition unit acquires information entered by the input unit. For example, the acquisition unit can acquire inventory information from a platform. The acquisition unit can also acquire store layout information. For example, the acquisition unit can acquire a store layout diagram from a platform and provide it to the customer. Furthermore, the acquisition unit can acquire service information and event information. For example, the acquisition unit can acquire parking service information and event schedules from a platform. The provision unit provides answers to customers based on the information acquired by the acquisition unit. For example, the provision unit can provide customers with information on the availability of products based on inventory information. Furthermore, the provision unit can guide customers to the location of products based on store layout information. For example, in response to the question, "Which shelf is the boiled bamboo shoots on?", the provision unit will guide the customer to the location of the relevant shelf based on store layout information. Furthermore, the provision unit can also provide answers to customers based on service information and event information.For example, in response to the question, "How much do I need to spend to receive a parking service voucher?", the information provision unit will answer the required purchase amount based on the service information. This allows the information provision system according to this embodiment to provide quick and appropriate answers to customer questions.

[0069] The reception desk accepts questions from customers. For example, customers can input questions using smartphones or tablets. Specifically, the reception desk provides an interface for customers to input questions through a dedicated application or website. This allows customers to easily input and submit questions. The reception desk can also accept customer questions using voice input. For example, if a customer asks "What are today's sale items?", voice recognition technology is used to convert the question into text and input it into the system. The voice recognition technology employs noise cancellation and voice filtering to ensure accurate text conversion. Furthermore, the reception desk can also accept questions from customers at the in-store information counter. For example, if a customer uses a tablet at the information counter to input a question, the tablet has a dedicated question input application installed, making it easy for the customer to input their question. This allows the reception desk to accept questions regardless of the device or method the customer uses, improving customer convenience.

[0070] The input unit pre-enters answers to questions received by the reception unit. For example, managers and tenants of commercial facilities can input answers to customer questions into the platform. Specifically, managers and tenants can input and save answers to questions in text format through a dedicated management screen. The input unit can also automatically generate answers to customer questions using generative AI. The generative AI analyzes the intent of the question using natural language processing technology and generates the optimal answer based on the information entered into the platform. For example, in response to the question "What are today's sale items?", the generative AI searches the sale information entered into the platform and generates an appropriate answer. Furthermore, the input unit can periodically update answers to customer questions. For example, the input unit inputs daily sale information into the platform, and the generative AI provides answers based on that information. This allows the input unit to always provide answers based on the latest information, enabling accurate and timely information provision to customers.

[0071] The acquisition unit retrieves information entered by the input unit. For example, the acquisition unit can retrieve inventory information from a platform. Specifically, the acquisition unit can link with the commercial facility's inventory management system to retrieve inventory information in real time. This allows it to provide answers to customer inquiries about specific products based on the latest inventory status. The acquisition unit can also retrieve store layout information. For example, the acquisition unit can retrieve a store layout diagram from the platform and provide it to customers. The layout diagram shows details such as product placement and aisle locations, helping customers quickly find the products they are looking for. Furthermore, the acquisition unit can also retrieve service information and event information. For example, the acquisition unit can retrieve parking service information and event schedules from the platform. This allows it to provide accurate information to customers when they inquire about parking conditions or event times. The acquisition unit centrally manages this information and can link with other systems and departments as needed. This allows the acquisition unit to retrieve information efficiently and effectively, improving the overall system performance.

[0072] The information provision department provides answers to customers based on information acquired by the information acquisition department. For example, the information provision department can provide customers with information on the availability of products based on inventory information. Specifically, the information provision department analyzes the inventory information acquired from the information acquisition department and displays the availability of the product the customer inquired about in real time. The information provision department can also guide customers to the location of products based on store layout information. For example, in response to the question, "Which shelf is the boiled bamboo shoots on?", the information provision department will guide the customer to the location of the relevant shelf based on the store layout information. The information provision department can not only visually display the layout diagram but also provide information to customers through voice guidance and text messages. Furthermore, the information provision department can provide answers to customers based on service information and event information. For example, in response to the question, "How much do I need to spend to get a parking voucher?", the information provision department will answer the required purchase amount based on service information. By providing this information quickly and accurately, the information provision department can improve customer satisfaction. Furthermore, the information provision department can collect customer feedback and continuously improve the accuracy and effectiveness of the information it provides. This allows the service department to provide customers with quick and appropriate answers, thereby improving customer convenience.

[0073] The acquisition unit can acquire inventory information. For example, the acquisition unit can acquire product inventory information from a platform. For example, the acquisition unit can acquire the quantity and location of a product. The acquisition unit can also update inventory information in real time. For example, the acquisition unit can link with an inventory database to understand the inventory status of products in real time. Furthermore, the acquisition unit can provide answers to customers based on the inventory information. For example, the acquisition unit can provide an answer to the question, "Is this product in stock?" based on the inventory information. In this way, by acquiring inventory information, the system can provide customers with the latest inventory status. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or not using AI. For example, the acquisition unit can use an AI model to analyze the inventory database in order to acquire inventory information and obtain the latest inventory status.

[0074] The acquisition unit can acquire store layout information. For example, the acquisition unit can acquire store layout information from a platform. For example, the acquisition unit can acquire the placement of products and the location of aisles. The acquisition unit can also provide answers to customers based on the store layout information. For example, in response to the question, "Which shelf is the boiled bamboo shoots on?", the acquisition unit will guide the customer to the location of the relevant shelf based on the store layout information. Furthermore, the acquisition unit can update the store layout information in real time. For example, the acquisition unit can periodically update the store layout diagram to provide the latest information. In this way, by acquiring store layout information, it is possible to provide customers with guidance within the store. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or not using AI. For example, in order to acquire store layout information, the acquisition unit can use an AI model to analyze the store layout diagram and acquire the placement of products and the location of aisles.

[0075] The acquisition unit can acquire service information. For example, the acquisition unit can acquire service information from a platform. For example, the acquisition unit can acquire the types of services offered and details of those services. The acquisition unit can also provide answers to customers based on the service information. For example, in response to the question, "How much do I need to buy to get a parking voucher?", the acquisition unit will answer the required purchase amount based on the service information. Furthermore, the acquisition unit can update service information in real time. For example, the acquisition unit can periodically update the content and conditions of services to provide the latest information. In this way, by acquiring service information, the acquisition unit can provide customers with the latest service information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or not using AI. For example, the acquisition unit can use an AI model to analyze a service database in order to acquire service information and obtain the latest service information.

[0076] The acquisition unit can acquire event information. For example, the acquisition unit can acquire event information from a platform. For example, the acquisition unit can acquire the date, time, location, and content of an event. The acquisition unit can also provide answers to customers based on the event information. For example, the acquisition unit can provide an answer to the question, "What events are happening today?" based on the event information. Furthermore, the acquisition unit can update event information in real time. For example, the acquisition unit can periodically update the event schedule and content to provide the latest information. In this way, by acquiring event information, the acquisition unit can provide customers with the latest event information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or not using AI. For example, the acquisition unit can use an AI model to analyze an event database in order to acquire event information and obtain the latest event information.

[0077] The service provider can provide answers to customers based on acquired inventory information. For example, the service provider can provide customers with information on the availability of a product based on the inventory information. For example, in response to the question, "Is this product in stock?", the service provider can provide an answer based on the inventory information. The service provider can also provide information on the expected arrival date of a product based on the inventory information. For example, in response to the question, "When will this product be in stock?", the service provider can provide an answer on the expected arrival date based on the inventory information. Furthermore, the service provider can also provide information on the reservation status of a product based on the inventory information. For example, in response to the question, "Can I reserve this product?", the service provider can provide an answer on the reservation status based on the inventory information. In this way, by providing answers to customers based on inventory information, the service provider can convey accurate inventory status to customers. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can acquire inventory information, analyze the inventory status using an AI model, and provide it to the customer.

[0078] The service provider can provide answers to customers based on the acquired store layout information. For example, the service provider can guide customers to the location of products based on the store layout information. For example, in response to the question, "Which shelf is the boiled bamboo shoots on?", the service provider can guide customers to the location of the relevant shelf based on the store layout information. The service provider can also provide a store map based on the store layout information. For example, the service provider can provide a map to customers based on the store layout diagram. Furthermore, the service provider can also guide customers to the placement of products based on the store layout information. For example, in response to the question, "Where is this product?", the service provider can guide customers to the placement of the product based on the store layout information. In this way, by providing answers to customers based on the store layout information, accurate directions within the store can be conveyed to customers. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can acquire store layout information, analyze it using an AI model, and provide it to the customer.

[0079] The service provider can provide answers to customers based on the acquired service information. For example, the service provider can provide customers with the latest service information based on the service information. For example, in response to the question, "How much do I need to spend to get a parking voucher?", the service provider can answer with the required purchase amount based on the service information. The service provider can also provide details about the services offered based on the service information. For example, in response to the question, "How can I use this service?", the service provider can provide instructions on how to use it based on the service information. Furthermore, the service provider can also provide information on the conditions for providing the service based on the service information. For example, in response to the question, "What are the conditions for using this service?", the service provider can provide information on the conditions for providing the service based on the service information. In this way, by providing answers to customers based on the service information, the service provider can accurately convey the latest service information to customers. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can acquire service information, analyze it using an AI model, and provide it to the customer.

[0080] The service provider can provide answers to customers based on the acquired event information. For example, the service provider can provide customers with the latest event information based on the event information. For example, in response to the question, "What is happening today?", the service provider can provide an answer based on the event information. The service provider can also provide details about events based on the event information. For example, in response to the question, "Where is this event being held?", the service provider can provide the venue based on the event information. Furthermore, the service provider can also provide the event schedule based on the event information. For example, in response to the question, "What time does this event start?", the service provider can provide the schedule based on the event information. In this way, by providing answers to customers based on event information, the service provider can accurately convey the latest event information to customers. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can acquire event information, analyze it using an AI model, and provide it to customers.

[0081] The reception desk can estimate the customer's emotions and adjust the way questions are answered based on the estimated emotions. For example, if the customer is stressed, the reception desk can provide a simple interface and minimize the steps required to enter questions. If the customer is relaxed, the reception desk can also provide detailed input options and suggest a customizable way to answer questions. Furthermore, if the customer is in a hurry, the reception desk can prioritize voice input to quickly answer questions. This allows for optimal question answering for the customer by adjusting the way questions are answered according to their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines 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 reception desk may be performed using AI or not. For example, the reception desk can acquire customer emotion data, estimate emotions using generative AI, and adjust the way questions are answered.

[0082] The reception desk can analyze the customer's past question history when receiving a question and select the most suitable reception method. For example, the reception desk can automatically display as suggestions the types of questions the customer has frequently asked in the past. The reception desk can also prioritize suggesting question methods (voice, text, etc.) that the customer has used in the past. Furthermore, the reception desk can predict and suggest the types of questions the customer will use at specific times based on their past question history. In this way, by analyzing the customer's past question history, the reception desk can provide the most suitable question reception method. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can acquire the customer's past question history, analyze it using an AI model, and select the most suitable reception method.

[0083] The reception desk can filter questions based on the customer's current situation and areas of interest when they are received. For example, the reception desk can prioritize displaying relevant questions based on the customer's current location. It can also filter and display relevant questions based on the customer's areas of interest. Furthermore, the reception desk can suggest appropriate questions based on the customer's current situation (e.g., time of day or weather). This allows for priority reception of highly relevant questions by filtering them based on the customer's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can acquire the customer's current situation and areas of interest, filter them using an AI model, and then display the questions.

[0084] The reception desk can estimate the customer's emotions and prioritize the questions to be answered based on the estimated emotions. For example, if the customer is nervous, the reception desk may prioritize important questions. If the customer is relaxed, the reception desk may also prioritize detailed questions. Furthermore, if the customer is in a hurry, the reception desk may prioritize questions that require a quick answer. This allows for prioritizing important questions by determining the priority of questions 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 desk may be performed using AI or not. For example, the reception desk can acquire customer emotion data, estimate emotions using generative AI, and determine the priority of questions.

[0085] The reception desk can prioritize receiving questions that are highly relevant, taking into account the customer's geographical location. For example, the reception desk can prioritize receiving questions based on the customer's current location. It can also prioritize receiving questions about nearby stores and facilities based on the customer's geographical location. Furthermore, the reception desk can prioritize receiving questions about region-specific information, taking into account the customer's geographical location. In this way, by considering the customer's geographical location, highly relevant questions can be prioritized. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can acquire the customer's geographical location, use an AI model to select highly relevant questions, and receive them.

[0086] The reception desk can analyze the customer's social media activity when receiving a question and accept relevant questions. For example, the reception desk can suggest questions based on the customer's interests from their social media activity. The reception desk can also analyze the customer's social media posts and prioritize accepting relevant questions. Furthermore, the reception desk can suggest relevant questions by referring to the activity of the customer's followers and friends on social media. In this way, by analyzing the customer's social media activity, relevant questions can be prioritized. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can acquire the customer's social media activity, analyze it using an AI model, select relevant questions, and accept them.

[0087] The input unit can estimate the customer's emotions and adjust the input method based on the estimated emotions. For example, if the customer is stressed, the input unit can provide a simple interface and minimize the input steps. If the customer is relaxed, the input unit can also provide detailed input options and suggest a customizable input method. Furthermore, if the customer is in a hurry, the input unit can prioritize voice input to allow for quick input of responses. This allows for optimal response input for the customer by adjusting the input method according to their 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 input unit may be performed using AI or not. For example, the input unit can acquire customer emotion data, estimate emotions using generative AI, and adjust the input method for responses.

[0088] The input unit can adjust the level of detail in the input based on the importance of the question when entering an answer. For example, the input unit will input a detailed answer for important questions. It can also input a concise answer for general questions. Furthermore, it can input a quick answer for urgent questions. This allows for detailed answers to important questions by adjusting the level of detail based on the importance of the question. Some or all of the above processing in the input unit may be performed using AI, for example, or not. For example, the input unit can evaluate the importance of the question and adjust the level of detail using an AI model.

[0089] The input unit can apply different input algorithms depending on the question category when inputting answers. For example, for questions about products, the input unit can input answers by referring to the product database. It can also input answers for questions about services by referring to service information. Furthermore, for questions about events, the input unit can input answers by referring to event information. This allows for the provision of appropriate answers by applying different input algorithms depending on the question category. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can classify the question category and apply an appropriate input algorithm using an AI model.

[0090] The input unit can estimate the customer's emotions and adjust the length of the response based on the estimated emotions. For example, if the customer is in a hurry, the input unit will input a short, concise response. If the customer is relaxed, the input unit can input a longer response that includes detailed explanations. Furthermore, if the customer is excited, the input unit can input a response with visually stimulating effects. This allows the system to provide the most appropriate response for the customer by adjusting the response length according to their 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 input unit may be performed using AI or not. For example, the input unit can acquire customer emotion data, estimate emotions using generative AI, and adjust the response length.

[0091] The input unit can prioritize inputs based on when the questions were submitted when entering answers. For example, the input unit may prioritize answering recently submitted questions. It can also prioritize answering questions submitted within a specific time period. Furthermore, it can prioritize answering urgent questions. This allows for the rapid provision of answers by prioritizing inputs based on when the questions were submitted. Some or all of the above processing in the input unit may be performed using AI, for example, or not. For example, the input unit may evaluate when the questions were submitted and use an AI model to determine the priority of the inputs.

[0092] The input unit can adjust the order of inputs based on the relevance of the questions when inputting answers. For example, the input unit may prioritize answering questions related to the customer's current situation. It can also prioritize answering questions related to the customer's areas of interest. Furthermore, it can prioritize answering questions related to the customer's past question history. By adjusting the order of inputs based on the relevance of the questions, it is possible to provide preferential answers to highly relevant questions. Some or all of the above processing in the input unit may be performed using AI, for example, or not. For example, the input unit may evaluate the relevance of the questions and adjust the order of inputs using an AI model.

[0093] The acquisition unit can estimate the customer's emotions and adjust the information acquisition method based on the estimated emotions. For example, if the customer is stressed, the acquisition unit can provide a simple interface and minimize the information acquisition procedure. If the customer is relaxed, the acquisition unit can also provide detailed information acquisition options and suggest a customizable acquisition method. Furthermore, if the customer is in a hurry, the acquisition unit can enable rapid information acquisition. This allows for optimal information acquisition for the customer by adjusting the information acquisition method according to their 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 acquisition unit may be performed using AI or not. For example, the acquisition unit can acquire customer emotion data, estimate emotions using generative AI, and adjust the information acquisition method.

[0094] The acquisition unit can select the optimal acquisition method by referring to past acquired data when acquiring information. For example, the acquisition unit may prioritize acquiring information that has been frequently acquired in the past. The acquisition unit can also analyze past acquired data and select the optimal acquisition method. Furthermore, the acquisition unit can predict and acquire information to be acquired during a specific time period based on past acquired data. This allows the acquisition unit to provide the optimal information acquisition method by referring to past acquired data. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can acquire past acquired data, analyze it using an AI model, and select the optimal acquisition method.

[0095] The acquisition unit can apply different acquisition methods to each category of information when acquiring it. For example, when acquiring product information, the acquisition unit can refer to a product database. Similarly, when acquiring service information, the acquisition unit can refer to a service database. Furthermore, when acquiring event information, the acquisition unit can refer to an event database. This allows for the provision of accurate information by applying the appropriate acquisition method according to the information category. Some or all of the above-described processes in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can classify the information categories and apply the appropriate acquisition method using an AI model.

[0096] The data acquisition unit can estimate the customer's emotions and determine the priority of information to acquire based on the estimated emotions. For example, if the customer is nervous, the data acquisition unit will prioritize acquiring important information. If the customer is relaxed, the data acquisition unit can also prioritize acquiring detailed information. Furthermore, if the customer is in a hurry, the data acquisition unit can prioritize acquiring information that can be retrieved quickly. In this way, by prioritizing information according to the customer's emotions, important information can be acquired preferentially. 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 data acquisition unit may be performed using AI, for example, or not using AI. For example, the data acquisition unit can acquire customer emotion data, estimate emotions using generative AI, and determine the priority of information.

[0097] The information acquisition unit can determine the priority of information acquisition based on the timing of information submission. For example, the acquisition unit may prioritize the acquisition of recently submitted information. It can also prioritize the acquisition of information submitted during a specific time period. Furthermore, it can prioritize the acquisition of information of high urgency. This allows for the rapid provision of information by determining the priority of acquisition based on the timing of information submission. Some or all of the above-described processes in the acquisition unit may be performed using AI, for example, or not. For example, the acquisition unit may evaluate the timing of information submission and determine the priority of acquisition using an AI model.

[0098] The data acquisition unit can adjust the order of data acquisition based on the relevance of the information. For example, the data acquisition unit may prioritize acquiring information related to the customer's current situation. It can also prioritize acquiring information related to the customer's areas of interest. Furthermore, it can prioritize acquiring information related to the customer's past question history. By adjusting the order of data acquisition based on the relevance of the information, it is possible to prioritize the acquisition of highly relevant information. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit may evaluate the relevance of the information and adjust the order of data acquisition using an AI model.

[0099] The service provider can estimate the customer's emotions and adjust the way it provides answers based on those emotions. For example, if the customer is stressed, the service provider can provide a simple interface and minimize the steps involved in providing answers. If the customer is relaxed, the service provider can also provide detailed answer options and suggest a customizable delivery method. Furthermore, if the customer is in a hurry, the service provider can provide answers quickly. This allows for the provision of the best possible answers for the customer by adjusting the delivery method according to their 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 service provider may be performed using AI or not. For example, the service provider can acquire customer emotion data, estimate emotions using generative AI, and adjust the way it provides answers.

[0100] The service provider can select the optimal delivery method by referring to the customer's past question history when providing an answer. For example, the service provider can provide the optimal answer based on the questions the customer has frequently asked in the past. The service provider can also analyze the customer's past question history and select the optimal delivery method. Furthermore, the service provider can predict and provide answers to be delivered at specific times based on the customer's past question history. In this way, the service provider can provide the optimal answer delivery method by referring to the customer's past question history. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can acquire the customer's past question history, analyze it using an AI model, and select the optimal delivery method.

[0101] The service provider can customize the means of providing answers based on the customer's current situation. For example, if the customer is in a store, the service provider can provide a store map. If the customer is asking a question online, the service provider can also provide a detailed answer including links and images. Furthermore, if the customer is in a hurry, the service provider can provide a concise and quick answer. This allows for the provision of appropriate answers by customizing the means of providing answers based on the customer's current situation. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can acquire the customer's current situation and customize the means of providing answers using an AI model.

[0102] The service provider can estimate the customer's emotions and prioritize responses based on those emotions. For example, if the customer is nervous, the service provider may prioritize providing important answers. Similarly, if the customer is relaxed, the service provider may prioritize providing detailed answers. Furthermore, if the customer is in a hurry, the service provider may prioritize providing answers that can be delivered quickly. This allows for the prioritization of important answers by determining response priorities 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 may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can acquire customer emotion data, estimate emotions using generative AI, and determine response priorities.

[0103] The service provider can select the optimal delivery method when providing responses, taking into account the customer's geographical location. For example, the service provider can provide relevant information based on the customer's current location. It can also provide information about nearby stores and facilities based on the customer's geographical location. Furthermore, the service provider can provide region-specific information, taking into account the customer's geographical location. This allows for the provision of highly relevant information by considering the customer's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can acquire the customer's geographical location and select the optimal delivery method using an AI model.

[0104] The service provider can analyze the customer's social media activity and propose a means of providing information when providing responses. For example, the service provider can provide information based on topics of interest from the customer's social media activity. The service provider can also analyze the content of the customer's social media posts and provide relevant information. Furthermore, the service provider can provide relevant information by referring to the activities of the customer's followers and friends on social media. In this way, relevant information can be provided by analyzing the customer's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can acquire the customer's social media activity, analyze it using an AI model, and propose a means of providing information.

[0105] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0106] The service provider can estimate the customer's emotions and adjust the way responses are provided based on those estimated emotions. For example, if the customer is stressed, a simple interface can be provided and the response process minimized. If the customer is relaxed, detailed response options can be provided and a customizable response method can be suggested. Furthermore, if the customer is in a hurry, a response can be provided quickly. This allows for the provision of the most optimal response for the customer by adjusting the response method according to their 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 service provider may be performed using AI or not. For example, the service provider can acquire customer emotion data, estimate emotions using generative AI, and adjust the response method.

[0107] The data acquisition unit can select the optimal acquisition method by referring to past data acquisitions when acquiring information. For example, it can prioritize acquiring information that has been acquired frequently in the past. It can also analyze past data acquisitions to select the optimal acquisition method. Furthermore, it can predict and acquire information to be acquired at a specific time period based on past data acquisitions. In this way, by referring to past data acquisitions, the optimal information acquisition method can be provided. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can acquire past data, analyze it using an AI model, and select the optimal acquisition method.

[0108] The service provider can select the optimal delivery method by referring to the customer's past question history when providing answers. For example, it can provide the optimal answer based on the questions the customer has frequently asked in the past. It can also analyze the customer's past question history and select the optimal delivery method. Furthermore, it can predict and provide answers to be delivered at specific times based on the customer's past question history. In this way, the service provider can provide the optimal answer delivery method by referring to the customer's past question history. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can acquire the customer's past question history, analyze it using an AI model, and select the optimal delivery method.

[0109] The reception desk can filter questions based on the customer's current situation and areas of interest when receiving them. For example, it can prioritize displaying relevant questions based on the customer's current location. It can also filter and display relevant questions based on the customer's areas of interest. Furthermore, it can suggest appropriate questions based on the customer's current situation (e.g., time of day or weather). This allows for priority reception of highly relevant questions by filtering them based on the customer's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can acquire the customer's current situation and areas of interest, filter them using an AI model, and then display the questions.

[0110] The input unit can apply different input algorithms depending on the question category when inputting answers. For example, for questions about products, it can input answers by referring to the product database. For questions about services, it can input answers by referring to service information. Furthermore, for questions about events, it can input answers by referring to event information. By applying different input algorithms depending on the question category, appropriate answers can be provided. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can classify the question category and apply an appropriate input algorithm using an AI model.

[0111] The service provider can estimate the customer's emotions and prioritize responses based on those emotions. For example, if the customer is nervous, important answers can be prioritized. If the customer is relaxed, detailed answers can be prioritized. Furthermore, if the customer is in a hurry, answers that can be provided quickly can be prioritized. This allows for the priority of important answers by prioritizing responses 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 service provider may be performed using AI or not. For example, the service provider can acquire customer emotion data, estimate emotions using generative AI, and determine the priority of responses.

[0112] The acquisition unit can apply different acquisition methods to each category of information when acquiring it. For example, when acquiring product information, it can refer to the product database. When acquiring service information, it can also refer to the service database. Furthermore, when acquiring event information, it can also refer to the event database. This allows for the provision of accurate information by applying the appropriate acquisition method according to the category of information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can classify the categories of information and apply the appropriate acquisition method using an AI model.

[0113] The reception desk can estimate the customer's emotions and prioritize the questions to be answered based on those emotions. For example, if the customer is nervous, important questions can be prioritized. If the customer is relaxed, detailed questions can be prioritized. Furthermore, if the customer is in a hurry, questions requiring quick answers can be prioritized. This ensures that important questions are prioritized by prioritizing them 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 reception desk may be performed using AI or not. For example, the reception desk can acquire customer emotion data, estimate emotions using generative AI, and determine the priority of questions.

[0114] The input unit can prioritize inputs based on when the questions were submitted. For example, it can prioritize answering recently submitted questions. It can also prioritize answering questions submitted within a specific time period. Furthermore, it can prioritize answering urgent questions. This allows for quicker responses by prioritizing inputs based on when the questions were submitted. Some or all of the above processing in the input unit may be performed using AI, for example, or not. For example, the input unit can evaluate when the questions were submitted and use an AI model to determine the priority of the inputs.

[0115] The acquisition unit can estimate the customer's emotions and adjust the information acquisition method based on the estimated emotions. For example, if the customer is stressed, it can provide a simple interface and minimize the information acquisition procedure. If the customer is relaxed, it can provide detailed information acquisition options and suggest a customizable acquisition method. Furthermore, if the customer is in a hurry, it can enable rapid information acquisition. This allows for optimal information acquisition for the customer by adjusting the information acquisition method according to their 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 acquisition unit may be performed using AI or not. For example, the acquisition unit can acquire customer emotion data, estimate emotions using generative AI, and adjust the information acquisition method.

[0116] The following briefly describes the processing flow for example form 2.

[0117] Step 1: The reception desk receives questions from customers. For example, customers can enter their questions using a smartphone or tablet. The reception desk can also accept questions using voice input. For example, if a customer asks by voice, "What are today's sale items?", the reception desk will accept the question. Furthermore, customers can also enter their questions at the information counter within the store. For example, a customer can enter their question using a tablet at the information counter. Step 2: The input unit pre-enters answers to questions received by the reception unit. For example, the input unit allows commercial facility managers or tenants to input answers to customer questions into the platform. The input unit can also automatically generate answers to customer questions using generative AI. For example, the generative AI can generate an answer to the question "What are today's sale items?" based on the information entered into the platform. Furthermore, the input unit can periodically update the answers to customer questions. For example, the input unit can input daily sale information into the platform, and the generative AI can provide answers based on that information. Step 3: The acquisition unit retrieves the information entered by the input unit. For example, the acquisition unit can retrieve inventory information from the platform. The acquisition unit can also retrieve store layout information. For example, the acquisition unit can retrieve a store layout diagram from the platform and provide it to the customer. Furthermore, the acquisition unit can also retrieve service information and event information. For example, the acquisition unit can retrieve parking service information and event schedules from the platform. Step 4: The service department provides answers to customers based on the information acquired by the information acquisition department. For example, the service department can provide customers with information on the availability of products based on inventory information. The service department can also guide customers to the location of products based on store layout information. For example, in response to the question, "Which shelf is the boiled bamboo shoots on?", the service department will guide customers to the location of the relevant shelf based on the store layout information. Furthermore, the service department can also provide answers to customers based on service information and event information. For example, in response to the question, "How much do I need to spend to get a parking voucher?", the service department will answer with the required purchase amount based on service information.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] Each of the multiple elements described above, including the reception unit, input unit, acquisition unit, and provision unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, allowing customers to input questions using a smartphone or tablet. The input unit is implemented by the specific processing unit 290 of the data processing unit 12, allowing commercial facility managers and tenants to input answers into the platform. The acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12, acquiring inventory information and store layout information from the platform. The provision unit is implemented by the output device 40 of the smart device 14, providing answers to customers based on the acquired information. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0122] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] Each of the multiple elements described above, including the reception unit, input unit, acquisition unit, and provision unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, allowing customers to input questions by voice. The input unit is implemented by the specific processing unit 290 of the data processing unit 12, allowing commercial facility managers and tenants to input answers into the platform. The acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12, acquiring inventory information and store layout information from the platform. The provision unit is implemented by the speaker 240 of the smart glasses 214, providing answers to customers based on the acquired information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0138] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] Each of the multiple elements described above, including the reception unit, input unit, acquisition unit, and provision 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 microphone 238 of the headset terminal 314, allowing customers to input questions by voice. The input unit is implemented by the specific processing unit 290 of the data processing unit 12, allowing commercial facility managers and tenants to input answers into the platform. The acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12, acquiring inventory information and store layout information from the platform. The provision unit is implemented by the display 343 of the headset terminal 314, providing answers to customers based on the acquired information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0154] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] Each of the multiple elements described above, including the reception unit, input unit, acquisition unit, and provision 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 microphone 238 of the robot 414, allowing customers to input questions by voice. The input unit is implemented by the specific processing unit 290 of the data processing unit 12, allowing commercial facility managers and tenants to input answers into the platform. The acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12, acquiring inventory information and store layout information from the platform. The provision unit is implemented by the speaker 240 of the robot 414, providing answers to customers based on the acquired information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] (Note 1) A reception desk that handles customer inquiries, An input unit for pre-entering answers to questions received by the reception unit, An acquisition unit that acquires information input by the aforementioned input unit, A provision unit provides answers to customers based on the information acquired by the acquisition unit, Equipped with A system characterized by the following features. (Note 2) The acquisition unit is, Get inventory information The system described in Appendix 1, characterized by the features described herein. (Note 3) The acquisition unit is, Get store layout information The system described in Appendix 1, characterized by the features described herein. (Note 4) The acquisition unit is, Get service information The system described in Appendix 1, characterized by the features described herein. (Note 5) The acquisition unit is, Get event information The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provide responses to customers based on acquired inventory information. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Based on the acquired store layout information, we provide answers to customers. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned supply unit is, We provide responses to customers based on the acquired service information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned supply unit is, Based on the acquired event information, we provide responses to customers. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is We estimate the customer's emotions and adjust the way we ask questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving a question, the system analyzes the customer's past question history and selects the most suitable method of handling the question. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving inquiries, filtering is performed based on the customer's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is The system estimates the customer's emotions and prioritizes the questions to be asked based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is When receiving questions, the system prioritizes questions that are highly relevant, taking into account the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reception unit is When receiving a question, we analyze the customer's social media activity and select relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned input unit is The system estimates customer emotions and adjusts the response input method based on the estimated customer emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned input unit is When entering your answer, adjust the level of detail based on the importance of the question. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned input unit is When entering answers, different input algorithms are applied depending on the question category. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned input unit is It estimates the customer's emotions and adjusts the length of the input response based on the estimated customer emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned input unit is When entering your answers, prioritize your responses based on when the questions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned input unit is When entering answers, the order of input will be adjusted based on the relevance of the questions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The acquisition unit is, We estimate customer emotions and adjust how we acquire information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The acquisition unit is, When acquiring information, the optimal acquisition method is selected by referring to previously acquired data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The acquisition unit is, When acquiring information, different acquisition methods are applied for each category of information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The acquisition unit is, We estimate customer emotions and prioritize the information to acquire based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The acquisition unit is, When acquiring information, prioritize acquisition based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 27) The acquisition unit is, When acquiring information, adjust the acquisition order based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, We estimate customer emotions and adjust how we provide responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing answers, the system will refer to the customer's past question history to select the most appropriate method of delivery. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing responses, customize the method of delivery based on the customer's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, The system estimates customer emotions and prioritizes responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing responses, the optimal delivery method will be selected considering the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing responses, we analyze customers' social media activity and suggest methods for providing them. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0190] 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 that handles customer inquiries, An input unit for pre-entering answers to questions received by the reception unit, An acquisition unit that acquires information input by the aforementioned input unit, A provision unit provides answers to customers based on the information acquired by the acquisition unit, Equipped with A system characterized by the following features.

2. The acquisition unit is, Get inventory information The system according to feature 1.

3. The acquisition unit is, Get store layout information The system according to feature 1.

4. The acquisition unit is, Get service information The system according to feature 1.

5. The acquisition unit is, Get event information The system according to feature 1.

6. The aforementioned supply unit is, Provide responses to customers based on acquired inventory information. The system according to feature 1.

7. The aforementioned supply unit is, Based on the acquired store layout information, we provide answers to customers. The system according to feature 1.

8. The aforementioned supply unit is, We provide responses to customers based on the acquired service information. The system according to feature 1.

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