system

Abstract

An object of the system according to the embodiment is to efficiently perform reservation management and sales activity even in a busy period.SOLUTION: A system includes a reception unit, a generation unit, a display unit, and a provision unit. The reception unit automatically receives a telephone call from a customer. The generation unit generates a reservation schedule based on the information received by the reception unit. The display unit displays the status of the store and the status of the reservation system. The provision part generates a business sentence on the basis of the information obtained by the display part, and transmits it by the SNS.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to respond to phone calls during busy periods, and there were issues with efficient reservation management and sales activities.

[0005] The system according to the embodiment aims to efficiently manage reservations and conduct sales activities even during busy periods. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a visualization unit, and a provision unit. The reception unit automatically receives calls from customers. The generation unit generates a reservation schedule based on the information received by the reception unit. The visualization unit visualizes the status of the store and the status of the reservation system. The provision unit generates sales messages based on the information obtained by the visualization unit and sends them via SNS. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage reservations and conduct sales activities even during busy periods. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention is designed for businesses that cannot afford to waste time answering the phone. This system automatically accepts calls from customers and automatically links with a reservation system to generate reservation schedules. It also visualizes the status of the store and the reservation system, generates sales messages, and sends them via social media. This allows the system to efficiently handle reservations and sales without wasting time answering the phone. For example, when a customer requests a reservation over the phone, the generation AI automatically responds, confirms the reservation details, and updates the reservation system. Next, it visualizes the status of the store and the reservation system, and automatically creates sales messages during times when the store is not busy and sends them to customers via social media. This allows for efficient sales activities even during busy periods.

[0029] The system according to the embodiment includes a reception unit, a generation unit, a visualization unit, and a provision unit. The reception unit automatically receives calls from customers. For example, the reception unit can automatically receive calls from customers using voice recognition technology or an IVR (Interactive Voice Response) system. After receiving a call from a customer, the reception unit can also confirm the reservation details and update the reservation system. The generation unit automatically links with the reservation system to generate a reservation schedule. For example, the system can automatically link with the reservation system to generate a reservation schedule using API integration or real-time data synchronization. The visualization unit visualizes the status of the store and the status of the reservation system. For example, the system can visualize the status of the store and the status of the reservation system using a graphical user interface (GUI) or text display. The provision unit generates sales messages based on information obtained by the visualization unit and sends them via social media. For example, the system can generate sales messages using template-based generation or a specific social media platform and send them via social media. As a result, the system according to the embodiment automates customer phone calls and efficiently generates reservation schedules, visualizes store status, and generates and sends sales messages.

[0030] The reception unit can automatically accept phone calls from customers, confirm reservation details, and reflect them in the reservation system. The reception unit can automatically accept phone calls from customers using, for example, voice recognition technology or an IVR (Interactive Voice Response) system. After accepting a phone call from a customer, the reception unit can confirm the reservation details by voice recognition or user input. After confirming the reservation details, the reception unit can reflect them in the reservation system by writing them to a database or using an API. This makes reservation management more efficient by automatically accepting phone calls from customers, confirming the reservation details, and reflecting them in the reservation system.

[0031] The generation unit can automatically link with the reservation system to generate a reservation schedule. The generation unit automatically links with the reservation system using, for example, API integration or real-time data synchronization. The generation unit can also generate a reservation schedule based on criteria such as the priority of time slots and avoidance of overlapping reservations. This makes reservation management more efficient by automatically linking with the reservation system to generate a reservation schedule.

[0032] The visualization unit can visualize the status of the store and the status of the reservation system. The visualization unit visualizes the status of the store and the status of the reservation system, for example, using a graphical user interface (GUI) or text display. The visualization unit can also acquire and display the status of the store, such as inventory status and staff allocation status, and the status of the reservation system, such as availability of reservations and reservation cancellation status. In this way, visualization of the status of the store and the status of the reservation system improves the efficiency of sales activities.

[0033] The provision unit can create sales messages during times when the store is not open and send them to customers via social media. The provision unit creates sales messages using, for example, template-based generation or a specific social media platform, and sends them via social media. The provision unit can also create sales messages based on times when the store is not open, such as specific times of the day or days of the week. This allows sales messages to be created during times when the store is not open and sent to customers via social media, thereby streamlining sales activities.

[0034] The reception unit can analyze the customer's past reservation history and select an appropriate response method. The reception unit can analyze the customer's past reservation history, for example, using data mining or machine learning algorithms. For example, the generation AI can prioritize providing information about services that the customer has frequently used in the past based on those services. Also, if the customer has made reservations for a specific time slot in the past, the generation AI can prioritize providing information about availability for that time slot. Also, if the customer has had a specific problem in the past, the generation AI can prepare a solution to that problem in advance and respond quickly. In this way, the optimal response method can be selected by analyzing the customer's past reservation history.

[0035] The reception unit can customize the response content based on the customer's current situation. The reception unit grasps the customer's current situation, for example, using real-time data and information input by the customer. For example, if a customer calls at night, the generation AI can provide information about night-time service options. Also, if a customer calls from a specific area, the generation AI can provide information related to that area. Also, if a customer calls during a specific event, the generation AI can provide information about special services and promotions related to the event. This allows for more appropriate responses by customizing the response content according to the customer's current situation.

[0036] The reception unit can select the optimal response means depending on the customer's input method. The reception unit identifies the customer's input method, such as voice input or text input. For example, if a customer makes an inquiry by voice, the generation AI will respond by voice and provide the necessary information. Also, if a customer makes an inquiry by text, the generation AI can provide detailed information in text. Also, if a customer sends an image, the generation AI can analyze the image and provide related information. This improves customer satisfaction by selecting the optimal response means depending on the customer's input method.

[0037] The reception unit can respond with priority to highly relevant information based on the customer's geographical location information. The reception unit acquires the customer's geographical location information using, for example, GPS data or an IP address. For example, if a customer calls from a specific area, the generation AI can provide service and promotion information related to that area with priority. Also, if the customer is traveling, the generation AI can provide information related to the customer's travel destination. Also, if the customer is at a specific event venue, the generation AI can provide information related to that event with priority. In this way, highly relevant information can be provided by taking the customer's geographical location information into consideration.

[0038] The reception unit can analyze the customer's social media activity and reflect related information in the response. The reception unit can analyze the customer's social media activity, for example, by analyzing the content of posts and followers. For example, if the customer is participating in a specific event on social media, the generation AI can provide information related to that event. Also, if the customer mentions a specific product on social media, the generation AI can provide information related to that product. Also, if the customer mentions a specific problem on social media, the generation AI can provide a solution to that problem. In this way, by analyzing the customer's social media activity, related information can be provided.

[0039] The reception unit can customize the response method by reflecting the customer's past feedback. The reception unit reflects the customer's past feedback, for example, by analyzing the feedback and extracting areas for improvement. For example, the generation AI selects the optimal response method for the customer based on feedback provided by the customer in the past. Also, if the customer has been dissatisfied in the past, the generation AI can provide a response method to resolve that dissatisfaction. Also, if the customer has given a high rating in the past, the generation AI can provide a response method to maintain that rating. In this way, the optimal response method can be provided by reflecting the customer's past feedback.

[0040] When generating a reservation schedule, the generation unit can adjust the level of detail of the schedule based on the customer's importance. The generation unit evaluates the customer's importance using, for example, past transaction history or the customer's profile. For example, if the customer is a VIP, the generation AI can propose a detailed schedule. Also, if the customer is a new customer, the generation AI can propose a basic schedule. Also, if the customer is a frequent customer, the generation AI can propose a schedule based on the customer's past usage history. In this way, customer satisfaction is improved by adjusting the level of detail of the schedule based on the customer's importance.

[0041] When generating a reservation schedule, the generation unit can apply different generation algorithms depending on the reservation category. The generation unit identifies the reservation category based, for example, on the type of service or the customer's purpose. For example, in the case of a restaurant reservation, the generation AI can propose a schedule based on the availability of tables. In addition, in the case of a hair salon reservation, the generation AI can also propose a schedule based on the availability of stylists. In addition, in the case of a medical institution reservation, the generation AI can also propose a schedule based on the availability of doctors. In this way, by applying different generation algorithms depending on the reservation category, the accuracy of the schedule is improved.

[0042] When generating a reservation schedule, the generation unit can improve the accuracy of the generation by referring to the customer's past reservation history. The generation unit, for example, uses data mining or machine learning algorithms to refer to the customer's past reservation history. For example, based on reservations that the customer has canceled in the past, the generation AI can propose a schedule that takes into account the risk of cancellation. Also, based on reservations that the customer has given a high rating in the past, the generation AI can propose a schedule that will maintain that rating. Furthermore, if the customer has made reservations during a specific time period in the past, the generation AI can propose a schedule that gives priority to that time period. In this way, by referring to the customer's past reservation history, the accuracy of the schedule is improved.

[0043] When generating a reservation schedule, the generation unit can determine the priority of the schedule based on the time of reservation submission. The generation unit identifies the time of reservation submission based on, for example, the submission date and time or the submission order. For example, the generation AI can preferentially propose a schedule for a customer who makes a reservation early. The generation AI can also propose a schedule that can be quickly accommodated for an urgent reservation. The generation AI can also give priority to the schedule of a customer who makes reservations regularly. This enables efficient schedule management by determining the priority of the schedule based on the time of reservation submission.

[0044] When generating a reservation schedule, the generation unit can adjust the order of the schedule based on the relevance of reservations. The generation unit identifies the relevance of reservations based on, for example, the type of service or the customer's purpose. For example, if there are multiple reservations on the same day, the generation AI adjusts the schedule taking that relevance into consideration. The generation AI can also propose an efficient schedule for services that a customer will use consecutively. Furthermore, if a customer is attending a specific event, the generation AI can prioritize suggesting a schedule related to that event. This allows for efficient schedule management by adjusting the order of the schedule based on the relevance of reservations.

[0045] When generating a reservation schedule, the generation unit can adjust the use of technical terminology in the schedule according to the customer's level of expertise. The generation unit evaluates the customer's level of expertise, for example, using past transaction history or a customer profile. For example, if the customer has technical expertise, the generation AI can propose a schedule using technical terminology. Also, if the customer is a beginner, the generation AI can propose a schedule in easy-to-understand language. Also, if the customer is knowledgeable in a particular field, the generation AI can propose a schedule using technical terminology related to that field. In this way, customer satisfaction is improved by adjusting the use of technical terminology in the schedule according to the customer's level of expertise.

[0046] The visualization unit can improve the accuracy of visualization based on the interrelationships between stores during visualization. The visualization unit identifies the interrelationships between stores based on, for example, geographical proximity or business relevance. For example, the generation AI provides the optimal display method based on the relative locations of the stores. The generation AI can also display highly relevant information by taking into account the store's industry and service content. The generation AI can also provide the optimal display method based on the store's business hours and reservation status. In this way, the accuracy of visualization is improved by taking into account the interrelationships between stores.

[0047] The visualization unit can perform visualization while taking into account the store's attribute information. The visualization unit identifies the store's attribute information based on, for example, the store's size and the type of services it provides. For example, the generation AI provides the optimal display method depending on the store's size and industry. The generation AI can also display highly relevant information by taking into account the store's customer demographic and service content. The generation AI can also provide the optimal display method based on the store's location and access information. In this way, the accuracy of visualization is improved by taking into account the store's attribute information.

[0048] When visualizing, the visualization unit can weight the visualization based on the business frequency of the store. The visualization unit identifies the business frequency of the store based on, for example, the number of business days and business hours. For example, the generation AI weights the visualization so that stores with high business frequencies are displayed preferentially. The generation AI can also provide a special display method to highlight stores with low business frequencies. The generation AI can also adjust the display order and size according to the business frequency. In this way, by weighting the visualization based on the business frequency of the store, important information can be displayed preferentially.

[0049] The visualization unit can perform visualization while taking into account the geographic distribution of stores. The visualization unit identifies the geographic distribution of stores, for example, using map data or a geographic information system (GIS). For example, the generation AI displays stores on a map based on the store's location information. The generation AI can also display highly relevant stores by taking into account the geographic proximity of stores. The generation AI can also provide an optimal display method by taking into account the regional characteristics of stores. In this way, the accuracy of visualization is improved by taking into account the geographic distribution of stores.

[0050] The visualization unit can improve the accuracy of the visualization by referring to literature related to the store during visualization. The visualization unit, for example, refers to related literature by using a literature database or by using literature summaries. For example, the generation AI provides detailed information based on research papers and articles related to the store. The generation AI can also provide the optimal display method by referring to literature related to the store's industry and service content. The generation AI can also display highly relevant information based on literature related to the store's history and background. In this way, the accuracy of the visualization is improved by referring to literature related to the store.

[0051] The visualization unit can take the market value of a store into consideration when visualizing. The visualization unit identifies the market value of a store based on, for example, sales data or customer ratings. For example, the generation AI assigns weights so that stores with high market values ​​are displayed preferentially. The generation AI can also provide a special display method to highlight stores with low market values. The generation AI can also adjust the display order and size according to market value. This allows important information to be displayed preferentially by taking the market value of the store into consideration.

[0052] When generating sales messages, the provision unit can adjust the level of detail in the sales messages based on the importance of the store. The provision unit evaluates the importance of the store, for example, using past transaction history or customer profiles. For example, the generation AI creates detailed sales messages for important stores. The generation AI can also create sales messages containing basic information for new stores. The generation AI can also create sales messages that emphasize the features of frequently used stores. In this way, customer satisfaction is improved by adjusting the level of detail in the sales messages based on the importance of the store.

[0053] When generating sales messages, the provider can apply different generation algorithms depending on the store category. The provider can identify the store category based, for example, on the type of service or the customer's purpose. For example, in sales messages for a restaurant, the generation AI can emphasize menu and special offer information. In addition, in sales messages for a beauty salon, the generation AI can emphasize introductions to stylists and service details. In addition, in sales messages for a medical institution, the generation AI can emphasize introductions to doctors and medical details. In this way, the accuracy of sales messages is improved by applying different generation algorithms depending on the store category.

[0054] When generating sales messages, the provision unit can improve the accuracy of the generation by referring to the customer's past sales results. The provision unit can refer to the customer's past sales results, for example, using data mining or machine learning algorithms. For example, based on sales messages that the customer has given a high rating in the past, the generation AI can create sales messages that will maintain that rating. Also, based on sales messages that the customer has canceled in the past, the generation AI can create sales messages that take into account the risk of cancellation. Furthermore, if the customer has received sales messages during a specific time period in the past, the generation AI can create sales messages that give priority to that time period. In this way, the accuracy of sales messages can be improved by referring to the customer's past sales results.

[0055] When generating sales documents, the provision unit can determine the priority of the sales documents based on the time of submission. The provision unit identifies the time of submission of the sales documents based on, for example, the submission date and time or the submission order. For example, if a sales call is made early, the generation AI will prioritize creating sales documents. In addition, the generation AI can also create sales documents that can respond quickly to urgent sales calls. In addition, if sales calls are made regularly, the generation AI can also prioritize consideration of the schedule of those sales calls. In this way, by determining the priority of sales documents based on the time of submission, efficient sales activities become possible.

[0056] When generating sales texts, the provision unit can adjust the order of the sales texts based on the relevance of the sales. The provision unit identifies the relevance of sales based on, for example, the type of service or the customer's objectives. For example, if there are multiple sales on the same day, the generation AI adjusts the sales texts taking that relevance into consideration. The generation AI can also create efficient sales texts for services that a customer will use consecutively. Furthermore, if a customer is attending a specific event, the generation AI can prioritize creating sales texts related to that event. This allows for efficient sales activities by adjusting the order of sales texts based on the relevance of the sales.

[0057] When generating sales pitches, the provision unit can adjust the use of technical terms in the pitches according to the customer's level of expertise. The provision unit assesses the customer's level of expertise, for example, using past transaction history or the customer's profile. For example, if the customer has technical expertise, the generation AI can create the pitches using technical terms. Also, if the customer is a beginner, the generation AI can create the pitches in easy-to-understand language. Also, if the customer is knowledgeable in a particular field, the generation AI can create the pitches using technical terms related to that field. This improves customer satisfaction by adjusting the use of technical terms in the pitches according to the customer's level of expertise.

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

[0059] When generating a reservation schedule, the generation unit can improve the accuracy of the schedule by reflecting past customer feedback. For example, if a customer has preferred to make reservations during a specific time period in the past, the generation unit can generate a schedule that takes that time period into consideration. Also, if a customer has highly rated a specific service in the past, the generation unit can suggest a schedule that includes that service. Furthermore, the generation unit can generate a schedule that takes into account the risk of cancellation based on reservations that the customer has canceled in the past. In this way, the accuracy of the schedule can be improved by reflecting past customer feedback.

[0060] The reception unit can prioritize relevant information based on the customer's geographical location information. For example, if a customer calls from a specific area, the generation AI can prioritize providing service and promotion information related to that area. Also, if the customer is traveling, the generation AI can provide information related to the customer's travel destination. Also, if the customer is at a specific event venue, the generation AI can prioritize providing information related to that event. In this way, highly relevant information can be provided by taking the customer's geographical location information into consideration.

[0061] When generating sales messages, the provision unit can improve the accuracy of the generation by referring to the customer's past sales results. For example, based on sales messages that the customer has given a high rating in the past, the generation AI can create sales messages that will maintain that rating. Also, based on sales messages that the customer has canceled in the past, the generation AI can create sales messages that take into account the risk of cancellation. Furthermore, if the customer has received sales messages during a specific time period in the past, the generation AI can create sales messages that give priority to that time period. In this way, the accuracy of sales messages can be improved by referring to the customer's past sales results.

[0062] The visualization unit can perform visualization while taking into account the geographic distribution of stores. For example, it can identify the geographic distribution of stores using map data or a geographic information system (GIS). For example, the generation AI can display stores on a map based on the store's location information. The generation AI can also display highly relevant stores by taking into account the geographic proximity of stores. The generation AI can also provide the optimal display method by taking into account the regional characteristics of stores. In this way, the accuracy of visualization is improved by taking into account the geographic distribution of stores.

[0063] When generating sales documents, the provision unit can determine the priority of the sales documents based on the time of submission. For example, the time of submission of the sales documents can be determined based on the submission date and time or the submission order. For example, if a sales call is made early, the generation AI can create sales documents with priority. In addition, the generation AI can create sales documents that can respond quickly to urgent sales calls. In addition, if sales calls are made regularly, the generation AI can also give priority to considering the schedule of those sales calls. This allows for efficient sales activities by determining the priority of sales documents based on the time of submission.

[0064] The reception unit can analyze the customer's social media activity and reflect related information in the response. For example, the customer's social media activity can be analyzed by analyzing the content of posts and followers. For example, if a customer is participating in a specific event on social media, the generation AI can provide information related to that event. Also, if a customer mentions a specific product on social media, the generation AI can provide information related to that product. Also, if a customer mentions a specific problem on social media, the generation AI can provide a solution to that problem. In this way, relevant information can be provided by analyzing the customer's social media activity.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception department automatically receives calls from customers. For example, calls from customers can be automatically received using voice recognition technology or an IVR (Interactive Voice Response) system. After receiving a call from a customer, the reception department can also check the reservation details and update them in the reservation system. Step 2: The generation unit generates a reservation schedule based on the information received by the reception unit. For example, the generation unit can automatically link with a reservation system and generate a reservation schedule using API integration or real-time data synchronization. Step 3: The visualization unit displays the status of the store and the status of the reservation system. For example, the status of the store and the status of the reservation system can be visualized using a graphical user interface (GUI) or a text display. Step 4: The providing unit generates sales messages based on the information obtained by the visualization unit and sends them via SNS. For example, the sales messages can be generated and sent via SNS using template-based generation or a specific SNS platform.

[0067] (Example 2) A system according to an embodiment of the present invention is designed for businesses that cannot afford to waste time answering the phone. This system automatically accepts calls from customers and automatically links with a reservation system to generate reservation schedules. It also visualizes the status of the store and the reservation system, generates sales messages, and sends them via social media. This allows the system to efficiently handle reservations and sales without wasting time answering the phone. For example, when a customer requests a reservation over the phone, the generation AI automatically responds, confirms the reservation details, and updates the reservation system. Next, it visualizes the status of the store and the reservation system, and automatically creates sales messages during times when the store is not busy and sends them to customers via social media. This allows for efficient sales activities even during busy periods.

[0068] The system according to the embodiment includes a reception unit, a generation unit, a visualization unit, and a provision unit. The reception unit automatically receives calls from customers. For example, the reception unit can automatically receive calls from customers using voice recognition technology or an IVR (Interactive Voice Response) system. After receiving a call from a customer, the reception unit can also confirm the reservation details and update the reservation system. The generation unit automatically links with the reservation system to generate a reservation schedule. For example, the system can automatically link with the reservation system to generate a reservation schedule using API integration or real-time data synchronization. The visualization unit visualizes the status of the store and the status of the reservation system. For example, the system can visualize the status of the store and the status of the reservation system using a graphical user interface (GUI) or text display. The provision unit generates sales messages based on information obtained by the visualization unit and sends them via social media. For example, the system can generate sales messages using template-based generation or a specific social media platform and send them via social media. As a result, the system according to the embodiment automates customer phone calls and efficiently generates reservation schedules, visualizes store status, and generates and sends sales messages.

[0069] The reception unit can automatically accept phone calls from customers, confirm reservation details, and reflect them in the reservation system. The reception unit can automatically accept phone calls from customers using, for example, voice recognition technology or an IVR (Interactive Voice Response) system. After accepting a phone call from a customer, the reception unit can confirm the reservation details by voice recognition or user input. After confirming the reservation details, the reception unit can reflect them in the reservation system by writing them to a database or using an API. This makes reservation management more efficient by automatically accepting phone calls from customers, confirming the reservation details, and reflecting them in the reservation system.

[0070] The generation unit can automatically link with the reservation system to generate a reservation schedule. The generation unit automatically links with the reservation system using, for example, API integration or real-time data synchronization. The generation unit can also generate a reservation schedule based on criteria such as the priority of time slots and avoidance of overlapping reservations. This makes reservation management more efficient by automatically linking with the reservation system to generate a reservation schedule.

[0071] The visualization unit can visualize the status of the store and the status of the reservation system. The visualization unit visualizes the status of the store and the status of the reservation system, for example, using a graphical user interface (GUI) or text display. The visualization unit can also acquire and display the status of the store, such as inventory status and staff allocation status, and the status of the reservation system, such as availability of reservations and reservation cancellation status. In this way, visualization of the status of the store and the status of the reservation system improves the efficiency of sales activities.

[0072] The provision unit can create sales messages during times when the store is not open and send them to customers via social media. The provision unit creates sales messages using, for example, template-based generation or a specific social media platform, and sends them via social media. The provision unit can also create sales messages based on times when the store is not open, such as specific times of the day or days of the week. This allows sales messages to be created during times when the store is not open and sent to customers via social media, thereby streamlining sales activities.

[0073] The reception unit can estimate the customer's emotions and adjust the tone and content of the response based on the estimated customer emotions. The reception unit estimates the customer's emotions using, for example, voice analysis or text analysis. For example, if the customer is angry, the generation AI can respond in a calm tone and make specific suggestions to solve the problem. If the customer is anxious, the generation AI can respond in a gentle tone that gives a sense of security and provide a detailed explanation. If the customer is in a hurry, the generation AI can respond quickly and concisely, providing the necessary information in a short amount of time. This improves customer satisfaction by adjusting the tone and content of the response according to the customer's emotions. Emotion estimation is achieved using, for example, an emotion engine or generation AI with an emotion estimation function. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0074] The reception unit can analyze the customer's past reservation history and select an appropriate response method. The reception unit can analyze the customer's past reservation history, for example, using data mining or machine learning algorithms. For example, the generation AI can prioritize providing information about services that the customer has frequently used in the past based on those services. Also, if the customer has made reservations for a specific time slot in the past, the generation AI can prioritize providing information about availability for that time slot. Also, if the customer has had a specific problem in the past, the generation AI can prepare a solution to that problem in advance and respond quickly. In this way, the optimal response method can be selected by analyzing the customer's past reservation history.

[0075] The reception unit can customize the response content based on the customer's current situation. The reception unit grasps the customer's current situation, for example, using real-time data and information input by the customer. For example, if a customer calls at night, the generation AI can provide information about night-time service options. Also, if a customer calls from a specific area, the generation AI can provide information related to that area. Also, if a customer calls during a specific event, the generation AI can provide information about special services and promotions related to the event. This allows for more appropriate responses by customizing the response content according to the customer's current situation.

[0076] The reception unit can select the optimal response means depending on the customer's input method. The reception unit identifies the customer's input method, such as voice input or text input. For example, if a customer makes an inquiry by voice, the generation AI will respond by voice and provide the necessary information. Also, if a customer makes an inquiry by text, the generation AI can provide detailed information in text. Also, if a customer sends an image, the generation AI can analyze the image and provide related information. This improves customer satisfaction by selecting the optimal response means depending on the customer's input method.

[0077] The reception unit can estimate the customer's emotions and determine the priority of responses based on the estimated customer emotions. The reception unit estimates the customer's emotions using, for example, voice analysis or text analysis. For example, if a customer is very angry, the generation AI can process the customer's inquiry as a top priority. Also, if a customer is feeling anxious, the generation AI can quickly process the customer's inquiry and provide a sense of security. Also, if a customer is in a hurry, the generation AI can prioritize the customer's inquiry and respond quickly. In this way, by determining the priority of responses based on the customer's emotions, important inquiries can be handled quickly. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] The reception unit can respond with priority to highly relevant information based on the customer's geographical location information. The reception unit acquires the customer's geographical location information using, for example, GPS data or an IP address. For example, if a customer calls from a specific area, the generation AI can provide service and promotion information related to that area with priority. Also, if the customer is traveling, the generation AI can provide information related to the customer's travel destination. Also, if the customer is at a specific event venue, the generation AI can provide information related to that event with priority. In this way, highly relevant information can be provided by taking the customer's geographical location information into consideration.

[0079] The reception unit can analyze the customer's social media activity and reflect related information in the response. The reception unit can analyze the customer's social media activity, for example, by analyzing the content of posts and followers. For example, if the customer is participating in a specific event on social media, the generation AI can provide information related to that event. Also, if the customer mentions a specific product on social media, the generation AI can provide information related to that product. Also, if the customer mentions a specific problem on social media, the generation AI can provide a solution to that problem. In this way, by analyzing the customer's social media activity, related information can be provided.

[0080] The reception unit can customize the response method by reflecting the customer's past feedback. The reception unit reflects the customer's past feedback, for example, by analyzing the feedback and extracting areas for improvement. For example, the generation AI selects the optimal response method for the customer based on feedback provided by the customer in the past. Also, if the customer has been dissatisfied in the past, the generation AI can provide a response method to resolve that dissatisfaction. Also, if the customer has given a high rating in the past, the generation AI can provide a response method to maintain that rating. In this way, the optimal response method can be provided by reflecting the customer's past feedback.

[0081] The generation unit can estimate the customer's emotions and adjust the reservation schedule generation method based on the estimated customer emotions. The generation unit estimates the customer's emotions using, for example, voice analysis or text analysis. For example, if the customer is relaxed, the generation AI can suggest a relaxed schedule. Also, if the customer is in a hurry, the generation AI can suggest a schedule that allows for quick response. Also, if the customer is feeling anxious, the generation AI can suggest a schedule that gives a sense of security. In this way, customer satisfaction is improved by adjusting the reservation schedule generation method based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0082] When generating a reservation schedule, the generation unit can adjust the level of detail of the schedule based on the customer's importance. The generation unit evaluates the customer's importance using, for example, past transaction history or the customer's profile. For example, if the customer is a VIP, the generation AI can propose a detailed schedule. Also, if the customer is a new customer, the generation AI can propose a basic schedule. Also, if the customer is a frequent customer, the generation AI can propose a schedule based on the customer's past usage history. In this way, customer satisfaction is improved by adjusting the level of detail of the schedule based on the customer's importance.

[0083] When generating a reservation schedule, the generation unit can apply different generation algorithms depending on the reservation category. The generation unit identifies the reservation category based, for example, on the type of service or the customer's purpose. For example, in the case of a restaurant reservation, the generation AI can propose a schedule based on the availability of tables. In addition, in the case of a hair salon reservation, the generation AI can also propose a schedule based on the availability of stylists. In addition, in the case of a medical institution reservation, the generation AI can also propose a schedule based on the availability of doctors. In this way, by applying different generation algorithms depending on the reservation category, the accuracy of the schedule is improved.

[0084] When generating a reservation schedule, the generation unit can improve the accuracy of the generation by referring to the customer's past reservation history. The generation unit, for example, uses data mining or machine learning algorithms to refer to the customer's past reservation history. For example, based on reservations that the customer has canceled in the past, the generation AI can propose a schedule that takes into account the risk of cancellation. Also, based on reservations that the customer has given a high rating in the past, the generation AI can propose a schedule that will maintain that rating. Furthermore, if the customer has made reservations during a specific time period in the past, the generation AI can propose a schedule that gives priority to that time period. In this way, by referring to the customer's past reservation history, the accuracy of the schedule is improved.

[0085] The generation unit can estimate the customer's emotions and adjust the length of the schedule based on the estimated customer emotions. The generation unit estimates the customer's emotions using, for example, voice analysis or text analysis. For example, if the customer is in a hurry, the generation AI can suggest a schedule that can be completed in a short time. Also, if the customer is relaxed, the generation AI can suggest a relaxed schedule. Also, if the customer is feeling anxious, the generation AI can suggest a schedule that gives the customer a sense of security. In this way, adjusting the schedule length based on the customer's emotions improves customer satisfaction. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0086] When generating a reservation schedule, the generation unit can determine the priority of the schedule based on the time of reservation submission. The generation unit identifies the time of reservation submission based on, for example, the submission date and time or the submission order. For example, the generation AI can preferentially propose a schedule for a customer who makes a reservation early. The generation AI can also propose a schedule that can be quickly accommodated for an urgent reservation. The generation AI can also give priority to the schedule of a customer who makes reservations regularly. This enables efficient schedule management by determining the priority of the schedule based on the time of reservation submission.

[0087] When generating a reservation schedule, the generation unit can adjust the order of the schedule based on the relevance of reservations. The generation unit identifies the relevance of reservations based on, for example, the type of service or the customer's purpose. For example, if there are multiple reservations on the same day, the generation AI adjusts the schedule taking that relevance into consideration. The generation AI can also propose an efficient schedule for services that a customer will use consecutively. Furthermore, if a customer is attending a specific event, the generation AI can prioritize suggesting a schedule related to that event. This allows for efficient schedule management by adjusting the order of the schedule based on the relevance of reservations.

[0088] When generating a reservation schedule, the generation unit can adjust the use of technical terminology in the schedule according to the customer's level of expertise. The generation unit evaluates the customer's level of expertise, for example, using past transaction history or a customer profile. For example, if the customer has technical expertise, the generation AI can propose a schedule using technical terminology. Also, if the customer is a beginner, the generation AI can propose a schedule in easy-to-understand language. Also, if the customer is knowledgeable in a particular field, the generation AI can propose a schedule using technical terminology related to that field. In this way, customer satisfaction is improved by adjusting the use of technical terminology in the schedule according to the customer's level of expertise.

[0089] The visualization unit can estimate the customer's emotions and adjust the visualization display method based on the estimated customer emotions. The visualization unit estimates the customer's emotions using, for example, voice analysis or text analysis. For example, if the customer is nervous, the generation AI can provide a simple, highly visible display method. If the customer is relaxed, the generation AI can also provide a display method that includes detailed information. If the customer is in a hurry, the generation AI can also provide a display method that focuses on the main points. This improves customer satisfaction by adjusting the visualization display method based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0090] The visualization unit can improve the accuracy of visualization based on the interrelationships between stores during visualization. The visualization unit identifies the interrelationships between stores based on, for example, geographical proximity or business relevance. For example, the generation AI provides the optimal display method based on the relative locations of the stores. The generation AI can also display highly relevant information by taking into account the store's industry and service content. The generation AI can also provide the optimal display method based on the store's business hours and reservation status. In this way, the accuracy of visualization is improved by taking into account the interrelationships between stores.

[0091] The visualization unit can perform visualization while taking into account the store's attribute information. The visualization unit identifies the store's attribute information based on, for example, the store's size and the type of services it provides. For example, the generation AI provides the optimal display method depending on the store's size and industry. The generation AI can also display highly relevant information by taking into account the store's customer demographic and service content. The generation AI can also provide the optimal display method based on the store's location and access information. In this way, the accuracy of visualization is improved by taking into account the store's attribute information.

[0092] When visualizing, the visualization unit can weight the visualization based on the business frequency of the store. The visualization unit identifies the business frequency of the store based on, for example, the number of business days and business hours. For example, the generation AI weights the visualization so that stores with high business frequencies are displayed preferentially. The generation AI can also provide a special display method to highlight stores with low business frequencies. The generation AI can also adjust the display order and size according to the business frequency. In this way, by weighting the visualization based on the business frequency of the store, important information can be displayed preferentially.

[0093] The visualization unit can estimate the customer's emotions and adjust the order in which the visualization results are displayed based on the estimated customer emotions. The visualization unit estimates the customer's emotions using, for example, voice analysis or text analysis. For example, if the customer is nervous, the generation AI can display important information first. Alternatively, if the customer is relaxed, the generation AI can sequentially display detailed information. Alternatively, if the customer is in a hurry, the generation AI can prioritize displaying information that covers the main points. This improves customer satisfaction by adjusting the order in which the visualization results are displayed based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0094] The visualization unit can perform visualization while taking into account the geographic distribution of stores. The visualization unit identifies the geographic distribution of stores, for example, using map data or a geographic information system (GIS). For example, the generation AI displays stores on a map based on the store's location information. The generation AI can also display highly relevant stores by taking into account the geographic proximity of stores. The generation AI can also provide an optimal display method by taking into account the regional characteristics of stores. In this way, the accuracy of visualization is improved by taking into account the geographic distribution of stores.

[0095] The visualization unit can improve the accuracy of the visualization by referring to literature related to the store during visualization. The visualization unit, for example, refers to related literature by using a literature database or by using literature summaries. For example, the generation AI provides detailed information based on research papers and articles related to the store. The generation AI can also provide the optimal display method by referring to literature related to the store's industry and service content. The generation AI can also display highly relevant information based on literature related to the store's history and background. In this way, the accuracy of the visualization is improved by referring to literature related to the store.

[0096] The visualization unit can take the market value of a store into consideration when visualizing. The visualization unit identifies the market value of a store based on, for example, sales data or customer ratings. For example, the generation AI assigns weights so that stores with high market values ​​are displayed preferentially. The generation AI can also provide a special display method to highlight stores with low market values. The generation AI can also adjust the display order and size according to market value. This allows important information to be displayed preferentially by taking the market value of the store into consideration.

[0097] The providing unit can estimate the customer's emotions and adjust the way in which sales messages are expressed based on the estimated customer emotions. The providing unit estimates the customer's emotions using, for example, speech analysis or text analysis. For example, if the customer is relaxed, the generation AI can create sales messages using friendly expressions. If the customer is in a hurry, the generation AI can create sales messages that are concise and to the point. If the customer is feeling anxious, the generation AI can create sales messages using expressions that give a sense of security. In this way, customer satisfaction is improved by adjusting the way in which sales messages are expressed based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0098] When generating sales messages, the provision unit can adjust the level of detail in the sales messages based on the importance of the store. The provision unit evaluates the importance of the store, for example, using past transaction history or customer profiles. For example, the generation AI creates detailed sales messages for important stores. The generation AI can also create sales messages containing basic information for new stores. The generation AI can also create sales messages that emphasize the features of frequently used stores. In this way, customer satisfaction is improved by adjusting the level of detail in the sales messages based on the importance of the store.

[0099] When generating sales messages, the provider can apply different generation algorithms depending on the store category. The provider can identify the store category based, for example, on the type of service or the customer's purpose. For example, in sales messages for a restaurant, the generation AI can emphasize menu and special offer information. In addition, in sales messages for a beauty salon, the generation AI can emphasize introductions to stylists and service details. In addition, in sales messages for a medical institution, the generation AI can emphasize introductions to doctors and medical details. In this way, the accuracy of sales messages is improved by applying different generation algorithms depending on the store category.

[0100] When generating sales messages, the provision unit can improve the accuracy of the generation by referring to the customer's past sales results. The provision unit can refer to the customer's past sales results, for example, using data mining or machine learning algorithms. For example, based on sales messages that the customer has given a high rating in the past, the generation AI can create sales messages that will maintain that rating. Also, based on sales messages that the customer has canceled in the past, the generation AI can create sales messages that take into account the risk of cancellation. Furthermore, if the customer has received sales messages during a specific time period in the past, the generation AI can create sales messages that give priority to that time period. In this way, the accuracy of sales messages can be improved by referring to the customer's past sales results.

[0101] The providing unit can estimate the customer's emotions and adjust the length of the sales message based on the estimated customer emotions. The providing unit estimates the customer's emotions using, for example, voice analysis or text analysis. For example, if the customer is in a hurry, the generation AI can create a short, to-the-point sales message. Alternatively, if the customer is relaxed, the generation AI can create a longer sales message that includes detailed explanations. Alternatively, if the customer is feeling anxious, the generation AI can create a sales message that gives a sense of security. This improves customer satisfaction by adjusting the length of the sales message based on the customer's emotions. Emotion estimation is achieved using, for example, an emotion engine or generation AI with an emotion estimation function. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0102] When generating sales documents, the provision unit can determine the priority of the sales documents based on the time of submission. The provision unit identifies the time of submission of the sales documents based on, for example, the submission date and time or the submission order. For example, if a sales call is made early, the generation AI will prioritize creating sales documents. In addition, the generation AI can also create sales documents that can respond quickly to urgent sales calls. In addition, if sales calls are made regularly, the generation AI can also prioritize consideration of the schedule of those sales calls. In this way, by determining the priority of sales documents based on the time of submission, efficient sales activities become possible.

[0103] When generating sales texts, the provision unit can adjust the order of the sales texts based on the relevance of the sales. The provision unit identifies the relevance of sales based on, for example, the type of service or the customer's objectives. For example, if there are multiple sales on the same day, the generation AI adjusts the sales texts taking that relevance into consideration. The generation AI can also create efficient sales texts for services that a customer will use consecutively. Furthermore, if a customer is attending a specific event, the generation AI can prioritize creating sales texts related to that event. This allows for efficient sales activities by adjusting the order of sales texts based on the relevance of the sales.

[0104] When generating sales pitches, the provision unit can adjust the use of technical terms in the pitches according to the customer's level of expertise. The provision unit assesses the customer's level of expertise, for example, using past transaction history or the customer's profile. For example, if the customer has technical expertise, the generation AI can create the pitches using technical terms. Also, if the customer is a beginner, the generation AI can create the pitches in easy-to-understand language. Also, if the customer is knowledgeable in a particular field, the generation AI can create the pitches using technical terms related to that field. This improves customer satisfaction by adjusting the use of technical terms in the pitches according to the customer's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, generation unit, visualization unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can automatically receive calls from customers using the reception device 38 or microphone 38B of the smart device 14. The reception unit can also be realized by the specific processing unit 290 of the data processing device 12, and after receiving a call from a customer, can check the reservation details and reflect them in the reservation system. The generation unit, for example, is realized by the specific processing unit 290 of the data processing device 12, and automatically generates a reservation schedule in conjunction with the reservation system. The visualization unit, for example, is realized by the display 40A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and visualizes the status of the store and the status of the reservation system. The provision unit, for example, is realized by the specific processing unit 290 of the data processing device 12, and generates a sales message based on information obtained by the visualization unit and sends it via SNS. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, generation unit, visualization unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can automatically receive a call from a customer using the microphone 238 of the smart glasses 214. The reception unit can also be realized by the specific processing unit 290 of the data processing device 12, and after receiving a call from a customer, can check the reservation details and reflect them in the reservation system. The generation unit can be realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates a reservation schedule in conjunction with the reservation system. The visualization unit can be realized, for example, by the display of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and visualizes the status of the store and the status of the reservation system. The provision unit can be realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a sales message based on information obtained by the visualization unit and sends it via SNS. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, generation unit, visualization unit, and provision unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit can automatically receive calls from customers using the microphone 238 of the headset terminal 314. The reception unit can also be realized by the specific processing unit 290 of the data processing device 12, and after receiving a call from a customer, can check the reservation details and reflect them in the reservation system. The generation unit can be realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates a reservation schedule in conjunction with the reservation system. The visualization unit can be realized, for example, by the display 343 of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, and visualizes the status of the store and the status of the reservation system. The provision unit can be realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a sales message based on information obtained by the visualization unit and sends it via SNS. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, generation unit, visualization unit, and provision unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can automatically receive calls from customers using the microphone 238 of the robot 414. The reception unit can also be realized by the specific processing unit 290 of the data processing device 12, and after receiving a call from a customer, can check the reservation details and reflect them in the reservation system. The generation unit can be realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates a reservation schedule in conjunction with the reservation system. The visualization unit can be realized, for example, by the display of the robot 414 or the specific processing unit 290 of the data processing device 12, and visualizes the status of the store and the status of the reservation system. The provision unit can be realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a sales message based on information obtained by the visualization unit and sends it via SNS.

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

[0106] The reception department can analyze a customer's voice tone and speaking patterns to estimate their stress level. For example, if a customer speaks quickly and in a high-pitched voice, the generation AI can infer that the customer is stressed and respond in a calm tone. If the customer speaks slowly, the generation AI can infer that the customer is relaxed and provide a detailed explanation. Furthermore, if the customer speaks repetitively, the generation AI can infer that the customer is confused and provide a concise and clear response. This improves customer satisfaction by adjusting the response method according to the customer's stress level.

[0107] When generating a reservation schedule, the generation unit can improve the accuracy of the schedule by reflecting past customer feedback. For example, if a customer has preferred to make reservations during a specific time period in the past, the generation unit can generate a schedule that takes that time period into consideration. Also, if a customer has highly rated a specific service in the past, the generation unit can suggest a schedule that includes that service. Furthermore, the generation unit can generate a schedule that takes into account the risk of cancellation based on reservations that the customer has canceled in the past. In this way, the accuracy of the schedule can be improved by reflecting past customer feedback.

[0108] The visualization unit can estimate the customer's emotions and adjust the visualization display method based on the estimated customer emotions. For example, if the customer is nervous, the generation AI can provide a simple, highly visible display method. If the customer is relaxed, the generation AI can provide a display method that includes detailed information. If the customer is in a hurry, the generation AI can provide a display method that focuses on the main points. In this way, customer satisfaction is improved by adjusting the visualization display method based on the customer's emotions.

[0109] The provision unit can estimate the customer's emotions when generating sales text and adjust the way the sales text is expressed based on the estimated customer emotions. For example, if the customer is relaxed, the generation AI can create sales text using friendly expressions. If the customer is in a hurry, the generation AI can also create sales text that is concise and to the point. If the customer is feeling anxious, the generation AI can also create sales text using expressions that give a sense of security. In this way, customer satisfaction can be improved by adjusting the way the sales text is expressed based on the customer's emotions.

[0110] The reception unit can prioritize relevant information based on the customer's geographical location information. For example, if a customer calls from a specific area, the generation AI can prioritize providing service and promotion information related to that area. Also, if the customer is traveling, the generation AI can provide information related to the customer's travel destination. Also, if the customer is at a specific event venue, the generation AI can prioritize providing information related to that event. In this way, highly relevant information can be provided by taking the customer's geographical location information into consideration.

[0111] When generating an appointment schedule, the generation unit can estimate the customer's emotions and adjust the length of the schedule based on the estimated customer emotions. For example, if the customer is in a hurry, the generation AI can suggest a schedule that can be completed in a short time. Also, if the customer is relaxed, the generation AI can suggest a relaxed schedule. Also, if the customer is feeling anxious, the generation AI can suggest a schedule that gives the customer a sense of security. In this way, adjusting the length of the schedule based on the customer's emotions improves customer satisfaction.

[0112] When generating sales messages, the provision unit can improve the accuracy of the generation by referring to the customer's past sales results. For example, based on sales messages that the customer has given a high rating in the past, the generation AI can create sales messages that will maintain that rating. Also, based on sales messages that the customer has canceled in the past, the generation AI can create sales messages that take into account the risk of cancellation. Furthermore, if the customer has received sales messages during a specific time period in the past, the generation AI can create sales messages that give priority to that time period. In this way, the accuracy of sales messages can be improved by referring to the customer's past sales results.

[0113] The visualization unit can perform visualization while taking into account the geographic distribution of stores. For example, it can identify the geographic distribution of stores using map data or a geographic information system (GIS). For example, the generation AI can display stores on a map based on the store's location information. The generation AI can also display highly relevant stores by taking into account the geographic proximity of stores. The generation AI can also provide the optimal display method by taking into account the regional characteristics of stores. In this way, the accuracy of visualization is improved by taking into account the geographic distribution of stores.

[0114] When generating sales documents, the provision unit can determine the priority of the sales documents based on the time of submission. For example, the time of submission of the sales documents can be determined based on the submission date and time or the submission order. For example, if a sales call is made early, the generation AI can create sales documents with priority. In addition, the generation AI can create sales documents that can respond quickly to urgent sales calls. In addition, if sales calls are made regularly, the generation AI can also give priority to considering the schedule of those sales calls. This allows for efficient sales activities by determining the priority of sales documents based on the time of submission.

[0115] The reception unit can analyze the customer's social media activity and reflect related information in the response. For example, the customer's social media activity can be analyzed by analyzing the content of posts and followers. For example, if a customer is participating in a specific event on social media, the generation AI can provide information related to that event. Also, if a customer mentions a specific product on social media, the generation AI can provide information related to that product. Also, if a customer mentions a specific problem on social media, the generation AI can provide a solution to that problem. In this way, relevant information can be provided by analyzing the customer's social media activity.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The reception department automatically receives calls from customers. For example, calls from customers can be automatically received using voice recognition technology or an IVR (Interactive Voice Response) system. After receiving a call from a customer, the reception department can also check the reservation details and update them in the reservation system. Step 2: The generation unit generates a reservation schedule based on the information received by the reception unit. For example, the generation unit can automatically link with a reservation system and generate a reservation schedule using API integration or real-time data synchronization. Step 3: The visualization unit displays the status of the store and the status of the reservation system. For example, the status of the store and the status of the reservation system can be visualized using a graphical user interface (GUI) or a text display. Step 4: The providing unit generates sales messages based on the information obtained by the visualization unit and sends them via SNS. For example, the sales messages can be generated and sent via SNS using template-based generation or a specific SNS platform.

[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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0123] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0155] 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 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0162] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0172] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0173] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0174] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0175] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0176] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0178] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0179] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[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] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0182] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0183] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0184] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0185] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0186] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0187] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0188] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0189] [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk that automatically receives calls from customers, a generation unit that generates a reservation schedule based on the information received by the reception unit; a visualization unit that visualizes the status of the store and the status of the reservation system; a providing unit that generates sales messages based on the information obtained by the visualization unit and transmits the sales messages via SNS. A system characterized by:

2. The reception unit Automatically receive calls from customers, confirm reservation details, and update the reservation system 2. The system of claim 1.

3. The generation unit Automatically link with the reservation system to generate a reservation schedule 2. The system of claim 1.

4. The visualization unit Visualize store status and reservation system status 2. The system of claim 1.

5. The providing unit Create sales pitches during off-peak hours and send them to customers via social media 2. The system of claim 1.

6. The reception unit Estimate customer sentiment and adjust the tone and content of your responses based on that sentiment 2. The system of claim 1.

7. The reception unit Analyze the customer's past booking history and choose the appropriate response method 2. The system of claim 1.

8. The reception unit Customize your responses based on the customer's current situation 2. The system of claim 1.

9. The reception unit Choose the best response method based on customer input method 2. The system of claim 1.

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