system

Abstract

The system according to the embodiment aims to improve the efficiency of restaurant operations, and to provide optimal seating arrangements, shift management, and performance forecasting. [Solution] The system according to the embodiment comprises a management unit, a proposal unit, a shift unit, an analysis unit, a prediction unit, and a marketing proposal unit. The management unit manages reservation status. The proposal unit proposes seating allocations based on the reservation status managed by the management unit. The shift unit automates staff shifts. The analysis unit analyzes point-of-sale management data. The prediction unit predicts performance data based on the data analyzed by the analysis unit. The marketing proposal unit understands customer trends and makes marketing proposals.

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, restaurant reservation management, staff shift management, and performance forecasting were all done manually, making efficient operation difficult.

[0005] The system according to the embodiment aims to improve the efficiency of restaurant operations, and to provide optimal seating arrangements, shift management, and performance forecasting. [Means for solving the problem]

[0006] The system according to the embodiment includes a management unit, a proposal unit, a shift unit, an analysis unit, a prediction unit, and a marketing proposal unit. The management unit manages reservation status. The proposal unit proposes seating allocations based on the reservation status managed by the management unit. The shift unit automates staff shifts. The analysis unit analyzes point-of-sale management data. The prediction unit predicts performance data based on the data analyzed by the analysis unit. The marketing proposal unit understands customer trends and makes marketing proposals. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of restaurant operations, and can perform optimal seating arrangements, shift management, and performance forecasting. [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 restaurant management system according to an embodiment of the present invention uses AI to manage restaurant reservations and propose optimal seating arrangements. This restaurant management system uses AI to manage restaurant reservations and propose optimal seating arrangements, preventing duplicate reservations and wasted seats, enabling efficient seat utilization. AI also automates and optimizes staff shifts, reducing labor costs. By creating optimal shifts that take into account staff working hours and break times, labor waste is reduced. Furthermore, AI analyzes point-of-sale (POS) data and proposes menu improvements and inventory management. By understanding popular menu items and inventory trends, efficient menu configuration and inventory management are possible. AI predicts restaurant performance data and enables future forecasts. Based on past data, future sales and customer numbers are predicted, helping to develop management strategies. Finally, AI identifies frequent customers and their tendencies and makes personalized marketing proposals. By analyzing customer preferences and behavioral patterns and providing individually optimized promotions, customer satisfaction is improved. This allows the restaurant management system to efficiently manage reservations, propose seating arrangements, automate shifts, analyze data, forecast performance, and propose marketing proposals.

[0029] A restaurant management system according to an embodiment includes a management unit, a proposal unit, a shift unit, an analysis unit, a prediction unit, and a marketing unit. The management unit manages reservation status. For example, the management unit collects reservation information and uses an algorithm to prevent duplication and waste. The proposal unit proposes optimal seating arrangements based on the reservation status managed by the management unit. The proposal unit proposes optimal seating arrangements based on information provided by the management unit. The shift unit automates and optimizes staff shifts. For example, the shift unit creates optimal shifts taking into account staff working hours and break times. The analysis unit analyzes point-of-sale information management data. For example, the analysis unit identifies best-selling menu items and inventory trends. The prediction unit predicts performance data based on data analyzed by the analysis unit. For example, the prediction unit predicts future sales and customer numbers based on past data. The marketing unit understands customer trends and makes marketing proposals. For example, the marketing unit analyzes customer preferences and behavioral patterns to provide individually optimized promotions. As a result, the restaurant management system according to the embodiment can efficiently manage reservations, propose seating arrangements, automate shifts, analyze data, forecast performance, and make marketing proposals.

[0030] The management unit can collect reservation information and use an algorithm to prevent duplication and waste. The management unit, for example, collects reservation information and uses an algorithm to prevent duplication and waste. For example, the management unit normalizes data and organizes reservation information using a deduplication technique. The management unit can also perform database consistency checks to maintain consistency of the reservation information. For example, the management unit uses an algorithm to detect duplication of reservation information and automatically eliminate the duplication. The management unit can also use a reservation optimization algorithm to prevent waste of reservation information. For example, the management unit updates the reservation information in real time to reflect the latest reservation status to prevent duplicate reservations. This prevents duplication and waste of reservation information, enabling efficient reservation management.

[0031] The proposal unit can propose an optimal seating arrangement based on information provided by the management unit. The proposal unit, for example, proposes an optimal seating arrangement based on information provided by the management unit. For example, the proposal unit proposes an optimal seating arrangement by taking into account seat arrangements and available seat types. The proposal unit can also propose a seating arrangement by taking into account customer reservation information and special requests. For example, the proposal unit proposes a seating arrangement that maximizes seat utilization efficiency based on customer reservation information. The proposal unit can also propose a special seating arrangement in response to a customer's special request. For example, the proposal unit proposes a seating arrangement for a special event or anniversary based on a customer request. In this way, by proposing an optimal seating arrangement based on information from the management unit, seat utilization efficiency is improved.

[0032] The shift department can create shifts based on the working hours and break times of staff members. For example, the shift department creates optimal shifts taking into account the working hours and break times of staff members. For example, the shift department creates shifts taking into account statutory working hours and the frequency of breaks. The shift department can also customize shifts taking into account the skills and qualifications of staff members. For example, the shift department creates optimal shifts based on the skills of staff members. The shift department can also create optimal shifts based on the qualifications of staff members. For example, the shift department creates shifts based on the qualification certificates and skill test results of staff members. In this way, by taking into account the working hours and break times of staff members, it is possible to reduce wasted labor.

[0033] The analysis unit can analyze point-of-sale information management data to understand trends in best-selling menu items and inventory. The analysis unit, for example, analyzes point-of-sale information management data to understand trends in best-selling menu items and inventory. For example, the analysis unit identifies best-selling menu items based on sales volume and sales amount. The analysis unit can also understand inventory trends based on inventory turnover and days of inventory. For example, the analysis unit can propose efficient inventory management based on inventory trends. The analysis unit can also propose menu improvements based on trends in best-selling menu items. For example, the analysis unit can propose the development of new menu items based on data on best-selling menu items. In this way, understanding trends in best-selling menu items and inventory enables efficient menu composition and inventory management.

[0034] The prediction unit can predict future sales and the number of visitors based on past data. The prediction unit, for example, predicts future sales and the number of visitors based on past data. For example, the prediction unit predicts future sales and the number of visitors based on past sales data and visitor count data. The prediction unit can also predict future performance using prediction methods such as time series analysis and regression analysis. For example, the prediction unit predicts future sales trends using time series analysis. The prediction unit can also predict future visitor counts using regression analysis. For example, the prediction unit selects an optimal prediction algorithm based on past performance data. This allows prediction of future sales and the number of visitors based on past data, which can be useful in formulating management strategies.

[0035] The marketing department can analyze customer preferences and behavioral patterns and provide individually optimal promotions. The marketing department, for example, analyzes customer preferences and behavioral patterns and provides individually optimal promotions. For example, the marketing department understands customer preferences based on the customer's purchase history and website browsing history. The marketing department can also analyze customer behavioral patterns based on survey results and feedback data. For example, the marketing department provides optimal promotions based on the customer's preferences. The marketing department can also provide individually customized promotions based on the customer's behavioral patterns. For example, the marketing department suggests specific promotions based on the customer's preferences and behavioral patterns. In this way, by analyzing the customer's preferences and behavioral patterns, it is possible to provide individually optimal promotions and improve customer satisfaction.

[0036] The management unit can analyze past reservation history and select the optimal reservation management algorithm. The management unit, for example, analyzes past reservation history and selects the optimal reservation management algorithm. For example, the management unit prioritizes reserving time slots that the user has frequently used in the past. The management unit can also prioritize reserving seats that the user has preferred in the past. Furthermore, the management unit can take into account the user's past reservation cancellation history and select an algorithm that minimizes the risk of cancellation. For example, the management unit selects the optimal reservation management algorithm based on the past reservation history. In this way, by analyzing past reservation history, the optimal reservation management algorithm can be selected, enabling efficient reservation management.

[0037] The management unit can perform filtering based on the user's current situation and areas of interest when acquiring reservation information. For example, the management unit performs filtering based on the user's current situation and areas of interest when acquiring reservation information. For example, the management unit allows the user to preferentially reserve indoor seats based on the current weather. Furthermore, if the user is interested in a particular dish, the management unit can also preferentially reserve seats where that dish is served. Furthermore, if the user is attending a particular event, the management unit can also preferentially reserve seats related to that event. For example, the management unit filters reservation information based on the user's current situation and areas of interest and suggests optimal reservations. As a result, more appropriate reservation management is possible by filtering based on the user's current situation and areas of interest.

[0038] The management unit can prioritize managing highly relevant reservations based on the user's geographical location information when acquiring reservation information. For example, when acquiring reservation information, the management unit prioritizes managing highly relevant reservations by taking into account the user's geographical location information. For example, if the user is nearby, the management unit prioritizes reserving nearby seats. In addition, if the user is coming from a distance, the management unit can also adjust the reservation to match the arrival time. Furthermore, if the user is interested in a specific area, the management unit can prioritize reserving seats in that area. For example, the management unit prioritizes managing highly relevant reservations based on the user's geographical location information. In this way, highly relevant reservations can be prioritized by taking into account the user's geographical location information.

[0039] The management unit can analyze the user's social media activity when acquiring reservation information and acquire related reservation information. For example, when acquiring reservation information, the management unit analyzes the user's social media activity and acquires related reservation information. For example, if the user follows a specific restaurant on social media, the management unit can prioritize reserving a table at that restaurant. Furthermore, if the user shows interest in a specific event on social media, the management unit can also prioritize reserving a table related to that event. Furthermore, if the user shows interest in a specific dish on social media, the management unit can also prioritize reserving a table where that dish is served. For example, the management unit acquires related reservation information based on the user's social media activity and suggests optimal reservations. In this way, by analyzing the user's social media activity, related reservation information can be acquired and more appropriate reservation management can be achieved.

[0040] The suggestion unit can adjust the level of detail of the proposal based on the importance of the reservation when proposing seating allocation. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the reservation when proposing seating allocation. For example, the suggestion unit makes a detailed seating allocation proposal for an important reservation. The suggestion unit can also make a concise seating allocation proposal for a general reservation. Furthermore, the suggestion unit can make a seating allocation proposal that can be quickly handled for an urgent reservation. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the reservation. In this way, by adjusting the level of detail of the proposal based on the importance of the reservation, more appropriate seating allocation proposals can be made.

[0041] The suggestion unit can apply different suggestion algorithms depending on the category of the reservation when proposing seating arrangements. For example, the suggestion unit applies different suggestion algorithms depending on the category of the reservation when proposing seating arrangements. For example, in the case of a family reservation, the suggestion unit suggests seats where the whole family can sit together. In addition, the suggestion unit can also suggest quiet seats in the case of a business reservation. Furthermore, in the case of a date reservation, the suggestion unit can also suggest romantic seats. For example, the suggestion unit applies different suggestion algorithms depending on the category of the reservation. In this way, by applying different suggestion algorithms depending on the category of the reservation, more appropriate seating arrangement suggestions can be made.

[0042] The proposal unit can determine the priority of proposals based on the time of submission of reservations when proposing seating allocation. For example, the proposal unit determines the priority of proposals based on the time of submission of reservations when proposing seating allocation. For example, the proposal unit gives priority to proposals submitted early. The proposal unit can also quickly propose urgent reservations. Furthermore, the proposal unit can make appropriate seating proposals depending on the time of submission. For example, the proposal unit determines the priority of proposals based on the time of submission of reservations. In this way, by determining the priority of proposals based on the time of submission of reservations, more appropriate seating proposals can be made.

[0043] The suggestion unit can adjust the order of suggestions based on the relevance of reservations when proposing seating allocation. For example, the suggestion unit adjusts the order of suggestions based on the relevance of reservations when proposing seating allocation. For example, the suggestion unit preferentially suggests highly relevant seats based on the user's past reservation history. The suggestion unit can also preferentially suggest highly relevant seats based on the user's current situation. Furthermore, the suggestion unit can also preferentially suggest highly relevant seats based on the user's areas of interest. For example, the suggestion unit adjusts the order of suggestions based on the relevance of reservations. In this way, adjusting the order of suggestions based on the relevance of reservations enables more appropriate seating allocation suggestions.

[0044] When creating shifts, the shift department can create optimal shifts by referring to the staff member's past work history. When creating shifts, the shift department, for example, creates optimal shifts by referring to the staff member's past work history. For example, the shift department creates optimal shifts by taking into account the staff member's past working hours. The shift department can also create optimal shifts by taking into account the staff member's past break times. Furthermore, the shift department can analyze the staff member's past work patterns and create optimal shifts. For example, the shift department creates optimal shifts based on the staff member's past work history. In this way, the optimal shifts can be created by referring to the staff member's past work history.

[0045] The shift department can customize shifts based on the skills and qualifications of staff members when creating shifts. For example, the shift department customizes shifts based on the skills and qualifications of staff members when creating shifts. For example, the shift department creates an optimal shift based on the skills of staff members. The shift department can also create an optimal shift based on the qualifications of staff members. Furthermore, the shift department can create an optimal shift by combining the skills and qualifications of staff members. For example, the shift department creates shifts based on the qualification certificates and skill test results of staff members. This makes it possible to create more appropriate shifts by customizing shifts based on the skills and qualifications of staff members.

[0046] The shift department can create optimal shifts by taking into account the geographical location information of staff members when creating shifts. For example, when creating shifts, the shift department creates optimal shifts by taking into account the geographical location information of staff members. For example, if a staff member lives nearby, the shift department will prioritize creating nearby shifts. In addition, if a staff member commutes from a long distance, the shift department can also create shifts by taking into account the commuting time. Furthermore, the shift department can also create optimal shifts based on the geographical location information of staff members. For example, the shift department creates shifts based on the geographical location information of staff members. In this way, the optimal shifts can be created by taking into account the geographical location information of staff members.

[0047] The shift department can analyze the social media activities of staff members and obtain related shift information when creating shifts. For example, when creating shifts, the shift department analyzes the social media activities of staff members and obtains related shift information. For example, if a staff member is participating in a specific event on social media, the shift department can create a shift related to that event. Also, if a staff member has shown interest in a specific activity on social media, the shift department can create a shift related to that activity. Furthermore, the shift department can analyze the social media activities of staff members and create optimal shifts. For example, the shift department creates shifts based on the social media activities of staff members. In this way, by analyzing the social media activities of staff members, related shift information can be obtained and more appropriate shifts can be created.

[0048] The analysis unit can select the optimal analysis algorithm by referring to past sales data when analyzing data. For example, the analysis unit selects the optimal analysis algorithm by referring to past sales data when analyzing data. For example, the analysis unit selects the optimal analysis algorithm based on past best-selling menu items. The analysis unit can also select the optimal analysis algorithm based on past inventory data. Furthermore, the analysis unit can analyze past sales patterns and select the optimal analysis algorithm. For example, the analysis unit selects the optimal analysis algorithm based on past sales data. This allows the optimal analysis algorithm to be selected by referring to past sales data, enabling efficient data analysis.

[0049] The analysis unit can apply different analysis methods depending on the menu category when analyzing data. For example, the analysis unit applies different analysis methods depending on the menu category when analyzing data. For example, the analysis unit applies a specific analysis method when analyzing data about main dishes. The analysis unit can also apply a different analysis method when analyzing data about desserts. Furthermore, the analysis unit can apply yet another analysis method when analyzing data about drinks. For example, the analysis unit applies different analysis methods depending on the menu category. This enables more appropriate data analysis by applying different analysis methods depending on the menu category.

[0050] The analysis unit can determine the priority of analysis based on the time of submission of sales data when analyzing data. For example, the analysis unit determines the priority of analysis based on the time of submission of sales data when analyzing data. For example, the analysis unit prioritizes analysis of sales data that is submitted early. The analysis unit can also quickly analyze urgent sales data. Furthermore, the analysis unit can perform appropriate data analysis depending on the time of submission. For example, the analysis unit determines the priority of analysis based on the time of submission of sales data. In this way, more appropriate data analysis is possible by determining the priority of analysis based on the time of submission of sales data.

[0051] The analysis unit can improve the accuracy of the analysis by referring to related market data when analyzing data. For example, the analysis unit can improve the accuracy of the analysis by referring to related market data when analyzing data. For example, the analysis unit can improve the accuracy of the analysis by referring to market trend data. The analysis unit can also improve the accuracy of the analysis by referring to competitor data. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to customer feedback data. For example, the analysis unit improves the accuracy of the analysis based on related market data. In this way, the accuracy of the analysis can be improved by referring to related market data.

[0052] The prediction unit can select the optimal prediction algorithm by referring to past performance data when making a prediction. For example, the prediction unit selects the optimal prediction algorithm by referring to past performance data when making a prediction. For example, the prediction unit selects the optimal prediction algorithm based on past sales data. The prediction unit can also select the optimal prediction algorithm based on past customer count data. Furthermore, the prediction unit can analyze past performance patterns and select the optimal prediction algorithm. For example, the prediction unit selects the optimal prediction algorithm based on past performance data. This allows the optimal prediction algorithm to be selected by referring to past performance data, making efficient predictions possible.

[0053] The prediction unit can apply different prediction methods depending on the performance category when making a prediction. For example, the prediction unit applies different prediction methods depending on the performance category when making a prediction. For example, the prediction unit applies a specific prediction method when making a sales prediction. The prediction unit can also apply a different prediction method when making a customer count prediction. Furthermore, the prediction unit can apply yet another prediction method when making an inventory prediction. For example, the prediction unit applies different prediction methods depending on the performance category. In this way, applying different prediction methods depending on the performance category enables more appropriate predictions.

[0054] The prediction unit can determine the priority of predictions based on the submission time of performance data at the time of prediction. For example, the prediction unit determines the priority of predictions based on the submission time of performance data at the time of prediction. For example, the prediction unit gives priority to performance data that is submitted early. The prediction unit can also quickly predict urgent performance data. Furthermore, the prediction unit can make appropriate predictions depending on the submission time. For example, the prediction unit determines the priority of predictions based on the submission time of performance data. In this way, more appropriate predictions can be made by determining the priority of predictions based on the submission time of performance data.

[0055] The prediction unit can improve the accuracy of the prediction by referring to related market data when making a prediction. For example, the prediction unit improves the accuracy of the prediction by referring to related market data when making a prediction. For example, the prediction unit improves the accuracy of the prediction by referring to market trend data. The prediction unit can also improve the accuracy of the prediction by referring to data of competitors. Furthermore, the prediction unit can improve the accuracy of the prediction by referring to customer feedback data. For example, the prediction unit improves the accuracy of the prediction based on related market data. In this way, the accuracy of the prediction can be improved by referring to related market data.

[0056] The marketing department can provide optimal proposals by referring to the customer's past behavioral patterns when making marketing proposals. For example, the marketing department can provide optimal proposals by referring to the customer's past behavioral patterns when making marketing proposals. For example, the marketing department can provide optimal marketing proposals based on the customer's past purchase history. The marketing department can also provide optimal marketing proposals based on the customer's past visit history. Furthermore, the marketing department can also provide optimal marketing proposals based on the customer's past feedback. For example, the marketing department can provide optimal proposals based on the customer's past behavioral patterns. In this way, optimal marketing proposals can be provided by referring to the customer's past behavioral patterns.

[0057] The marketing department can customize the proposal based on the customer's preferences when making a marketing proposal. For example, the marketing department customizes the proposal based on the customer's preferences when making a marketing proposal. For example, the marketing department provides an optimal marketing proposal based on the customer's favorite dishes. The marketing department can also provide an optimal marketing proposal based on the customer's favorite drinks. Furthermore, the marketing department can also provide an optimal marketing proposal based on the customer's favorite events. For example, the marketing department customizes the proposal based on the customer's preferences. In this way, customizing the proposal based on the customer's preferences enables more appropriate marketing proposals.

[0058] The marketing department can provide optimal proposals by taking into consideration the geographical location information of the customer when making marketing proposals. For example, the marketing department can provide optimal proposals by taking into consideration the geographical location information of the customer when making marketing proposals. For example, if the customer lives nearby, the marketing department can prioritize proposals for nearby stores. In addition, if the customer is coming from a distance, the marketing department can also make proposals that are tailored to the customer's arrival time. Furthermore, the marketing department can also provide optimal marketing proposals based on the geographical location information of the customer. For example, the marketing department makes proposals based on the geographical location information of the customer. In this way, optimal marketing proposals can be provided by taking into consideration the geographical location information of the customer.

[0059] The marketing department can analyze a customer's social media activity and provide relevant proposals when making marketing proposals. For example, when making marketing proposals, the marketing department analyzes a customer's social media activity and provides relevant proposals. For example, if a customer follows a particular restaurant on social media, the marketing department can prioritize proposals for that restaurant. Furthermore, if a customer shows interest in a particular event on social media, the marketing department can prioritize proposals related to that event. Furthermore, if a customer shows interest in a particular dish on social media, the marketing department can prioritize proposals related to that dish. For example, the marketing department makes proposals based on a customer's social media activity. In this way, by analyzing a customer's social media activity, relevant proposals can be provided, enabling more appropriate marketing proposals.

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

[0061] The management unit can also analyze reservation trends for specific days of the week or time periods based on the user's past reservation history and suggest optimal reservation slots. For example, the management unit can identify days of the week or time periods on which the user has made frequent reservations in the past and prioritize reservations for those time periods. The management unit can also consider the congestion of reservations on specific days of the week or time periods and suggest reservation slots that avoid congestion. Furthermore, the management unit can analyze the user's past reservation cancellation history and suggest reservations that avoid time periods with a high risk of cancellation. This enables more efficient and satisfying reservation management based on the user's past reservation history.

[0062] The suggestion unit can also analyze the frequency of use and satisfaction of specific seats based on the user's past seating allocation history and propose optimal seating allocation. For example, the suggestion unit can identify seats that the user has preferred in the past and preferentially suggest those seats. The suggestion unit can also analyze the user's feedback on specific seats and propose seats that provide high satisfaction. Furthermore, the suggestion unit can improve seat utilization efficiency by taking into account the frequency of use of specific seats and preferentially suggesting seats that are used less frequently. This makes it possible to propose seating allocations that provide higher satisfaction based on the user's past seating history.

[0063] The shift department can also analyze work patterns for specific days of the week and time periods based on staff members' past work history and create optimal shifts. For example, the shift department can identify days of the week and time periods on which staff members have frequently worked in the past and create shifts for those time periods as a priority. The shift department can also take into account the congestion situation on specific days of the week and time periods and create shifts to avoid congestion. Furthermore, the shift department can analyze staff members' past work performance and create shifts for time periods when performance is high. This makes it possible to create more efficient and satisfying shifts based on staff members' past work history.

[0064] The analysis unit can also analyze the best-selling items of specific menu items or products based on the user's past purchase history and propose an optimal menu configuration. For example, the analysis unit can identify menu items or products that the user has frequently purchased in the past and prioritize those menu items or products. The analysis unit can also analyze the best-selling items of specific menu items or products and propose highly popular menu items or products. Furthermore, the analysis unit can take into account the inventory status of specific menu items or products and propose efficient inventory management. This enables a more efficient and satisfying menu configuration based on the user's past purchase history.

[0065] The prediction unit can also analyze the store visit tendency on specific days of the week or time periods based on the user's past store visit history and predict future store visits. For example, the prediction unit can identify days of the week or time periods on which the user frequently visited the store in the past and predict future store visits based on those time periods. The prediction unit can also take into account the store's crowdedness on specific days of the week or time periods and predict store visits to avoid crowding. Furthermore, the prediction unit can analyze the user's past store visit cancellation history and predict store visits to avoid time periods with a high risk of cancellations. This enables more accurate and efficient store visit predictions based on the user's past store visit history.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The management unit manages the reservation status. For example, the management unit collects reservation information and uses an algorithm to prevent duplication and waste. Step 2: The proposal unit proposes an optimal seating arrangement based on the reservation status managed by the management unit. The proposal unit proposes an optimal seating arrangement based on, for example, information provided by the management unit. Step 3: The shift department automates and optimizes staff shifts. The shift department creates optimal shifts, taking into account, for example, staff working hours and break times. Step 4: The analysis unit analyzes the point-of-sale data. For example, the analysis unit identifies best-selling menu items and inventory trends. Step 5: The prediction unit predicts performance data based on the data analyzed by the analysis unit. For example, the prediction unit predicts future sales and the number of visitors based on past data. Step 6: The marketing department identifies customer trends and makes marketing proposals. For example, the marketing department analyzes customer preferences and behavioral patterns to provide individually tailored promotions.

[0068] (Example 2) A restaurant management system according to an embodiment of the present invention uses AI to manage restaurant reservations and propose optimal seating arrangements. This restaurant management system uses AI to manage restaurant reservations and propose optimal seating arrangements, preventing duplicate reservations and wasted seats, enabling efficient seat utilization. AI also automates and optimizes staff shifts, reducing labor costs. By creating optimal shifts that take into account staff working hours and break times, labor waste is reduced. Furthermore, AI analyzes point-of-sale (POS) data and proposes menu improvements and inventory management. By understanding popular menu items and inventory trends, efficient menu configuration and inventory management are possible. AI predicts restaurant performance data and enables future forecasts. Based on past data, future sales and customer numbers are predicted, helping to develop management strategies. Finally, AI identifies frequent customers and their tendencies and makes personalized marketing proposals. By analyzing customer preferences and behavioral patterns and providing individually optimized promotions, customer satisfaction is improved. This allows the restaurant management system to efficiently manage reservations, propose seating arrangements, automate shifts, analyze data, forecast performance, and propose marketing proposals.

[0069] A restaurant management system according to an embodiment includes a management unit, a proposal unit, a shift unit, an analysis unit, a prediction unit, and a marketing unit. The management unit manages reservation status. For example, the management unit collects reservation information and uses an algorithm to prevent duplication and waste. The proposal unit proposes optimal seating arrangements based on the reservation status managed by the management unit. The proposal unit proposes optimal seating arrangements based on information provided by the management unit. The shift unit automates and optimizes staff shifts. For example, the shift unit creates optimal shifts taking into account staff working hours and break times. The analysis unit analyzes point-of-sale information management data. For example, the analysis unit identifies best-selling menu items and inventory trends. The prediction unit predicts performance data based on data analyzed by the analysis unit. For example, the prediction unit predicts future sales and customer numbers based on past data. The marketing unit understands customer trends and makes marketing proposals. For example, the marketing unit analyzes customer preferences and behavioral patterns to provide individually optimized promotions. As a result, the restaurant management system according to the embodiment can efficiently manage reservations, propose seating arrangements, automate shifts, analyze data, forecast performance, and make marketing proposals.

[0070] The management unit can collect reservation information and use an algorithm to prevent duplication and waste. The management unit, for example, collects reservation information and uses an algorithm to prevent duplication and waste. For example, the management unit normalizes data and organizes reservation information using a deduplication technique. The management unit can also perform database consistency checks to maintain consistency of the reservation information. For example, the management unit uses an algorithm to detect duplication of reservation information and automatically eliminate the duplication. The management unit can also use a reservation optimization algorithm to prevent waste of reservation information. For example, the management unit updates the reservation information in real time to reflect the latest reservation status to prevent duplicate reservations. This prevents duplication and waste of reservation information, enabling efficient reservation management.

[0071] The proposal unit can propose an optimal seating arrangement based on information provided by the management unit. The proposal unit, for example, proposes an optimal seating arrangement based on information provided by the management unit. For example, the proposal unit proposes an optimal seating arrangement by taking into account seat arrangements and available seat types. The proposal unit can also propose a seating arrangement by taking into account customer reservation information and special requests. For example, the proposal unit proposes a seating arrangement that maximizes seat utilization efficiency based on customer reservation information. The proposal unit can also propose a special seating arrangement in response to a customer's special request. For example, the proposal unit proposes a seating arrangement for a special event or anniversary based on a customer request. In this way, by proposing an optimal seating arrangement based on information from the management unit, seat utilization efficiency is improved.

[0072] The shift department can create shifts based on the working hours and break times of staff members. For example, the shift department creates optimal shifts taking into account the working hours and break times of staff members. For example, the shift department creates shifts taking into account statutory working hours and the frequency of breaks. The shift department can also customize shifts taking into account the skills and qualifications of staff members. For example, the shift department creates optimal shifts based on the skills of staff members. The shift department can also create optimal shifts based on the qualifications of staff members. For example, the shift department creates shifts based on the qualification certificates and skill test results of staff members. In this way, by taking into account the working hours and break times of staff members, it is possible to reduce wasted labor.

[0073] The analysis unit can analyze point-of-sale information management data to understand trends in best-selling menu items and inventory. The analysis unit, for example, analyzes point-of-sale information management data to understand trends in best-selling menu items and inventory. For example, the analysis unit identifies best-selling menu items based on sales volume and sales amount. The analysis unit can also understand inventory trends based on inventory turnover and days of inventory. For example, the analysis unit can propose efficient inventory management based on inventory trends. The analysis unit can also propose menu improvements based on trends in best-selling menu items. For example, the analysis unit can propose the development of new menu items based on data on best-selling menu items. In this way, understanding trends in best-selling menu items and inventory enables efficient menu composition and inventory management.

[0074] The prediction unit can predict future sales and the number of visitors based on past data. The prediction unit, for example, predicts future sales and the number of visitors based on past data. For example, the prediction unit predicts future sales and the number of visitors based on past sales data and visitor count data. The prediction unit can also predict future performance using prediction methods such as time series analysis and regression analysis. For example, the prediction unit predicts future sales trends using time series analysis. The prediction unit can also predict future visitor counts using regression analysis. For example, the prediction unit selects an optimal prediction algorithm based on past performance data. This allows prediction of future sales and the number of visitors based on past data, which can be useful in formulating management strategies.

[0075] The marketing department can analyze customer preferences and behavioral patterns and provide individually optimal promotions. The marketing department, for example, analyzes customer preferences and behavioral patterns and provides individually optimal promotions. For example, the marketing department understands customer preferences based on the customer's purchase history and website browsing history. The marketing department can also analyze customer behavioral patterns based on survey results and feedback data. For example, the marketing department provides optimal promotions based on the customer's preferences. The marketing department can also provide individually customized promotions based on the customer's behavioral patterns. For example, the marketing department suggests specific promotions based on the customer's preferences and behavioral patterns. In this way, by analyzing the customer's preferences and behavioral patterns, it is possible to provide individually optimal promotions and improve customer satisfaction.

[0076] The restaurant management system further includes a management unit that estimates a user's emotions and adjusts reservation priorities based on the estimated user emotions. The management unit, for example, estimates a user's emotions and adjusts reservation priorities based on the estimated user emotions. For example, if a user is feeling stressed, the management unit may prioritize reserving seats where the user can relax. Furthermore, if a user is in a hurry, the management unit may prioritize reserving seats that can be quickly accommodated. Furthermore, if a user is planning a special event, the management unit may prioritize reserving special seats. For example, the management unit may analyze the user's emotions using an algorithm that estimates the user's emotions and adjust reservation priorities. This enables more appropriate reservation management by adjusting reservation priorities based on the user's emotions.

[0077] The management unit can analyze past reservation history and select the optimal reservation management algorithm. The management unit, for example, analyzes past reservation history and selects the optimal reservation management algorithm. For example, the management unit prioritizes reserving time slots that the user has frequently used in the past. The management unit can also prioritize reserving seats that the user has preferred in the past. Furthermore, the management unit can take into account the user's past reservation cancellation history and select an algorithm that minimizes the risk of cancellation. For example, the management unit selects the optimal reservation management algorithm based on the past reservation history. In this way, by analyzing past reservation history, the optimal reservation management algorithm can be selected, enabling efficient reservation management.

[0078] The management unit can perform filtering based on the user's current situation and areas of interest when acquiring reservation information. For example, the management unit performs filtering based on the user's current situation and areas of interest when acquiring reservation information. For example, the management unit allows the user to preferentially reserve indoor seats based on the current weather. Furthermore, if the user is interested in a particular dish, the management unit can also preferentially reserve seats where that dish is served. Furthermore, if the user is attending a particular event, the management unit can also preferentially reserve seats related to that event. For example, the management unit filters reservation information based on the user's current situation and areas of interest and suggests optimal reservations. As a result, more appropriate reservation management is possible by filtering based on the user's current situation and areas of interest.

[0079] The management unit can estimate the user's emotions and evaluate the risk of reservation cancellation based on the estimated user emotions. The management unit, for example, estimates the user's emotions and evaluates the risk of reservation cancellation based on the estimated user emotions. For example, the management unit evaluates the cancellation risk as high if the user is feeling stressed. The management unit can also evaluate the cancellation risk as low if the user is relaxed. Furthermore, the management unit can evaluate the cancellation risk as medium if the user is in a hurry. For example, the management unit analyzes the user's emotions using an algorithm for estimating user emotions and evaluates the cancellation risk of the reservation. In this way, the cancellation risk can be minimized by evaluating the cancellation risk of the reservation based on the user's emotions.

[0080] The management unit can prioritize managing highly relevant reservations based on the user's geographical location information when acquiring reservation information. For example, when acquiring reservation information, the management unit prioritizes managing highly relevant reservations by taking into account the user's geographical location information. For example, if the user is nearby, the management unit prioritizes reserving nearby seats. In addition, if the user is coming from a distance, the management unit can also adjust the reservation to match the arrival time. Furthermore, if the user is interested in a specific area, the management unit can prioritize reserving seats in that area. For example, the management unit prioritizes managing highly relevant reservations based on the user's geographical location information. In this way, highly relevant reservations can be prioritized by taking into account the user's geographical location information.

[0081] The management unit can analyze the user's social media activity when acquiring reservation information and acquire related reservation information. For example, when acquiring reservation information, the management unit analyzes the user's social media activity and acquires related reservation information. For example, if the user follows a specific restaurant on social media, the management unit can prioritize reserving a table at that restaurant. Furthermore, if the user shows interest in a specific event on social media, the management unit can also prioritize reserving a table related to that event. Furthermore, if the user shows interest in a specific dish on social media, the management unit can also prioritize reserving a table where that dish is served. For example, the management unit acquires related reservation information based on the user's social media activity and suggests optimal reservations. In this way, by analyzing the user's social media activity, related reservation information can be acquired and more appropriate reservation management can be achieved.

[0082] The suggestion unit can estimate the user's emotions and adjust the seating arrangement suggestion method based on the estimated user's emotions. The suggestion unit, for example, estimates the user's emotions and adjusts the seating arrangement suggestion method based on the estimated user's emotions. For example, the suggestion unit can suggest spacious seats when the user is relaxed. Furthermore, the suggestion unit can suggest seats that can be accommodated quickly when the user is in a hurry. Furthermore, the suggestion unit can suggest special seats when the user is planning a special event. For example, the suggestion unit can analyze the user's emotions using an algorithm that estimates the user's emotions and adjust the seating arrangement suggestion method. In this way, by adjusting the seating arrangement suggestion method based on the user's emotions, more appropriate seating arrangement suggestions can be made.

[0083] The suggestion unit can adjust the level of detail of the proposal based on the importance of the reservation when proposing seating allocation. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the reservation when proposing seating allocation. For example, the suggestion unit makes a detailed seating allocation proposal for an important reservation. The suggestion unit can also make a concise seating allocation proposal for a general reservation. Furthermore, the suggestion unit can make a seating allocation proposal that can be quickly handled for an urgent reservation. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the reservation. In this way, by adjusting the level of detail of the proposal based on the importance of the reservation, more appropriate seating allocation proposals can be made.

[0084] The suggestion unit can apply different suggestion algorithms depending on the category of the reservation when proposing seating arrangements. For example, the suggestion unit applies different suggestion algorithms depending on the category of the reservation when proposing seating arrangements. For example, in the case of a family reservation, the suggestion unit suggests seats where the whole family can sit together. In addition, the suggestion unit can also suggest quiet seats in the case of a business reservation. Furthermore, in the case of a date reservation, the suggestion unit can also suggest romantic seats. For example, the suggestion unit applies different suggestion algorithms depending on the category of the reservation. In this way, by applying different suggestion algorithms depending on the category of the reservation, more appropriate seating arrangement suggestions can be made.

[0085] The suggestion unit can estimate the user's emotions and determine seating priorities based on the estimated user emotions. The suggestion unit, for example, estimates the user's emotions and determines seating priorities based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can preferentially suggest seats where the user can relax. Also, if the user is in a hurry, the suggestion unit can preferentially suggest seats where a quick response is possible. Furthermore, if the user is planning a special event, the suggestion unit can preferentially suggest special seats. For example, the suggestion unit analyzes the user's emotions using an algorithm that estimates the user's emotions and determines seating priorities. In this way, by determining seating priorities based on the user's emotions, more appropriate seating suggestions can be made.

[0086] The proposal unit can determine the priority of proposals based on the time of submission of reservations when proposing seating allocation. For example, the proposal unit determines the priority of proposals based on the time of submission of reservations when proposing seating allocation. For example, the proposal unit gives priority to proposals submitted early. The proposal unit can also quickly propose urgent reservations. Furthermore, the proposal unit can make appropriate seating proposals depending on the time of submission. For example, the proposal unit determines the priority of proposals based on the time of submission of reservations. In this way, by determining the priority of proposals based on the time of submission of reservations, more appropriate seating proposals can be made.

[0087] The suggestion unit can adjust the order of suggestions based on the relevance of reservations when proposing seating allocation. For example, the suggestion unit adjusts the order of suggestions based on the relevance of reservations when proposing seating allocation. For example, the suggestion unit preferentially suggests highly relevant seats based on the user's past reservation history. The suggestion unit can also preferentially suggest highly relevant seats based on the user's current situation. Furthermore, the suggestion unit can also preferentially suggest highly relevant seats based on the user's areas of interest. For example, the suggestion unit adjusts the order of suggestions based on the relevance of reservations. In this way, adjusting the order of suggestions based on the relevance of reservations enables more appropriate seating allocation suggestions.

[0088] The shift unit can estimate the user's emotions and adjust the shift creation method based on the estimated user's emotions. The shift unit, for example, estimates the user's emotions and adjusts the shift creation method based on the estimated user's emotions. For example, if the user is feeling stressed, the shift unit creates a shift that allows the user to relax. Also, if the user is in a hurry, the shift unit can create a shift that allows the user to respond quickly. Furthermore, if the user is planning a special event, the shift unit can create a special shift. For example, the shift unit analyzes the user's emotions using an algorithm that estimates the user's emotions and adjusts the shift creation method. In this way, by adjusting the shift creation method based on the user's emotions, more appropriate shift creation is possible.

[0089] When creating shifts, the shift department can create optimal shifts by referring to the staff member's past work history. When creating shifts, the shift department, for example, creates optimal shifts by referring to the staff member's past work history. For example, the shift department creates optimal shifts by taking into account the staff member's past working hours. The shift department can also create optimal shifts by taking into account the staff member's past break times. Furthermore, the shift department can analyze the staff member's past work patterns and create optimal shifts. For example, the shift department creates optimal shifts based on the staff member's past work history. In this way, the optimal shifts can be created by referring to the staff member's past work history.

[0090] The shift department can customize shifts based on the skills and qualifications of staff members when creating shifts. For example, the shift department customizes shifts based on the skills and qualifications of staff members when creating shifts. For example, the shift department creates an optimal shift based on the skills of staff members. The shift department can also create an optimal shift based on the qualifications of staff members. Furthermore, the shift department can create an optimal shift by combining the skills and qualifications of staff members. For example, the shift department creates shifts based on the qualification certificates and skill test results of staff members. This makes it possible to create more appropriate shifts by customizing shifts based on the skills and qualifications of staff members.

[0091] The shift unit can estimate the user's emotions and determine shift priorities based on the estimated user's emotions. The shift unit, for example, estimates the user's emotions and determines shift priorities based on the estimated user's emotions. For example, if the user is feeling stressed, the shift unit can prioritize creating shifts that allow the user to relax. Also, if the user is in a hurry, the shift unit can prioritize creating shifts that allow the user to respond quickly. Furthermore, if the user is planning a special event, the shift unit can prioritize creating special shifts. For example, the shift unit can analyze the user's emotions using an algorithm that estimates the user's emotions and determine shift priorities. As a result, more appropriate shifts can be created by prioritizing shifts based on the user's emotions.

[0092] The shift department can create optimal shifts by taking into account the geographical location information of staff members when creating shifts. For example, when creating shifts, the shift department creates optimal shifts by taking into account the geographical location information of staff members. For example, if a staff member lives nearby, the shift department will prioritize creating nearby shifts. In addition, if a staff member commutes from a long distance, the shift department can also create shifts by taking into account the commuting time. Furthermore, the shift department can also create optimal shifts based on the geographical location information of staff members. For example, the shift department creates shifts based on the geographical location information of staff members. In this way, the optimal shifts can be created by taking into account the geographical location information of staff members.

[0093] The shift department can analyze the social media activities of staff members and obtain related shift information when creating shifts. For example, when creating shifts, the shift department analyzes the social media activities of staff members and obtains related shift information. For example, if a staff member is participating in a specific event on social media, the shift department can create a shift related to that event. Also, if a staff member has shown interest in a specific activity on social media, the shift department can create a shift related to that activity. Furthermore, the shift department can analyze the social media activities of staff members and create optimal shifts. For example, the shift department creates shifts based on the social media activities of staff members. In this way, by analyzing the social media activities of staff members, related shift information can be obtained and more appropriate shifts can be created.

[0094] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the data analysis method based on the estimated user's emotions. For example, the analysis unit performs a detailed data analysis when the user is relaxed. The analysis unit can also perform a brief data analysis when the user is in a hurry. Furthermore, the analysis unit can perform a special data analysis when the user is planning a special event. For example, the analysis unit analyzes the user's emotions using an algorithm that estimates the user's emotions and adjusts the data analysis method. In this way, adjusting the data analysis method based on the user's emotions enables more appropriate data analysis.

[0095] The analysis unit can select the optimal analysis algorithm by referring to past sales data when analyzing data. For example, the analysis unit selects the optimal analysis algorithm by referring to past sales data when analyzing data. For example, the analysis unit selects the optimal analysis algorithm based on past best-selling menu items. The analysis unit can also select the optimal analysis algorithm based on past inventory data. Furthermore, the analysis unit can analyze past sales patterns and select the optimal analysis algorithm. For example, the analysis unit selects the optimal analysis algorithm based on past sales data. This allows the optimal analysis algorithm to be selected by referring to past sales data, enabling efficient data analysis.

[0096] The analysis unit can apply different analysis methods depending on the menu category when analyzing data. For example, the analysis unit applies different analysis methods depending on the menu category when analyzing data. For example, the analysis unit applies a specific analysis method when analyzing data about main dishes. The analysis unit can also apply a different analysis method when analyzing data about desserts. Furthermore, the analysis unit can apply yet another analysis method when analyzing data about drinks. For example, the analysis unit applies different analysis methods depending on the menu category. This enables more appropriate data analysis by applying different analysis methods depending on the menu category.

[0097] The analysis unit can estimate the user's emotions and determine the priorities of data analysis based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions and determines the priorities of data analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can prioritize data analysis that will help the user relax. Also, if the user is in a hurry, the analysis unit can prioritize data analysis that will allow a quick response. Furthermore, if the user is planning a special event, the analysis unit can prioritize special data analysis. For example, the analysis unit uses an algorithm that estimates the user's emotions to analyze the user's emotions and determine the priorities of data analysis. This enables more appropriate data analysis by determining the priorities of data analysis based on the user's emotions.

[0098] The analysis unit can determine the priority of analysis based on the time of submission of sales data when analyzing data. For example, the analysis unit determines the priority of analysis based on the time of submission of sales data when analyzing data. For example, the analysis unit prioritizes analysis of sales data that is submitted early. The analysis unit can also quickly analyze urgent sales data. Furthermore, the analysis unit can perform appropriate data analysis depending on the time of submission. For example, the analysis unit determines the priority of analysis based on the time of submission of sales data. In this way, more appropriate data analysis is possible by determining the priority of analysis based on the time of submission of sales data.

[0099] The analysis unit can improve the accuracy of the analysis by referring to related market data when analyzing data. For example, the analysis unit can improve the accuracy of the analysis by referring to related market data when analyzing data. For example, the analysis unit can improve the accuracy of the analysis by referring to market trend data. The analysis unit can also improve the accuracy of the analysis by referring to competitor data. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to customer feedback data. For example, the analysis unit improves the accuracy of the analysis based on related market data. In this way, the accuracy of the analysis can be improved by referring to related market data.

[0100] The prediction unit can estimate the user's emotions and adjust the prediction method based on the estimated user's emotions. For example, the prediction unit estimates the user's emotions and adjusts the prediction method based on the estimated user's emotions. For example, the prediction unit makes a detailed prediction when the user is relaxed. The prediction unit can also make a concise prediction when the user is in a hurry. Furthermore, the prediction unit can make a special prediction when the user is planning a special event. For example, the prediction unit analyzes the user's emotions using an algorithm for estimating the user's emotions and adjusts the prediction method. In this way, adjusting the prediction method based on the user's emotions enables more accurate predictions.

[0101] The prediction unit can select the optimal prediction algorithm by referring to past performance data when making a prediction. For example, the prediction unit selects the optimal prediction algorithm by referring to past performance data when making a prediction. For example, the prediction unit selects the optimal prediction algorithm based on past sales data. The prediction unit can also select the optimal prediction algorithm based on past customer count data. Furthermore, the prediction unit can analyze past performance patterns and select the optimal prediction algorithm. For example, the prediction unit selects the optimal prediction algorithm based on past performance data. This allows the optimal prediction algorithm to be selected by referring to past performance data, making efficient predictions possible.

[0102] The prediction unit can apply different prediction methods depending on the performance category when making a prediction. For example, the prediction unit applies different prediction methods depending on the performance category when making a prediction. For example, the prediction unit applies a specific prediction method when making a sales prediction. The prediction unit can also apply a different prediction method when making a customer count prediction. Furthermore, the prediction unit can apply yet another prediction method when making an inventory prediction. For example, the prediction unit applies different prediction methods depending on the performance category. In this way, applying different prediction methods depending on the performance category enables more appropriate predictions.

[0103] The prediction unit can estimate the user's emotions and determine the priority of predictions based on the estimated user's emotions. The prediction unit, for example, estimates the user's emotions and determines the priority of predictions based on the estimated user's emotions. For example, if the user is feeling stressed, the prediction unit can prioritize predictions that will help the user relax. Also, if the user is in a hurry, the prediction unit can prioritize predictions that will allow a quick response. Furthermore, if the user is planning a special event, the prediction unit can prioritize special predictions. For example, the prediction unit can analyze the user's emotions using an algorithm that estimates the user's emotions and determine the priority of predictions. As a result, more accurate predictions can be made by determining the priority of predictions based on the user's emotions.

[0104] The prediction unit can determine the priority of predictions based on the submission time of performance data at the time of prediction. For example, the prediction unit determines the priority of predictions based on the submission time of performance data at the time of prediction. For example, the prediction unit gives priority to performance data that is submitted early. The prediction unit can also quickly predict urgent performance data. Furthermore, the prediction unit can make appropriate predictions depending on the submission time. For example, the prediction unit determines the priority of predictions based on the submission time of performance data. In this way, more appropriate predictions can be made by determining the priority of predictions based on the submission time of performance data.

[0105] The prediction unit can improve the accuracy of the prediction by referring to related market data when making a prediction. For example, the prediction unit improves the accuracy of the prediction by referring to related market data when making a prediction. For example, the prediction unit improves the accuracy of the prediction by referring to market trend data. The prediction unit can also improve the accuracy of the prediction by referring to data of competitors. Furthermore, the prediction unit can improve the accuracy of the prediction by referring to customer feedback data. For example, the prediction unit improves the accuracy of the prediction based on related market data. In this way, the accuracy of the prediction can be improved by referring to related market data.

[0106] The marketing department can estimate the user's emotions and adjust the method of marketing proposals based on the estimated user's emotions. The marketing department, for example, estimates the user's emotions and adjusts the method of marketing proposals based on the estimated user's emotions. For example, the marketing department can make detailed marketing proposals when the user is relaxed. Also, the marketing department can make brief marketing proposals when the user is in a hurry. Furthermore, the marketing department can make special marketing proposals when the user is planning a special event. For example, the marketing department can analyze the user's emotions using an algorithm that estimates the user's emotions and adjust the method of marketing proposals. In this way, by adjusting the method of marketing proposals based on the user's emotions, more appropriate marketing proposals can be made.

[0107] The marketing department can provide optimal proposals by referring to the customer's past behavioral patterns when making marketing proposals. For example, the marketing department can provide optimal proposals by referring to the customer's past behavioral patterns when making marketing proposals. For example, the marketing department can provide optimal marketing proposals based on the customer's past purchase history. The marketing department can also provide optimal marketing proposals based on the customer's past visit history. Furthermore, the marketing department can also provide optimal marketing proposals based on the customer's past feedback. For example, the marketing department can provide optimal proposals based on the customer's past behavioral patterns. In this way, optimal marketing proposals can be provided by referring to the customer's past behavioral patterns.

[0108] The marketing department can customize the proposal based on the customer's preferences when making a marketing proposal. For example, the marketing department customizes the proposal based on the customer's preferences when making a marketing proposal. For example, the marketing department provides an optimal marketing proposal based on the customer's favorite dishes. The marketing department can also provide an optimal marketing proposal based on the customer's favorite drinks. Furthermore, the marketing department can also provide an optimal marketing proposal based on the customer's favorite events. For example, the marketing department customizes the proposal based on the customer's preferences. In this way, customizing the proposal based on the customer's preferences enables more appropriate marketing proposals.

[0109] The marketing department can estimate the user's emotions and prioritize marketing proposals based on the estimated user's emotions. The marketing department, for example, estimates the user's emotions and prioritizes marketing proposals based on the estimated user's emotions. For example, if the user is feeling stressed, the marketing department can prioritize marketing proposals that will help the user relax. Also, if the user is in a hurry, the marketing department can prioritize marketing proposals that can be responded to quickly. Furthermore, if the user is planning a special event, the marketing department can prioritize special marketing proposals. For example, the marketing department uses an algorithm that estimates the user's emotions to analyze the user's emotions and prioritize marketing proposals. In this way, by prioritizing marketing proposals based on the user's emotions, more appropriate marketing proposals can be made.

[0110] The marketing department can provide optimal proposals by taking into consideration the geographical location information of the customer when making marketing proposals. For example, the marketing department can provide optimal proposals by taking into consideration the geographical location information of the customer when making marketing proposals. For example, if the customer lives nearby, the marketing department can prioritize proposals for nearby stores. In addition, if the customer is coming from a distance, the marketing department can also make proposals that are tailored to the customer's arrival time. Furthermore, the marketing department can also provide optimal marketing proposals based on the geographical location information of the customer. For example, the marketing department makes proposals based on the geographical location information of the customer. In this way, optimal marketing proposals can be provided by taking into consideration the geographical location information of the customer.

[0111] The marketing department can analyze a customer's social media activity and provide relevant proposals when making marketing proposals. For example, when making marketing proposals, the marketing department analyzes a customer's social media activity and provides relevant proposals. For example, if a customer follows a particular restaurant on social media, the marketing department can prioritize proposals for that restaurant. Furthermore, if a customer shows interest in a particular event on social media, the marketing department can prioritize proposals related to that event. Furthermore, if a customer shows interest in a particular dish on social media, the marketing department can prioritize proposals related to that dish. For example, the marketing department makes proposals based on a customer's social media activity. In this way, by analyzing a customer's social media activity, relevant proposals can be provided, enabling more appropriate marketing proposals. === Hard Collateral 1-1 === Each of the multiple elements, including the management unit, proposal unit, shift unit, analysis unit, prediction unit, and marketing unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the management unit is realized by the control unit 46A of the smart device 14 collecting reservation information and using an algorithm to prevent duplication and waste. The proposal unit proposes optimal seating arrangements by the specific processing unit 290 of the data processing device 12. The shift unit automates and optimizes staff shifts by the specific processing unit 290 of the data processing device 12. The analysis unit analyzes point-of-sale management data by the specific processing unit 290 of the data processing device 12. The prediction unit predicts performance data by the specific processing unit 290 of the data processing device 12. The marketing unit grasps customer trends and makes marketing proposals by the specific processing unit 290 of the data processing device 12. Furthermore, the management unit is realized by using an algorithm to estimate user emotions and adjust reservation priorities based on the estimated user emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the management unit, proposal unit, shift unit, analysis unit, prediction unit, and marketing 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 management unit is realized by the control unit 46A of the smart glasses 214 using an algorithm that collects reservation information and prevents duplication and waste. The proposal unit proposes optimal seating arrangements using the specific processing unit 290 of the data processing device 12. The shift unit automates and optimizes staff shifts using the specific processing unit 290 of the data processing device 12. The analysis unit analyzes point-of-sale management data using the specific processing unit 290 of the data processing device 12. The prediction unit predicts performance data using the specific processing unit 290 of the data processing device 12. The marketing unit grasps customer trends and makes marketing proposals using the specific processing unit 290 of the data processing device 12. Furthermore, the management unit is realized by an algorithm that estimates user emotions and adjusts reservation priorities based on the estimated user emotions. === Hard Collateral 1-3 === Each of the multiple elements, including the management unit, proposal unit, shift unit, analysis unit, prediction unit, and marketing unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the management unit is realized by the control unit 46A of the headset terminal 314 collecting reservation information and using an algorithm to prevent duplication and waste. The proposal unit proposes optimal seating arrangements by the specific processing unit 290 of the data processing device 12. The shift unit automates and optimizes staff shifts by the specific processing unit 290 of the data processing device 12. The analysis unit analyzes point-of-sale management data by the specific processing unit 290 of the data processing device 12. The prediction unit predicts performance data by the specific processing unit 290 of the data processing device 12. The marketing unit grasps customer trends and makes marketing proposals by the specific processing unit 290 of the data processing device 12. Furthermore, the management unit is realized by using an algorithm that estimates user emotions and adjusts reservation priorities based on the estimated user emotions. === Hard Collateral 1-4 === Each of the multiple elements, including the management unit, proposal unit, shift unit, analysis unit, prediction unit, and marketing unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the management unit is realized by the control unit 46A of the robot 414 collecting reservation information and using an algorithm to prevent duplication and waste. The proposal unit proposes optimal seating arrangements by the specific processing unit 290 of the data processing device 12. The shift unit automates and optimizes staff shifts by the specific processing unit 290 of the data processing device 12. The analysis unit analyzes point-of-sale management data by the specific processing unit 290 of the data processing device 12. The prediction unit predicts performance data by the specific processing unit 290 of the data processing device 12. The marketing unit grasps customer trends and makes marketing proposals by the specific processing unit 290 of the data processing device 12. Furthermore, the management unit is realized by using an algorithm to estimate user emotions and adjust reservation priorities based on the estimated user emotions.

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

[0113] The management unit can also analyze reservation trends for specific days of the week or time periods based on the user's past reservation history and suggest optimal reservation slots. For example, the management unit can identify days of the week or time periods on which the user has made frequent reservations in the past and prioritize reservations for those time periods. The management unit can also consider the congestion of reservations on specific days of the week or time periods and suggest reservation slots that avoid congestion. Furthermore, the management unit can analyze the user's past reservation cancellation history and suggest reservations that avoid time periods with a high risk of cancellation. This enables more efficient and satisfying reservation management based on the user's past reservation history.

[0114] The suggestion unit can also analyze the frequency of use and satisfaction of specific seats based on the user's past seating allocation history and propose optimal seating allocation. For example, the suggestion unit can identify seats that the user has preferred in the past and preferentially suggest those seats. The suggestion unit can also analyze the user's feedback on specific seats and propose seats that provide high satisfaction. Furthermore, the suggestion unit can improve seat utilization efficiency by taking into account the frequency of use of specific seats and preferentially suggesting seats that are used less frequently. This makes it possible to propose seating allocations that provide higher satisfaction based on the user's past seating history.

[0115] The shift department can also analyze work patterns for specific days of the week and time periods based on staff members' past work history and create optimal shifts. For example, the shift department can identify days of the week and time periods on which staff members have frequently worked in the past and create shifts for those time periods as a priority. The shift department can also take into account the congestion situation on specific days of the week and time periods and create shifts to avoid congestion. Furthermore, the shift department can analyze staff members' past work performance and create shifts for time periods when performance is high. This makes it possible to create more efficient and satisfying shifts based on staff members' past work history.

[0116] The analysis unit can also analyze the best-selling items of specific menu items or products based on the user's past purchase history and propose an optimal menu configuration. For example, the analysis unit can identify menu items or products that the user has frequently purchased in the past and prioritize those menu items or products. The analysis unit can also analyze the best-selling items of specific menu items or products and propose highly popular menu items or products. Furthermore, the analysis unit can take into account the inventory status of specific menu items or products and propose efficient inventory management. This enables a more efficient and satisfying menu configuration based on the user's past purchase history.

[0117] The prediction unit can also analyze the store visit tendency on specific days of the week or time periods based on the user's past store visit history and predict future store visits. For example, the prediction unit can identify days of the week or time periods on which the user frequently visited the store in the past and predict future store visits based on those time periods. The prediction unit can also take into account the store's crowdedness on specific days of the week or time periods and predict store visits to avoid crowding. Furthermore, the prediction unit can analyze the user's past store visit cancellation history and predict store visits to avoid time periods with a high risk of cancellations. This enables more accurate and efficient store visit predictions based on the user's past store visit history.

[0118] The management unit can also estimate the user's emotions and adjust reservation priorities based on the estimated user emotions. For example, if the user is feeling stressed, the management unit can prioritize reserving seats that allow relaxation. Also, if the user is in a hurry, the management unit can prioritize reserving seats that allow for quick response. Furthermore, if the user is planning a special event, the management unit can prioritize reserving special seats. This allows for more appropriate reservation management by adjusting reservation priorities based on the user's emotions.

[0119] The suggestion unit can also estimate the user's emotions and adjust the seating allocation suggestion method based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can suggest a spacious seat. Also, if the user is in a hurry, the suggestion unit can suggest a seat that can be quickly accommodated. Furthermore, if the user is planning a special event, the suggestion unit can suggest a special seat. In this way, by adjusting the seating allocation suggestion method based on the user's emotions, more appropriate seating allocation suggestions can be made.

[0120] The shift unit can also estimate the user's emotions and adjust the shift creation method based on the estimated user's emotions. For example, if the user is feeling stressed, the shift unit can create a relaxing shift. If the user is in a hurry, the shift unit can also create a shift that allows for quick response. Furthermore, if the user is planning a special event, the shift unit can also create a special shift. In this way, by adjusting the shift creation method based on the user's emotions, more appropriate shift creation becomes possible.

[0121] The analysis unit can also estimate the user's emotions and adjust the data analysis method based on the estimated user's emotions. For example, the analysis unit can perform a detailed data analysis when the user is relaxed. Alternatively, the analysis unit can perform a brief data analysis when the user is in a hurry. Furthermore, the analysis unit can perform a special data analysis when the user is planning a special event. This allows for more appropriate data analysis by adjusting the data analysis method based on the user's emotions.

[0122] The prediction unit can also estimate the user's emotions and adjust the prediction method based on the estimated user's emotions. For example, the prediction unit can make a detailed prediction when the user is relaxed. Alternatively, the prediction unit can make a brief prediction when the user is in a hurry. Furthermore, the prediction unit can make a special prediction when the user is planning a special event. In this way, adjusting the prediction method based on the user's emotions enables more accurate prediction.

[0123] The processing flow of the second embodiment will be briefly explained below.

[0124] Step 1: The management unit manages the reservation status. For example, the management unit collects reservation information and uses an algorithm to prevent duplication and waste. Step 2: The proposal unit proposes an optimal seating arrangement based on the reservation status managed by the management unit. The proposal unit proposes an optimal seating arrangement based on, for example, information provided by the management unit. Step 3: The shift department automates and optimizes staff shifts. The shift department creates optimal shifts, taking into account, for example, staff working hours and break times. Step 4: The analysis unit analyzes the point-of-sale data. For example, the analysis unit identifies best-selling menu items and inventory trends. Step 5: The prediction unit predicts performance data based on the data analyzed by the analysis unit. For example, the prediction unit predicts future sales and the number of visitors based on past data. Step 6: The marketing department identifies customer trends and makes marketing proposals. For example, the marketing department analyzes customer preferences and behavioral patterns to provide individually tailored promotions.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0126] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0131] The data processing device 12 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0142] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0143] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0159] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0162] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] The data processing device 12 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0175] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0176] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0177] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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).

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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, in order to avoid confusion and to 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.

[0195] 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.

[0196] [Explanation of symbols]

[0197] 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 management department that manages reservation status; a proposal unit that proposes seat allocation based on the reservation status managed by the management unit; A shift department that automates staff shifts, an analytics department that analyzes point-of-sale data; a prediction unit that predicts performance data based on the data analyzed by the analysis unit; A marketing department that understands customer trends and makes marketing proposals. A system characterized by:

2. The management unit Collect booking information and use algorithms to prevent duplication and waste 2. The system of claim 1.

3. The proposal unit Proposes optimal seating arrangements based on the information provided by the management department 2. The system of claim 1.

4. The shift unit is Create shifts based on staff working hours and break times 2. The system of claim 1.

5. The analysis unit Analyze point-of-sale data to understand best-selling menu items and inventory trends 2. The system of claim 1.

6. The prediction unit Predict future sales and customer numbers based on past data 2. The system of claim 1.

7. The marketing department Analyze customer preferences and behavioral patterns to provide personalized promotions 2. The system of claim 1.

8. The management unit Using an algorithm to estimate user emotions, the system adjusts reservation priorities based on the estimated user emotions.

2. The system of claim 1.

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